@misc{chi2024universal,
title = {{Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots}},
author = {Cheng Chi and Zhenjia Xu and Chuer Pan and Eric Cousineau and Benjamin Burchfiel and Siyuan Feng and Russ Tedrake and Shuran Song},
year = {2024},
eprint = {2402.10329},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2402.10329},
}
HIL-SERL — real-world RL with human corrections, reported to reach near-perfect success on precise assembly and dynamic tasks after a few hours of training.HIL-SERL：结合人类纠正的真机强化学习，报告称在精密装配与动态任务上经数小时训练即可接近满分成功率。
@misc{luo2024precise,
title = {{Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning}},
author = {Jianlan Luo and Charles Xu and Jeffrey Wu and Sergey Levine},
year = {2024},
eprint = {2410.21845},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.21845},
}
PyTorch library with datasets, pretrained policies (ACT, Diffusion, SmolVLA) and drivers for low-cost arms.PyTorch 机器人学习库，提供数据集、预训练策略（ACT、Diffusion、SmolVLA）与低成本机械臂驱动。
Generating action sequences by denoising handles multimodal demos far better than regression; now a standard IL baseline.以去噪方式生成动作序列，比回归更能处理多模态演示，已成为模仿学习标准基线。
@misc{chi2023diffusion,
title = {{Diffusion Policy: Visuomotor Policy Learning via Action Diffusion}},
author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song},
year = {2023},
eprint = {2303.04137},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2303.04137},
}
@misc{zhao2023learning,
title = {{Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware}},
author = {Tony Z. Zhao and Vikash Kumar and Sergey Levine and Chelsea Finn},
year = {2023},
eprint = {2304.13705},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2304.13705},
}
GPU-parallel motion generation for arms — many trajectory optimizations run at once, giving collision-free, smooth plans in milliseconds.面向机械臂的 GPU 并行运动生成：同时运行大量轨迹优化，在毫秒级给出无碰撞且平滑的轨迹。
@misc{sundaralingam2023curobo,
title = {{cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation}},
author = {Balakumar Sundaralingam and Siva Kumar Sastry Hari and Adam Fishman and Caelan Garrett and Karl Van Wyk and Valts Blukis and Alexander Millane and Helen Oleynikova and Ankur Handa and Fabio Ramos and Nathan Ratliff and Dieter Fox},
year = {2023},
eprint = {2310.17274},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2310.17274},
}
Drives robots with a frozen frontier LLM plus in-context 'knowledge packages' on 12 sim benchmarks + 8 real robots; reports beating prior bests on 9 of 12, but weak real-robot results (competence, not yet intelligence).用冻结的前沿大模型加上下文“知识包”直接控制机器人，覆盖 12 个仿真基准和 8 台真机；报告在 12 个基准中的 9 个超过此前最好结果，但真机表现较弱——结论是具备了具身“能力”，尚未达到具身“智能”。
@misc{sensenova2026has,
title = {{Has GPT-6-Astra Achieved Embodied Intelligence?}},
author = {SenseNova and Joint Research Team and Zhongang Cai and Ziang Cao and Tailai Chen and Han Chong Chua and Linjun Dai and Hanming Deng and Hokin Deng and Haiwen Diao and Chenjing Ding and Weichen Fan and Xiao Fu and Zerui Gong and Chenyang Gu and Zexin He and Zhi Hou and Bo Li and Qinyue Li and Quanzhou Li and Xiaowan Li and Zhichao Liao and Dahua Lin and Zilong Lin and Bo Liu and Chunxiao Liu and Pengfei Liu and Tianqi Liu and Ziwei Liu and Chen Change Loy and Lewei Lu and Jiapeng Luo and Yuexiao Ma and Liang Pan and Xingang Pan and Oscar Qian and Ziyi Shang and Yuyao Shao and Dawei Su and Dacheng Tao and Qingyi Tao and Quan Wang and Ruisi Wang and Xiaogang Wang and Ziyi Wang and Beichen Wen and Donghao Wu and Jiaxu Wu and Silei Wu and Xiaoming Wu and others},
year = {2026},
eprint = {2610.22471},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.22471},
}
Wraps a frozen VLA in two loops — one keeps user preferences and scene memory for planning, one blocks hazardous actions and calls recovery skills; reports violations down from 52.6% to 6.1%, recovery up from 13% to 80%.给冻结的 VLA 套上双环框架：规划环保存用户偏好与场景记忆以做个性化任务分解，执行环拦截危险接触并调用可复用的恢复技能；报告安全违规率从 52.6% 降到 6.1%，失败恢复成功率从 13% 提升到 80%。
@misc{wang2026carryon,
title = {{CarryOn: A Dual-Loop Harness for Personalized and Reliable Robot Assistance}},
author = {Zixuan Wang and Yuqi Liu and Yuxin Chen and Sengqiao Yang and Jinhui Ye and Pengguang Chen and Changsheng Lu and Shu Liu and Bei Yu and Jiaya Jia},
year = {2026},
eprint = {2610.21453},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.21453},
}
Six frontier general-purpose models drive a LIBERO-10 arm through tool calls (end-effector poses, gripper), no robot training; best reported success is 44%. More reasoning effort usually helps but costs more.让六个前沿通用大模型通过工具调用（指定末端位姿与夹爪开合）直接控制 LIBERO-10 机械臂，无任何机器人专门训练；报告的最高成功率为 44%。提高推理强度通常有帮助，但推理成本更高。
Harness where a frontier LLM reviews a frozen π0.5 policy less often as experience builds up and focuses on recovery after errors; reports ~57% fewer reviewer tokens and higher success on four sim tasks.让前沿大模型监督冻结的 π0.5 策略的执行框架：随着经验积累逐步减少审查频率，并把监督集中在出错后的恢复上；报告在四个仿真任务上审查 token 平均减少约 57%，成功率也更高。
@misc{lin2026fastharness,
title = {{FastHarness: Experience-Driven Review Scheduling for Vision-Language-Action Policies}},
author = {Tao Lin and Yuxin Du and Haoxiao Wang and Zile Zhong and Bo Zhao},
year = {2026},
eprint = {2610.31427},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31427},
}
Between direct action prediction and full video prediction: VLDA learns a compact latent 'Delta' of task-relevant state change and uses it to seed action denoising. Built on Baidu's open LoongForge.介于直接预测动作与完整预测未来画面之间：VLDA 学习一个紧凑的隐变量 Delta 表示与任务相关的状态变化，并用它引导动作去噪；实现于百度开源训练框架 LoongForge。
@misc{zhang2026vision,
title = {{Vision-Language-Delta-Action: Learning Compact Deltas as Actionable Transition Interfaces for Robotic Manipulation}},
author = {Lingsen Zhang and Wei Li and Jie He and Henghua Zhang and Kai Mo and Shiyong Li and Yanpeng Wang and Rui Shao},
year = {2026},
eprint = {2610.25874},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25874},
}
Deployment knob for flow-matching VLAs: fewer solver steps hurt under fast replanning but help with long open-loop chunks. Tuning both together, training-free, reports higher success and lower latency for π0/π0.5/GR00T.流匹配 VLA 的部署调参发现：求解步数少在频繁重规划时会降低精度，但在长时开环执行动作块时反而有利。报告在 π0、π0.5、GR00T 上联合调整两者，无需训练即可提高成功率并降低推理延迟。
@misc{yang2026coupling,
title = {{Coupling Execution Horizon and Sampling Depth in Flow-Matching VLA Policies}},
author = {Jiabing Yang and Yuan Xu and Yixiang Chen and Qisen Ma and Peiyan Li and Kai Wang and Chenhang Cui and Tao Yu and Zichen Wen and Wen Ye and Bowen Fang and Ziheng He and Xiangnan Wu and Yingda Li and Yan Huang and Liang Wang},
year = {2026},
eprint = {2610.32102},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.32102},
}
Report: GPT-6-Astra drives a robot on EBench via waypoints or by calling a VLA/WAM policy. Direct control leads on short tasks, trails on long-horizon and contact-rich ones; calling a policy cuts token cost.评测报告：在 EBench 上让 GPT-6-Astra 直接输出路径点控制机器人，或通过执行框架调用 VLA/WAM 策略。直接控制在短时任务上领先，但在长时程与接触密集任务上落后；调用策略可显著减少 token 开销。
@misc{yao2026profiling,
title = {{Profiling a Frontier Agent with EBench: From Direct Control to Harness with Policy}},
author = {Yifei Yao and Yude Zou and Guanbo Jiao and Xing Gao and Ning Gao and Yukai Wang and Jiantong Chen and Zihou Zhu and Xinyu Li and Ningyu Yan and Shuai Wang and Jianming Ma and Haoyu Guo and Weinan Zhang and Chunhua Shen},
year = {2026},
eprint = {2610.35832},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35832},
}
Mixed-precision quantization for VLAs that sets each weight matrix's bit width by how much it affects actions; reports 92.7% LIBERO success at 3 bits (above AWQ and GPTQ) and 70.5% at 2.5 bits.面向 VLA 的混合精度量化：根据每个权重矩阵对输出动作的影响分配比特数；报告在 3 比特下 LIBERO 成功率 92.7%（高于 AWQ 与 GPTQ），2.5 比特下仍有 70.5%。
@misc{qiu2026agq,
title = {{AGQ: Action-Guided Ultra-Low-Bit Quantization for Vision-Language-Action Models}},
author = {Xuhong Qiu and Zhuoyu Wu and Qiawei Zheng and Wenhui Ou and Wenqi Fang and Raphaël C. -W. Phan},
year = {2026},
eprint = {2610.33074},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2610.33074},
}
Pretrains a 4B VLA on 3D-grounded latent actions mined from 30K hours of human, robot and sim video, then adapts to real robot actions. Reports 98.8% on LIBERO and 88.6% on RoboTwin 2.0.从 3 万小时人类、机器人与仿真视频中学习带 3D 约束的连续潜在动作（RynnLAM），用于预训练 4B 参数 VLA，再适配到具体机器人动作空间。报告 LIBERO 98.8%、RoboTwin 2.0 平均 88.6%。
@misc{zhao2026rynnvla,
title = {{RynnVLA-Latent: Scaling Robotic Manipulation Foundation Models with 3D-Grounded Latent Actions}},
author = {Haoyu Zhao and Xingyue Zhao and Zhian Su and Bohan Hou and Yinzhou Tang and Fengyi Wang and Kehan Li and Yuming Jiang and Siteng Huang and Xin Li and Zhenyu Zeng and Mingxiu Chen and Deli Zhao and Zhongyu Li},
year = {2026},
eprint = {2610.34356},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34356},
}
Teaches a VLA to know when it is done or failing: stop and failure signals are two extra action dimensions, denoised faster than the actions so they can guide them; no frame-level progress labels. Real-robot tests.让 VLA 自己判断任务完成或失败：把停止与失败信号作为动作块的两个额外维度，与动作一起去噪且去噪更快，从而引导后续动作生成；无需逐帧进度标注，在真机操作实验中验证。
@misc{liu2026echo,
title = {{Echo: Coupled Action and Task Status Signal Denoising for Vision Language Action Models}},
author = {Yufeng Liu and Bohao Fan and Juntu Zhao and Xinyi Wang and Hang Yu and Wenzhuan Luo and Yingzhi Wang and Qinghai Li and Yingdong Hu and Junliang Guo and Richard Wang and Dequan Wang and Yang Gao and Cheng Ma},
year = {2026},
eprint = {2610.31569},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31569},
}
Lets a VLA pick, at each step, between fine and temporally coarser action decoders: redundant motion runs faster while precision phases stay fine-grained. Tested on 3 sim benchmarks and 6 real tasks.让 VLA 在每一步自行选择细粒度或时间上更粗的动作解码器：冗余动作阶段加速执行，精细接触阶段保持细粒度控制；在 3 个仿真基准和 6 个真实任务上验证效率提升且成功率不降。
@misc{jeon2026pacevla,
title = {{PaceVLA: Adaptive Temporal Action Quantization in Vision-Language-Action Models}},
author = {Hojin Jeon and Jimin Lee and Taekwan Kim and Nayoung Oh and Taeyoung Kim and Myungkyu Koo and Jinmo Koo and Joonwoo Ahn and Jinwoo Shin},
year = {2026},
eprint = {2610.25961},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25961},
}
Test-time add-on for frozen VLAs: a GNN retrieves future motion primitives from a graph of training demos and gates them into the actions. Reports up to +12% in sim and +40% real (GR00T N1.7, π0.5, StableVLA).面向冻结 VLA 的测试时插件：把训练示范组织成运动基元图，用 GNN 定位当前执行并检索后续基元，再按维度门控融合到预测动作中；报告在 GR00T N1.7、π0.5 等上仿真最高 +12%、真机 +40%。
@misc{quang2026retrieve,
title = {{Retrieve to Act: Motion Primitive Graph Retrieval for Robust VLA Execution}},
author = {Pham Tri Quang and Bao-Ngoc Dao and Minh Duc Nguyen and Duc-Duy Nguyen and Hung T. Ho and Linh Dang Le and Trong-Bao Ho and Thien-Loc Ha and Tan Q. Nguyen and Tuan Dam and Vu N. Duong and Daniel Sonntag and Ngan Le and Khoa D. Doan and Jan Peters and An Thai Le and Duy M. H. Nguyen and Vien Anh Ngo},
year = {2026},
eprint = {2610.37215},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37215},
}
Finds VLAs over-rely on the wrist camera; predicts future interaction intent from past actions to route between global and wrist views per stage. Reports +10.1 pp over π0.5 on RoboTwin 2.0 and 71.7% → 85.0% real.发现现有 VLA 过度依赖腕部相机；根据历史动作预测未来交互意图，按任务阶段在全局与腕部视角间动态路由；报告在 RoboTwin 2.0 上比 π0.5 高 10.1 个百分点，真机成功率由 71.7% 提升到 85.0%。
A frozen frontier VLM, prompted with the whole episode, outputs waypoints directly and reports 92.1% on RoboMME memory tasks with no task training.冻结的前沿 VLM 以整段回合为上下文直接输出路径点，在 RoboMME 记忆类任务上报告 92.1% 成功率，且无需任务训练。
@misc{jedlicki2026can,
title = {{Can a Frontier VLM Serve as a Robot Policy for Memory-Dependent Manipulation?}},
author = {Jack B. Jedlicki and Amish Sethi and Heng Yang},
year = {2026},
eprint = {2610.17350},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.17350},
}
Asks whether joint readings alone suffice for millimetre-precise insertion — trains sim-to-real policies with no camera, force or tactile input.探究仅靠关节读数能否完成毫米级精密插装：训练不用相机、力或触觉输入的 sim-to-real 策略。
WACL learns from human demos to push and hold with the whole arm, choosing where on the arm to make contact while keeping the gripper's constraints.WACL 从人类示范中学习用整条手臂推、抵物体，自主选择手臂上的接触位置，同时满足末端执行器的约束。
@misc{huang2026beyond,
title = {{Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations}},
author = {Bo-Ruei Huang and Daniel Seita and Erdem Bıyık},
year = {2026},
eprint = {2610.17802},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.17802},
}
A coding agent drives a bimanual robot through one command line; after each failure it reads the logs and writes or fixes small reusable tool programs.编程智能体通过一条命令行驱动双臂机器人；每次失败后读取日志，编写或修复可复用的小工具程序。
@misc{gao2026hexashell,
title = {{HexaShell: A Thin Harness for Exploring the Limits of VLM Agents as Robot Coding Agents}},
author = {Hongcheng Gao and Alex Zhou and Jay Zhu and Zelin Zheng and Shijia Ge and Xiping Li and Di Wu and Yazhe Wang and Jianshu Zeng and Xuan Shangguan and Yongli Wang and Yexing Wan and Lingyu He and Zhiqi Jia and Sihang Wu and Xiao He},
year = {2026},
eprint = {2610.23479},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.23479},
}
Grasps several objects at once with a dexterous hand by learning one-step residual corrections to a reference trajectory, with a strategy that adapts to how the objects are laid out.让灵巧手一次抓取多个物体：学习对参考轨迹的单步残差修正，并根据物体的空间布局调整抓取策略。
CTR retimes the two arms in bimanual demos at training time while keeping task-required constraints, so the policy stops using the other arm's progress as a shortcut cue.CTR 在训练时对双臂示教中两只手臂的动作时序重新组合，同时保留任务必需的约束，避免策略把另一只手臂的进度当作捷径线索。
@misc{hu2026learn,
title = {{Learn the Task, Not the Schedule: Reducing Spurious Cross-Arm Coupling in Bimanual Imitation Learning}},
author = {Shuyuan Hu and Deng-Ke Wu and Yifan Jin and Qianhang Luo and Mingchen Ju and Kai Ye and Bo Zhao},
year = {2026},
eprint = {2610.26507},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.26507},
}
One two-hand policy (PPO + BC on its own verified successes) tracks retargeted human hand-object motions; ships BiHOI (1,575 sequences, 229 objects). Reports 86.8% on unseen trajectories, 57.3% on new objects.用一个双手跟踪策略（PPO 结合对自身成功执行的行为克隆）复现重定向后的人手-物体交互动作，并发布 BiHOI 数据集（1575 段双手序列、229 个物体）。论文报告在未见轨迹上成功率 86.8%、新物体上 57.3%。
@misc{sun2026donic,
title = {{DONIC: Generalist Dexterous Bimanual Tracking Control from Human Demonstrations}},
author = {Mingze Sun and Ziang Cao and Yuan Fei and Qiankuan Tao and Zihan Wang and Tianqi Liu and Junhao Chen and Haiwen Diao and Fangzhou Hong and Zhaoxi Chen and Ziwei Liu},
year = {2026},
eprint = {2610.29997},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29997},
}
SERA turns the high-level VLM into an agent with an editable harness that tracks task progress for a fixed VLA executor, and revises the harness from rollouts; reports 51.4% vs 44.4% overall success on RoboMME.SERA 把高层 VLM 规划器做成带可编辑执行框架的智能体，为固定的底层 VLA 记录任务进度与执行反馈，并利用环境回放自动改进框架；报告在 RoboMME 上总体成功率 51.4%，未自我演化时为 44.4%。
@misc{du2026from,
title = {{From VLM Planners to Robotic Agents: Self-Evolving Harnesses for History-Dependent Manipulation}},
author = {Tingwen Du and Haoyu Guo and Hanxue Zhang and Xuanxu Wang and Junting Dong and Chao Gao and Mingfei Han and Tao Lu and Mulin Yu and Chunhua Shen and Xiaojun Chang and Zhihui Li},
year = {2026},
eprint = {2610.29560},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29560},
}
A coding agent that reads wrist force/torque as well as images, writes probing and force-feedback programs, and revises them from force logs; no demos or training. Beats its vision-only variant on 5 real tasks.同时读取腕部力/力矩与图像的编程智能体：编写接触探测与力反馈控制程序，并在每次执行后依据力记录诊断失败、修改代码；无需示教或训练。在 5 个真实任务上优于仅视觉版本。
@misc{tan2026forca,
title = {{FORCA: A Force-Aware Coding Agent for Contact-Rich Robot Manipulation}},
author = {Xiao Tan and Yixuan Wang and Zhexi Wang and Wenzhen Yuan},
year = {2026},
eprint = {2610.31841},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31841},
}
36 contact-rich tasks built around eight everyday skills, with 3D-printable objects that each have a matching sim model. Reports that tested visuo-tactile policies did not clearly beat vision-only ones.围绕八种日常接触技能设计的 36 个接触密集型任务，40 多个物体可用消费级 3D 打印机复现，且每个都有对应仿真模型。报告称所测的视触觉策略并未明显优于纯视觉策略。
Mobile dexterous grasping learned in sim: a whole-body controller does reaching and active vision, local RL experts the object, all distilled into one point-cloud policy; reports zero-shot real transfer.在仿真中学习移动灵巧抓取：全身控制器负责底盘-手臂协同到达与主动视觉，局部强化学习专家负责物体交互，二者蒸馏成一个基于点云的视觉策略；报告可零样本迁移到真实世界的数百个物体与场景。
@misc{yang2026cowboy,
title = {{COWBOY: A Scalable Sim-to-Real Framework for Learning Contextual Whole-Body Manipulation}},
author = {Jiahui Yang and Jason Jingzhou Liu and Ray Muxin Liu and Andrew E. Wang and Kenneth Shaw and Deepak Pathak and Ruslan Salakhutdinov},
year = {2026},
eprint = {2610.28948},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.28948},
}
ARPG pretrains an action encoder on human manipulation data, then trains a 130M generative policy in its space. Reports bimanual dexterous success up from 10.1% (π0.5) to 44.7% on DexJoCo.ARPG 把动作表征学习与动作生成解耦：先在大规模人类操作数据上预训练动作编码器，再在其表征空间训练 1.3 亿参数的生成策略。报告在 DexJoCo 双手灵巧任务上平均成功率从 π0.5 的 10.1% 提升到 44.7%。
@misc{xu2026scaling,
title = {{Scaling Dexterous Manipulation via Action Representation Pretraining}},
author = {Yanjinghao Xu and Jiexin Zhang and Huan Zhao and Jianjia Mo and Zhenghao Yin and Dingkang Liang and Xiang Bai and Han Ding},
year = {2026},
eprint = {2610.34453},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34453},
}
Language-to-dexterous-manipulation as modulated tracking: a distilled generalist tracker plus an RL-trained modulator that keeps adjusting the reference, object latent and position target; zero-shot sim-to-real.把语言条件灵巧操作变成“可调制的跟踪”：蒸馏得到的通用跟踪器加 RL 训练的调制器，在执行中持续修正参考轨迹、物体隐表征与位置目标，抑制误差累积；零样本迁移到真机。
@misc{adalibieke2026adadextrack,
title = {{AdaDexTrack: Dynamic Modulation for Adaptive and Generalizable Dexterous Manipulation Tracking}},
author = {Jianibieke Adalibieke and Qianwei Han and Xueyi Liu and Yuzhe Qin and Li Yi},
year = {2026},
eprint = {2610.33852},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.33852},
}
Foresight without predicting future frames: a training-only head forecasts the next action chunk and backpropagates through the policy's latent. No inference cost; reports up to +14 pts on mm-precision real tasks.不预测未来图像也能获得“预见性”：仅在训练时用级联头预测下一个动作块，并把损失回传到在线策略的隐状态，推理零额外开销；报告在耳机装配、穿鞋带等毫米级真机任务上最高提升 14 个百分点。
@misc{yang2026adapolicy,
title = {{AdaPolicy: Action-Driven Anticipatory Robot Policies via Next-Chunk Prediction}},
author = {Fan Yang and Yuting Su and Chen Zhao and Fugui Fan and Peiguang Jing},
year = {2026},
eprint = {2610.34280},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34280},
}
DISCO runs progressive-widening MCTS in GPU-parallel simulation, with a learned policy proposing continuous actions, then distills plans into a policy-value model used without search. Reports wins over BC, RL and search.DISCO 在 GPU 并行物理仿真中用渐进展宽 MCTS 搜索长程重排计划，由学习的策略提出连续动作、价值函数打分，并周期性把搜索结果蒸馏回策略-价值模型；部署时无需仿真搜索。报告在仿真与真机重排任务上优于行为克隆、强化学习与搜索基线（CoRL 2026 Oral）。
@misc{saha2026long,
title = {{Long-Horizon Multi-Object Rearrangement via Distilled Tree Search over Continuous Actions}},
author = {Kallol Saha and Yining Hong and Amber Li and Yejin Choi and Maxim Likhachev and David Held},
year = {2026},
eprint = {2610.25785},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25785},
}
@misc{strong2026real,
title = {{Real2Sem2Real: 3D Semantic Augmentations for Robot Learning from a Single Human RGB Demonstration}},
author = {Matthew Strong and Ankush K. Dhawan and Julien Poffet and Giuse Pham and Jinho So and Monroe Kennedy},
year = {2026},
eprint = {2610.28880},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.28880},
}
Sim-only RL recipe for in-hand object pose reaching on a LEAP hand, zero-shot to hardware; reports 95.2% of 4,800 unseen goals reached and a tunable residual safety filter that cuts drops by 75%.在仿真中无需人类数据训练 LEAP 灵巧手的手内物体位姿到达策略，零样本迁移到真机；报告在 4800 个未见目标上 95.2% 到达，可调节的残差安全滤波器使掉落减少 75%。
@misc{chuang2026pushing,
title = {{Pushing Dexterity and Safety for In-Hand Object Pose Reaching That Works Remarkably Well}},
author = {Ian Chuang and Hiya Shah and Vamsi Chilamkur and Iman Soltani and Claire Tomlin},
year = {2026},
eprint = {2610.37652},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37652},
}
Retargets human hand demos (video, mocap, tactile gloves) by keeping contact-force targets, not just kinematics; a tactile tracking controller replays them. Reports contact kept on 43–78% of frames vs <10%.把人手示教（视频、动捕、触觉手套）重定向到灵巧手时显式保留接触力目标而不只是运动学，并用触觉跟踪控制器回放；报告 43–78% 的接触帧保持接触，纯运动学方法不到 10%。
@misc{dai2026card,
title = {{CARD: Contact-Anchored Retargeting for Dexterous Manipulation}},
author = {Cunxi Dai and Botao He and Francois R Hogan and Wei Pan and Rao Fu and Wen-Han Hsieh and Ruoshi Liu and Jitendra Malik and Guanya Shi and Akash Sharma},
year = {2026},
eprint = {2610.36539},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36539},
}
Learns history-aware progress rewards for long-horizon manipulation so missing prerequisites are not rewarded, then refines policies from BC to online RL. Reports 31.8% on 18 held-out RLBench tasks (+9.2 pts).为长时程操作学习依赖执行历史的进度奖励，避免跳过前置步骤也得高分，再从行为克隆过渡到在线强化学习微调策略；报告在 18 个未参与预训练的 RLBench 任务上平均 31.8%（高出最强基线 9.2 个百分点），真机 57.5%。
@misc{chen2026robolongr,
title = {{RoboLongR: Causally Consistent Rewards Teach Robots to Progressively Master Long-Horizon Manipulation}},
author = {Zixuan Chen and Lin Cao and Fenggang Zhang and Haotong Zhang and Yangtao Chen and Jieqi Shi and Jing Huo and Yang Gao},
year = {2026},
eprint = {2610.33863},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.33863},
}
Builds pseudo-bimanual demos from single-arm data by sagittal-plane mirroring plus LLM-written coordination constraints. On AgileX ALOHA it reports 2.11× cups collected vs. a single-arm baseline.将单臂示范沿矢状面镜像（观测、本体感觉与动作），再由 LLM 生成时空协调约束，构造伪双臂示范；在 AgileX ALOHA（RoboTwin 仿真与真机）上报告收集杯子数达到单臂基线的 2.11 倍。
@misc{wu2026robostd,
title = {{RoboSTD: Zero-Shot Single-to-Dual Transfer via Sagittal Mirroring for Bimanual Learning}},
author = {Hongyu Wu and Chong Liu and Shaoxuan Xie and Shaqi Luo and Hanyu Feng and Shihan Wu and Guocai Yao},
year = {2026},
eprint = {2610.36685},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36685},
}
@misc{huang2026cuarms,
title = {{cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation}},
author = {Philip Huang and Fabio Ramos and Jiaoyang Li and Caelan Garrett and Lars Johannsmeier},
year = {2026},
eprint = {2610.36250},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36250},
}
Review (Annual Review of Control, Robotics, and Autonomous Systems) tracing tactile manipulation from contact-mechanics models to learned policies: sensing basics, state estimation, IL, RL, VLAs, predictive models.综述（Annual Review of Control, Robotics, and Autonomous Systems 录用稿）：从接触力学建模到数据驱动学习，梳理触觉操作的发展，涵盖传感基础、状态表征、模仿学习、强化学习、VLA 与预测模型。
HumanVerse, 500 h of egocentric human video with body and hand motion, aligned to the Unitree G1 for mid-training a humanoid model; reports 90% on SIMPLE and 62.5% on real loco-manipulation tasks.构建 500 小时的第一视角人类数据集 HumanVerse（含同步的身体与手部动作），对齐到 Unitree G1 后用于人形基础模型 λ0 的中期训练；论文报告 SIMPLE 上成功率 90%、真实移动操作任务 62.5%，人类数据越多下游表现越好。
@misc{team2026scaling,
title = {{$λ_0$: Scaling Open-World Egocentric Human Data for Humanoid Loco-Manipulation}},
author = {Lambda-humanoid Team and : and Chongyang Xu and Jin Chen and Jinhui Ye and Yilun Chen and Yiming Jiang and Zhao Wu and Hu Wei and Lin Qu and Shifeng Zhang and Yisheng He and Yuming Jiang and Zenan Meng and Zheng Wu and Mu Xu and Steven Hoi},
year = {2026},
eprint = {2610.18950},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.18950},
}
A quadruped solves a giant Rubik's Cube with its feet: onboard RGB-D estimates cube pose and colours, a planner sequences skills, and RL whole-body skills turn layers. Reports three full autonomous solves.四足机器人只用脚完成巨型魔方：机载 RGB-D 估计魔方位姿与颜色状态，规划器把解法转成技能序列，强化学习训练的全身技能负责翻转魔方、一脚固定一脚推动转层。报告完成三次完全自主求解。
@misc{amiet2026solving,
title = {{Solving a Giant Rubik's Cube with a Quadruped Robot}},
author = {Lukas Amiet and Gabriel Sánchez-Blanco Repgen and Mayank Mittal and Victor Klemm and Andrei Cramariuc and Marco Hutter and Cesar Cadena},
year = {2026},
eprint = {2610.24610},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.24610},
}
FLoRe: robot-free demos from force-sensing UMI-style grippers + body trackers train a diffusion policy that outputs per-hand force commands for a compliant whole-body controller; reports 80% vs. 26.7% for position-only.FLoRe：用带力传感的 UMI 式手持夹爪与人体追踪器采集无机器人示教，训练扩散策略输出每只手的力指令，由柔顺全身控制器执行；报告在三个接触密集任务上成功率 80%，仅位置控制为 26.7%。
@misc{li2026learning,
title = {{Learning Force-Reactive Humanoid Loco-Manipulation}},
author = {Sihang Li and Xiaofeng Guo and Jinkun Cao and Charlie Cheng and Ruihan Yang and Chen Feng and Guanya Shi and Haozhi Qi},
year = {2026},
eprint = {2610.35862},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35862},
}
Real-to-sim-to-real: smartphone scans become physics-ready sim scenes, whole-body motions are corrected by rollouts, giving 4,800 demos; a sim-only GR00T N1.7 on Unitree G1 reports 66.7% vs. 46.7% trained on real data.真实到仿真再到真实：用手机拍摄重建可物理仿真的场景，并根据仿真回放修正全身动作，生成 4800 条示教；仅用仿真数据训练的 GR00T N1.7 在宇树 G1 上报告平均成功率 66.7%，真实数据训练为 46.7%。
@misc{yang2026zero,
title = {{Zero-HLM: Generalizable Humanoid Loco-Manipulation without Real Robot Data}},
author = {Dianyi Yang and Mengya Liu and Zhumei Wang and Yu Liu and Xiaochen Zhang and Yixuan Li and Le Ma and Yuhang Yang and Weiqi Huang and Wei Liang and Baoxiong Jia and Siyuan Huang},
year = {2026},
eprint = {2610.35075},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35075},
}
Whole-body humanoid teleoperation from a compact target (root height and velocity + 4 keypoint poses, current frame only): ~20 ms headset-to-policy latency; a Unitree G1 catches a tossed ball.只用紧凑参考目标（根高度、根速度与 4 个稀疏关键点的当前帧位姿）实现人形机器人全身遥操作：头显到策略输出延迟约 20 ms，Unitree G1 能接住抛来的球并完成移动操作。
@misc{cao2026humit,
title = {{HuMiT: Low-Latency Whole-Body Humanoid Teleoperation via Compact Reference Tracking}},
author = {Liu Cao and Botian Xu and Ruoqu Chen and Jiajun Liu and Mingzhi Pei and Zihao Wang and Zhongyu Li and Mengdi Xu},
year = {2026},
eprint = {2610.29903},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29903},
}
FIBER plans in the latent space of a frozen behavioral foundation model: skills are projected into it, one perceptive flow planner is trained offline. Reports 42.0% mean sim success (latent replay: 11.6%); real G1.FIBER 以冻结的行为基础模型（BFM）潜在空间作为统一规划表示，把各任务专用策略投影进该空间，再离线训练单一感知流匹配规划器；仿真中搬箱、开门、抓取平均成功率 42.0%（潜在回放基线 11.6%），并在真实 Unitree G1 上验证。
@misc{lin2026action,
title = {{Action-Aligned Latent Planning for Versatile Humanoid Loco-Manipulation}},
author = {Yutang Lin and Guanya Shi and C. Karen Liu},
year = {2026},
eprint = {2610.24675},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.24675},
}
World action model that jointly predicts video, motion and contact wrench, outputting time-aligned motion–wrench chunks as control references (hybrid motion-force style) for force-sensitive manipulation.一个同时预测未来视频、运动与接触力旋量的世界动作模型，借鉴力位混合控制思路，输出时间对齐的运动-力旋量片段作为控制参考，面向仅靠视觉无法感知接触与负载的力敏感操作任务。
@misc{ding2026cw,
title = {{CW-WAM: A Structured Contact-Wrench World Action Model for Force-Sensitive Manipulation}},
author = {Houzhu Ding and Xinda Qi and Luqi Wang and Ming Yang},
year = {2026},
eprint = {2610.30117},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.30117},
}
AW-0 jointly generates code, future video and actions in one mixture-of-transformers; code structures planning and becomes reusable skills. Reports gains over VLA and WAM baselines on RoboTwin 2.0 and real two-arm tasks.AW-0 在同一个混合 Transformer 中联合生成代码、未来视频和机器人动作三路输出：代码既组织多步预测与控制，又沉淀为可复用技能库；报告在 RoboTwin 2.0 和真机双臂任务上优于 VLA 与 WAM 基线。
@misc{liu2026agentic,
title = {{Agentic World Action Models}},
author = {Zhuoyang Liu and Dantong Niu and Botong Zhang and Boning Shao and Jing Wang and Ruijie Zheng and Yuvan Sharma and Runyu Lu and Jiaming Liu and Renrui Zhang and Roei Herzig and Yutong Bai and Ken Goldberg and Danfei Xu and Yuke Zhu and Guanzhi Wang and Linxi "Jim" Fan and Trevor Darrell},
year = {2026},
eprint = {2610.30136},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.30136},
}
Dexterous-manipulation world model that first predicts object motion and deformation in metric 3D from a hand trajectory, then renders video. Reports 27–40% lower moved-point error than PointWorld.灵巧操作世界模型：先根据手部轨迹在度量 3D 空间中显式预测物体运动与形变，再据此渲染视频。报告在 HOT3D、OakInk2、PhysTwin 上运动点误差比 PointWorld 低 27–40%，两步因果生成大幅提速。
@misc{lee2026dexplicit,
title = {{DeXplicit: Making Physical State Explicit in Dexterous Manipulation World Models}},
author = {Adam Lee and Mutian Xu and Yukang Cao and Shulin Tian and Ziwei Liu},
year = {2026},
eprint = {2610.30781},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2610.30781},
}
World action model for moving targets: forecasts the object's future masks, steers a video model with a Trajectory ControlNet, and distills the pipeline to one-step video-action inference to cut latency.面向动态目标的世界动作模型：先预测目标物体的未来掩码轨迹，用 Trajectory ControlNet 引导视频模型生成与轨迹一致的未来，再通过解耦蒸馏实现一步视频-动作推理以降低延迟。
@misc{geng2026trajwam,
title = {{TrajWAM: An Efficient Trajectory-Guided World Action Model for Dynamic Robotic Manipulation}},
author = {Haohan Geng and Sha Zhang and Weishuo Sun and Guanting Ye and Wenhao Yu and Yuang Xie and Qicheng Zeng and Peng Ye and Jiajun Deng and Jianmin Ji},
year = {2026},
eprint = {2610.25763},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25763},
}
Compact (<100M) world-action model: predicts frozen-DINOv3 tokens at two future horizons and decodes action chunks, with no robot or video pretraining. Reports beating billion-parameter WAMs on RoboTwin 2.0.参数不到 1 亿的紧凑世界动作模型：在冻结 DINOv3 特征空间中预测两个未来时刻的 token 并直接回归动作块，无需机器人或视频预训练；报告在 RoboTwin 2.0 上超过十亿参数级 WAM。
@misc{liu2026gawm,
title = {{GAWM: A Compact Latent World-Action Model for Multi-Task Robot Manipulation}},
author = {Yang Liu and Xiaoli Liu and Liuzhao Li and Yuduo Song and Muyuan Liu and Xiang Gao},
year = {2026},
eprint = {2610.37091},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37091},
}
Turns a pretrained video diffusion transformer into a policy by encoding each action step as one patch in the video latent space; at test time it outputs an action chunk in one DDIM step (84 ms), no video generation.把预训练视频扩散 Transformer 改造成策略：每个时间步的动作编码为视频潜空间中的一个 patch，联合训练视频、视频+动作与纯动作三种扩散目标；测试时一步 DDIM 生成动作块（84 毫秒），无需生成视频。
@misc{collins2026tapestry,
title = {{TAPESTRY: A Single Backbone for Video Generation and Robot Control}},
author = {Jeremy A. Collins and Seungjae Lee and Rachanon Wachakorn and Krishnan Srinivasan and Animesh Garg and Vitor Campagnolo Guizilini and Paarth Shah},
year = {2026},
eprint = {2610.36823},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36823},
}
Adds future 3D point-motion prediction as an auxiliary task to a world action model (video/motion/action MoT); inference stays one visual pass. Reports gains over Fast-WAM with few demos and under shift.在世界动作模型中加入未来三维点运动预测作为辅助目标（视频/运动/动作三专家 MoT），推理仍只需一次视觉前向；报告在少量示范和分布偏移下明显优于 Fast-WAM（RoboCasa-GR1、LIBERO、LIBERO-Plus）。
@misc{hu2026trace,
title = {{Trace-WAM: Learning to Act via 3D Motion Prediction for World Action Models}},
author = {Xu Hu and Changjie Wu and Linzhe Shi and Hang Zhang and Chen Yang and Lingjun Zhang and Guowen Zhang and Mu Xu and Junran Peng and Qing Li and Zhaoxiang Zhang},
year = {2026},
eprint = {2610.36840},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36840},
}
@misc{kim2026adapting,
title = {{Adapting Video Models for Robot Control via Motion-guided Asymmetric Prediction}},
author = {Taeyoung Kim and Junyoung Sung and Heeseung Kwon and Minseop Kim and Jinwoo Shin},
year = {2026},
eprint = {2610.36624},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2610.36624},
}
Finds a VLA at 80% on atomic skills drops to 19% when composed into mobile manipulation, diagnoses why, and builds an agentic system around it.发现 VLA 在原子技能上 80% 成功率、组合成移动操作后仅 19%，分析失败原因并据此构建智能体式系统。
@misc{wang2026cracking,
title = {{Cracking Mobile Manipulation: From Failure Diagnosis to Agentic Robot System}},
author = {Zixuan Wang and Jia-Ning Li and Yinzhou Tang and Yanzhao Yu and Biao Wu and Meng Fang and Xin Li and Yiwu Zhong},
year = {2026},
eprint = {2610.19600},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.19600},
}
HiRe-MoMa fine-tunes a frozen mobile-manipulation policy in the real world by learning residual corrections, with only occasional human guidance.HiRe-MoMa 在真实环境中通过学习残差修正来微调冻结的移动操作策略，只需偶尔的人类引导。
@misc{gong2026hierarchical,
title = {{Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance}},
author = {Zhefei Gong and Hefeifei Jiang and Zehao Wang and Jiachen Li},
year = {2026},
eprint = {2610.20607},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.20607},
}
Fine-tunes Qwen3.5-9B on ~1.5M grounding samples auto-built from robot recordings plus other visual data; reports a 75.41 average over seven grounding benchmarks vs 73.68 for GPT-6 Astra.用自动标注的机器人操作录像加多来源视觉数据构建约 150 万条可验证的定位监督，微调 Qwen3.5-9B；报告在七个公开视觉定位基准上平均 75.41 分，GPT-6 Astra 为 73.68。
@misc{wang2026ace,
title = {{ACE-Brain-0.6: Scaling Spatial Grounding for Physical Foundation Models}},
author = {Yinhe Wang and Tao Tao and Zexin He and Shuang Luo and Wei Mao and Yao Mu and Feng Pan and Hanming Deng and Lewei Lu and Zhi Hou and Xingang Pan and Dahua Lin and Dacheng Tao and Xiaogang Wang},
year = {2026},
eprint = {2610.27040},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.27040},
}
An open-source ROS 2 multimode controller that switches control modes and triggers safety reflexes in under a millisecond, without stopping the task; shown on a Franka arm.开源的 ROS 2 多模式控制器，可在亚毫秒内切换控制模式并触发安全反射而无需中断任务；在 Franka 机械臂上验证。
Signed friction distance as a task-adaptive slip margin for bimanual tilting about a pivot; a sequence of SOCPs gives optimal contact forces. Real tests show adaptive margins beat fixed or no margins.以带符号摩擦距离度量防滑裕度，并在双臂绕支点倾转物体时按任务需求自适应调整；一系列二阶锥规划给出全局最优接触力。真机实验显示自适应裕度优于固定裕度和无裕度。
@misc{lee2026keeping,
title = {{Keeping Contact: Task-Adaptive Friction Margins for Bimanual Non-Prehensile Manipulation}},
author = {Qian Ying Lee and Lin Yang and Domenico Campolo},
year = {2026},
eprint = {2610.36961},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36961},
}
Deployed robots pool their runs; failures retrieve matching successful segments from the fleet buffer to fine-tune on, with no new demos. Reports 93–99% on five real tasks; 96 robots lift phone packing 79% → 98%.部署中的机器人共享执行经验：用失败片段在机群经验库中检索相似的成功片段来微调策略，无需新增示教或人工纠正。论文报告五个真机任务成功率 93–99%，96 台机器人把手机装盒任务从 79% 提升到 98%。
@misc{yu2026learning,
title = {{Learning from Fleet Experience: Collective Experience Learning Across 96 Robots}},
author = {Chao Yu and Shu'ang Yu and Hao Lin and Hongzhi Zang and Yuxiang Yan and Chenxi Jiang and Zhen Guo and Chuankang Li and Yihao Chen and Xin Xu and Xu Fu and Jiakai Zhou and Liangzhi Shi and Hongyi Ren and Yiwei Liu and Jing Wu and Yuanqing Wang and Chen Wu and Bo Dai and Chunyu Yang and Hongliang Huang and Shuoyu Yue and Weiqi Yin and Yinuo Chen and Ruihua Chen and Chunyang Zhu and Han Xiao and Yanghao Li and Ruipu Wu and Zhikai Cai and Xiu Chen and Xu Tan and Si Liu and Yuchen Yan and Jialin Zhang and Wen Zeng and Yiming Zhou and Zixiao Huang and Xinlei Chen and Guanghui Ren and Quanlu Zhang and Boxun Li and Maoqing Yao and Yu Wang},
year = {2026},
eprint = {2610.25736},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25736},
}
Continual off-policy RL for VLAs — small actor/critic networks carry over between tasks, distilled on a few kept episodes to avoid forgetting; reports 31–68.5% less real-robot time on new assembly tasks.面向 VLA 的持续离策略强化学习：轻量 actor/critic 在任务间延续，用少量保留的旧任务轨迹做蒸馏以防遗忘。论文报告新的接触式装配任务所需真机训练时间减少 31–68.5%，旧任务几乎不遗忘。
@misc{kim2026accrue,
title = {{ACCRUE: Continual Off-Policy Reinforcement Learning for Robot Foundation Models}},
author = {Changyeon Kim and Kimin Lee and Yashraj Narang and Yijie Guo},
year = {2026},
eprint = {2610.18596},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.18596},
}
TIDE updates a pretrained policy online from human interventions and its own rollouts, with no critic or reward model; reports gains on four real bimanual tasks, each session finishing in under an hour.TIDE 在部署过程中利用人类干预和策略自身的执行数据在线更新预训练策略，无需价值函数或奖励模型；报告在四个真实双臂任务上优于初始策略，每次在线后训练不到一小时完成。
@misc{gu2026training,
title = {{Training in Deployment: Human-in-the-loop Online Post-Training for Robot Manipulation}},
author = {Chun Gu and Xiaofei Wei and Yiming Chen and Bozhou Zhang and Juntong Zhang and Xiatian Zhu and Li Zhang},
year = {2026},
eprint = {2610.29264},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29264},
}
Uses RL as a demo generator, not a controller: a latent-conditioned policy finds several distinct successful solutions, exported as an imitation dataset; reports 78% real-robot success vs. 39% for the best baseline.把强化学习当作示教数据生成器而非部署控制器：以隐变量为条件的策略为同一任务找到多种不同的成功解法，导出为模仿学习数据集；报告真机下游成功率从最强基线的 39% 提升到 78%。
@misc{park2026discodemo,
title = {{DiscoDemo: Discovering Efficient and Diverse Robot Demonstrations for Imitation Learning}},
author = {Minho Park and Kinam Kim and Donghu Kim and Byungkun Lee and Dongyoon Hwang and Yongjae Shin and Junha Hyung and Hojoon Lee and Jaegul Choo},
year = {2026},
eprint = {2610.32093},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.32093},
}
Replay-free lifelong imitation learning: hierarchical dual-routing attention retrieves experts by instruction, then refines by context, plus key regularization against drift. Reports beating replay baselines on LIBERO.无回放的终身模仿学习：分层双路由注意力先按指令检索专家、再按上下文细化，并用键级几何正则抑制表征漂移带来的路由干扰；报告在多个 LIBERO 任务序列上优于无回放和有回放基线。
Fine-tunes diffusion and flow policies with automatic preference labels: rollouts are ranked by progress measured against a few expert demos, no human labels or reward design. Reports beating SFT on long-horizon tasks.用自动生成的偏好标签微调扩散与流匹配策略：以少量专家示教为参照衡量自主回放的顺序进度并据此排序，无需人工标注或手工奖励；报告在长时程操作任务上优于监督微调基线。
@misc{luu2026progress,
title = {{Progress-Guided Preference Generation for Aligning Generative Control Policies}},
author = {Tung M. Luu and Tri Ton and Duc M. Nguyen and Xuan Thanh Nguyen and Nguyen Gia Binh and Quang Tan Nguyen and Vu Duong and Daniel Sonntag and Duy M. H. Nguyen and Vien Anh Ngo and Chang D. Yoo},
year = {2026},
eprint = {2610.25692},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2610.25692},
}
100 h of wearable-captured human loco-manipulation (mostly professional work) aligning egocentric video, whole-body and finger motion, touch and step-level text; reports 71.9% lower body error than baselines.用可穿戴设备采集的 100 小时人类移动操作数据，侧重职业场景，对齐第一视角视频、全身与手指动作、触觉和步骤级文字描述；其补全全身动作的方法报告身体关键点误差比最强基线低 71.9%。
@misc{zhou2026egoexplorer,
title = {{EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ}},
author = {Yunsong Zhou and Xuekun Jiang and Haiqi Shen and Hangxu Liu and Chaojian Shi and Yujie Liu and Jingyuan Li and Haoran Yang and Jiafei Cao and Dongzhan Zhou and Dong Wang and Yao Mu and Chunhua Shen and Weinan Zhang},
year = {2026},
eprint = {2610.18872},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.18872},
}
Isaac Sim benchmark with 5 arms, 6 dexterous hands, ~200 assets and 18k human demos over 66 tasks; reports the best trained policy (a WAM) at only 29.1% on Core-50, near 0% on precision-fit and deformable tasks.基于 Isaac Sim 的灵巧操作基准：5 种机械臂、6 种灵巧手、约 200 个资产、1.8 万条人类演示、66 个任务；报告最好的训练策略（一种世界动作模型）在 Core-50 上仅 29.1%，精密装配和柔性物体任务接近 0%。
@misc{luo2026handson,
title = {{HandsOn: A Large-Scale Simulation Benchmark for Generalizable Dexterous Manipulation}},
author = {Tianwei Luo and Yiming He and Hongjin Wu and Ting Pan and Debin Ren and Haorui Guo and Junlin Yang and Yuxin Zuo and Zhehao Kou and Kaisen Yang and Zhuoqun Lai and Yi Ding and Jiale Yu and Yuchen Zhang and Zijie Qiu and Haozhan Li and Ning Ding},
year = {2026},
eprint = {2610.26765},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.26765},
}
Tests robot value (progress) models on 15 sim and 20 real tasks across task state, progress, failure/recovery and consistency; reports that good forward-progress scores hide weak history and recovery reasoning.在 15 个仿真和 20 个真实任务上评测机器人价值（进度）模型，覆盖任务状态、进度监测、失败与恢复、一致性四个维度；报告称前向进度得分高的模型在依赖历史和恢复判断上仍然薄弱。
@misc{liu2026robovalue,
title = {{RoboValue: A Fine-Grained Sim-and-Real Benchmark for Unified Evaluation of Robotic Value Models}},
author = {Shengbang Liu and Zhengye Du and Zhilong Wan and Honghao Su and Chenxiang Xia and Chang Ge and Jinyang Xiao and Nan Wang and Zhipeng Hu and Huining Yuan and Shu'ang Yu and Yue Chen and Tianxing Chen and Chuankang Li and Maoqing Yao and Wenbo Ding and Yu Wang and Guanghui Ren and Chao Yu},
year = {2026},
eprint = {2610.26663},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.26663},
}
216 LIBERO-based tasks with industrial constraints — conveyor loading/unloading/transfer, speed, time windows, tight tolerances; reports that VLAs and world action models degrade as constraints are combined.基于 LIBERO 的 216 个工业约束任务：传送带上料、下料与转运，可调节物体形状、带速、交互时间窗与公差；报告 VLA 和世界动作模型在多重约束叠加时性能逐步下降。
@misc{sung2026libero,
title = {{LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints}},
author = {Ming Shin Cindy Sung and Duan Leijiahao and Haoxiang Wang and Boning Tan and Liangna Fu and Chao Tan and Yafu Li and Yulong Chen and Chaoqun Wan and Zhen Huang and Shuxian Liang and Xian-Sheng Hua},
year = {2026},
eprint = {2610.16782},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2610.16782},
}
106 sim tasks for two dexterous hands along five axes (generalization, instructions, few demos, long horizon, memory), each with an out-of-domain twin; 8,080 teleoperated demos; seven VLAs benchmarked.双灵巧手仿真基准：106 个任务按泛化、指令遵循、少示教、长时程、记忆五个维度组织，每个任务配有域外变体；提供 8,080 条遥操作示教，并在统一协议下评测了七个 VLA 模型。
@misc{wang2026bidex,
title = {{BiDex-100: A Comprehensive Benchmark for Bimanual Dexterous Manipulation Across 100+ Simulation Tasks}},
author = {Yong Wang and Kecheng Zheng and Xing Zhu and Chao Xu and Yujun Shen and Shengze Cai},
year = {2026},
eprint = {2610.25459},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25459},
}
Benchmark for catching drifting, tumbling objects inside a spacecraft with a fixed-base arm, plus storage and servicing tasks. Reports a frozen generalist policy captured only 2/200 static targets.太空机器人操作基准：用固定基座机械臂抓取舱内漂浮、旋转的物体，另含样本存储与在轨维护任务。报告称所测的冻结通用策略在 200 个静止目标中只抓住 2 个。
@misc{wang2026astrobench,
title = {{AstroBench: Benchmarking Free-Floating Capture and Space Robotic Manipulation}},
author = {Zizhe Wang and Jia Deng and Xin Lu and Zehao Jia and Yi Huang and Yao Zhu},
year = {2026},
eprint = {2610.31064},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31064},
}
Isaac Lab digital-twin benchmark for language-conditioned spatial tasks on the low-cost SO-101 arm (138 objects, 916 episodes); sim ranks policies exactly as the real arm. Best VLAs ~61%, an LLM-agent harness 83.2%.基于 Isaac Lab 数字孪生的低成本 SO-101 机械臂语言条件空间操作基准（138 个物体、916 个评测回合）；仿真中的策略排名与真机完全一致。最好的 VLA 约 61%，由 LLM 写代码调用抓取策略的方案达 83.2%。
A portable hand-held rig for collecting demonstrations with a high-DoF robot hand, using the same hand hardware for collection and deployment, so there is no retargeting from human hands and no full robot setup needed.便携手持式数据采集装置，面向高自由度机器人灵巧手；采集与部署使用同一只机械手，无需从人手动作重定向，也无需搭建完整机器人系统。
@misc{xie2026dexport,
title = {{DexPort: A Hand-Held Skill Interface for Dexterous Hand Data Collection}},
author = {Amber Xie and Jiawei Fu and Xiaofeng Guo and Carmelo Sferrazza and Guanya Shi and Haozhi Qi},
year = {2026},
eprint = {2610.32969},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.32969},
}
Fully linkage-driven humanoid hand with 3 actuated DoF per finger; a flexure hinge and spring-steel drive rods replace many tiny parts, so it prints on a budget 3D printer at near human-hand size.完全连杆驱动的人形灵巧手，每根手指 3 个主动自由度；用柔性铰链和弹簧钢驱动杆取代大量微型零件，可用入门级 3D 打印机制造，尺寸接近真人手。
@misc{adwani2026phd,
title = {{PHD-Hand: A Printable Humanoid Dexterous Hand with Linkage-Driven 3-DOA Digits}},
author = {Neel Adwani and Muhaiminul Islam Akash and Saffia Ouali and Ethan Liu and Cong Wang},
year = {2026},
eprint = {2610.29030},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29030},
}
One operator drives a Unitree Go2 + Piper arm with a leader arm and two handheld controllers, with sensorless force feedback; records a unified 11-D action reused at deployment (shown by fine-tuning MolmoAct2).单人即可同时操控 Unitree Go2 四足与 Piper 机械臂：主端机械臂加两个手柄，无需力传感器即可力反馈；ROS 2 管线记录与部署共用的 11 维统一动作，并通过微调 MolmoAct2 验证从采集到部署的全流程。
@misc{langella2026locopilot,
title = {{LocoPilot: Single-Operator Bilateral Teleoperation for Loco-Manipulation Policy Learning}},
author = {Antonio Langella and Giovanni Minelli and Giovanni B. Dessy and Lorenzo Amatucci and Giulio Turrisi and Victor Barasuol and Claudio Semini},
year = {2026},
eprint = {2610.27998},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.27998},
}
Fully open UMI-style handheld gripper with a low-cost stereo camera for metric, low-drift trajectories, plus UMI-Loc-Bench-v1 for localization accuracy; hardware, software and data under a permissive license.完全开源的 UMI 式手持夹爪：3D 打印、标准 ISO 接口，用低成本双目相机获得公制尺度、低漂移的轨迹，并提出手持采集设备定位精度基准 UMI-Loc-Bench-v1；硬件、软件与数据以可商用许可证发布。
@misc{murali2026openjaws,
title = {{OpenJaws: An Open Hardware UMI Framework with Stereo Localization for Generalist Manipulation}},
author = {Adithyavairavan Murali and Yash Oza and Karanbir Chahal and Neel Jawale and Silas De Vries and Hesam Rabeti and Erwin Coumans and Balakumar Sundaralingam and Stan Birchfield},
year = {2026},
eprint = {2610.36598},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36598},
}
Pretrains a grasping VLA almost only on SynGrasp-1B, a billion-frame photorealistic sim dataset, co-trained with web data; reports zero-shot sim-to-real open-vocabulary grasping and few-shot adaptation.几乎只用十亿帧级的照片级仿真抓取数据集 SynGrasp-1B（并联合网络语义数据）预训练抓取 VLA；论文报告可零样本从仿真迁移到真实开放词汇抓取，并支持少样本适配。
@misc{deng2025graspvla,
title = {{GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data}},
author = {Shengliang Deng and Mi Yan and Songlin Wei and Haixin Ma and Yuxin Yang and Jiayi Chen and Zhiqi Zhang and Taoyu Yang and Xuheng Zhang and Wenhao Zhang and Heming Cui and Zhizheng Zhang and He Wang},
year = {2025},
eprint = {2505.03233},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2505.03233},
}
A dexterous VLA pretrained on human hand motion from mocap, VR and RGB video ('physical instruction tuning'), with part-level motion tokens, then aligned to 3D space and post-trained for real robot hands.灵巧手 VLA：先在动捕、VR 与普通视频中的人手动作上预训练（"物理指令微调"），使用部件级动作 token，再做三维空间对齐，并针对真实机器人灵巧手后训练。
@misc{luo2025being,
title = {{Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos}},
author = {Hao Luo and Yicheng Feng and Wanpeng Zhang and Sipeng Zheng and Ye Wang and Haoqi Yuan and Jiazheng Liu and Chaoyi Xu and Qin Jin and Zongqing Lu},
year = {2025},
eprint = {2507.15597},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2507.15597},
}
A hierarchical VLA in which a fine-tuned VLM draws a coarse 2D path for the end effector on the image and a low-level 3D policy follows it, so the VLM can be trained on cheap off-domain data such as sim or videos.分层 VLA：微调后的 VLM 在图像上画出末端执行器的粗略二维路径，再由底层三维策略跟随执行；因此 VLM 可以用仿真、视频等低成本的非本域数据训练。
@misc{li2025hamster,
title = {{HAMSTER: Hierarchical Action Models for Open-World Robot Manipulation}},
author = {Yi Li and Yuquan Deng and Jesse Zhang and Joel Jang and Marius Memmel and Raymond Yu and Caelan Reed Garrett and Fabio Ramos and Dieter Fox and Anqi Li and Abhishek Gupta and Ankit Goyal},
year = {2025},
eprint = {2502.05485},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.05485},
}
A pretrained VLM plans and a diffusion controller grasps with a dexterous hand, working on domain-invariant features from foundation models; reports 90%+ grasp success across thousands of unseen cluttered scenes.由预训练 VLM 做高层规划、扩散控制器驱动灵巧手抓取，底层输入为基础模型提取的域不变特征；报告在数千个未见过的杂乱场景中抓取成功率超过 90%。
@misc{zhong2025dexgraspvla,
title = {{DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping}},
author = {Yifan Zhong and Xuchuan Huang and Ruochong Li and Ceyao Zhang and Zhang Chen and Tianrui Guan and Fanlian Zeng and Ka Num Lui and Yuyao Ye and Yitao Liang and Yaodong Yang and Yuanpei Chen},
year = {2025},
eprint = {2502.20900},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.20900},
}
A slow latent diffusion policy plans action chunks while a fast policy reacts to tactile/force feedback; reports >35% higher scores than visual imitation baselines on three contact-rich tasks.慢速扩散策略在隐空间规划动作块，快速策略根据触觉/力反馈高频调整；论文报告在三个接触丰富任务上比纯视觉模仿学习基线总分高 35% 以上。
VPP — fine-tunes Stable Video Diffusion on robot and human videos and feeds its one-pass predictive features to a diffusion policy; reports 4.33 average length on CALVIN ABC→D and real Franka / dexterous-hand results.VPP：在机器人与人类操作视频上微调 Stable Video Diffusion，只做一次前向提取"预测性"视觉特征再交给扩散策略；论文报告 CALVIN ABC→D 平均完成长度 4.33，并有 Franka 与灵巧手真机实验。
@misc{hu2024video,
title = {{Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations}},
author = {Yucheng Hu and Yanjiang Guo and Pengchao Wang and Xiaoyu Chen and Yen-Jen Wang and Jianke Zhang and Koushil Sreenath and Chaochao Lu and Jianyu Chen},
year = {2024},
eprint = {2412.14803},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2412.14803},
}
UVA — one shared latent for video and actions with two light diffusion heads; masked training lets the same model act as policy, video generator and forward/inverse dynamics model, while keeping policy inference fast.UVA：视频与动作共享一个潜表示，配两个轻量扩散头解码；通过掩码训练，同一模型既能当策略，也能做视频生成和正/逆动力学，且策略推理仍然较快。
Genie Envisioner (AgiBot) — a video-diffusion world model (GE-Base) plus a flow-matching action decoder (GE-Act), an action-conditioned neural simulator (GE-Sim) and the EWMBench benchmark in one platform.智元 Genie Envisioner：一个平台集成视频扩散世界模型 GE-Base、流匹配动作解码器 GE-Act、动作条件神经仿真器 GE-Sim，以及世界模型评测基准 EWMBench。
@misc{liao2025genie,
title = {{Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation}},
author = {Yue Liao and Pengfei Zhou and Siyuan Huang and Donglin Yang and Shengcong Chen and Yuxin Jiang and Yue Hu and Jingbin Cai and Si Liu and Jianlan Luo and Liliang Chen and Shuicheng Yan and Maoqing Yao and Guanghui Ren},
year = {2025},
eprint = {2508.05635},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2508.05635},
}
DSRL — adapts a frozen diffusion policy by running RL over its input noise, not its weights; needs only forward passes, works with π0, and reports real-robot success rising from 20% to 90% in under 50 online episodes.DSRL：不改扩散策略的权重，而是对其输入的初始噪声做强化学习来"引导"输出动作；只需前向推理、可用于 π0，论文报告真机任务在不到 50 次在线交互内成功率从 20% 升至 90%。
@misc{wagenmaker2025steering,
title = {{Steering Your Diffusion Policy with Latent Space Reinforcement Learning}},
author = {Andrew Wagenmaker and Mitsuhiko Nakamoto and Yunchu Zhang and Seohong Park and Waleed Yagoub and Anusha Nagabandi and Abhishek Gupta and Sergey Levine},
year = {2025},
eprint = {2506.15799},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2506.15799},
}
Simulation pipeline that writes and checks task code with multimodal LLMs to mass-produce bimanual demos under heavy randomization (clutter, lighting, backgrounds, instructions); also a 50-task dual-arm benchmark.用多模态大模型自动生成并校验任务代码，在强域随机化（杂物、光照、背景、指令）下批量产出双臂演示数据的仿真管线，同时提供 50 个双臂任务的基准。
@misc{chen2025robotwin,
title = {{RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation}},
author = {Tianxing Chen and Zanxin Chen and Baijun Chen and Zijian Cai and Yibin Liu and Zixuan Li and Qiwei Liang and Xianliang Lin and Yiheng Ge and Zhenyu Gu and Weiliang Deng and Yubin Guo and Tian Nian and Xuanbing Xie and Qiangyu Chen and Kailun Su and Tianling Xu and Guodong Liu and Mengkang Hu and Huan-ang Gao and Kaixuan Wang and Zhixuan Liang and Yusen Qin and Xiaokang Yang and Ping Luo and Yao Mu},
year = {2025},
eprint = {2506.18088},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2506.18088},
}
Isaac Sim platform where an LLM builds tasks and layouts from a task-oriented scene graph over ~10K objects; its 200-scenario benchmark finds modular foundation-model systems generalize better than end-to-end policies.基于 Isaac Sim 的平台：LLM 依据任务导向场景图，从约 1 万个物体中生成任务与布局；其 200 个场景的基准发现，结合基础模型的模块化系统比端到端策略泛化更好。
@misc{gao2025genmanip,
title = {{GenManip: LLM-driven Simulation for Generalizable Instruction-Following Manipulation}},
author = {Ning Gao and Yilun Chen and Shuai Yang and Xinyi Chen and Yang Tian and Hao Li and Haifeng Huang and Hanqing Wang and Tai Wang and Jiangmiao Pang},
year = {2025},
eprint = {2506.10966},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2506.10966},
}
829 hours of egocentric Apple Vision Pro video with tracked 3D poses for every hand joint, across 194 tabletop tasks from tying shoelaces to folding laundry; includes hand-trajectory prediction benchmarks.用 Apple Vision Pro 采集的 829 小时第一视角视频，配有每个手部关节的三维姿态，覆盖从系鞋带到叠衣服的 194 种桌面任务，并提供手部轨迹预测基准。
@misc{hoque2025egodex,
title = {{EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video}},
author = {Ryan Hoque and Peide Huang and David J. Yoon and Mouli Sivapurapu and Jian Zhang},
year = {2025},
eprint = {2505.11709},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2505.11709},
}
A whole-body teleoperation interface plus a learning method for a wheeled bimanual robot doing long household tasks that need reach, balance and navigation.面向轮式双臂机器人的全身遥操作接口与学习方法，用于需要伸展、平衡与导航的长程家务任务。
@misc{jiang2025behavior,
title = {{BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities}},
author = {Yunfan Jiang and Ruohan Zhang and Josiah Wong and Chen Wang and Yanjie Ze and Hang Yin and Cem Gokmen and Shuran Song and Jiajun Wu and Li Fei-Fei},
year = {2025},
eprint = {2503.05652},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2503.05652},
}
A 3D-printable, 17-DoF tendon-driven hand with fingertip tactile sensing, under CHF 2,000 in materials; design files and code are open, and the paper reports long continuous runs for teleoperation and learning.可 3D 打印的 17 自由度腱驱动灵巧手，指尖带触觉传感，材料成本低于 2000 瑞士法郎；设计文件与代码开源，论文报告了可长时间连续运行用于遥操作与学习。
@misc{christoph2025orca,
title = {{ORCA: An Open-Source, Reliable, Cost-Effective, Anthropomorphic Robotic Hand for Uninterrupted Dexterous Task Learning}},
author = {Clemens C. Christoph and Maximilian Eberlein and Filippos Katsimalis and Arturo Roberti and Aristotelis Sympetheros and Michel R. Vogt and Davide Liconti and Chenyu Yang and Barnabas Gavin Cangan and Ronan J. Hinchet and Robert K. Katzschmann},
year = {2025},
eprint = {2504.04259},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2504.04259},
}
Exoskeleton demos are converted into pseudo-robot demonstrations; the paper reports its RISE-2 policy trained on them alone, with no robot data, performs comparably to one trained on teleoperated robot data.将外骨骼示教转换为“伪机器人”示教；论文报告仅用这些数据（无任何机器人数据）训练的 RISE-2 策略，效果与用遥操作机器人数据训练的策略相当。
@misc{fang2025airexo,
title = {{AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons}},
author = {Hongjie Fang and Chenxi Wang and Yiming Wang and Jingjing Chen and Shangning Xia and Jun Lv and Zihao He and Xiyan Yi and Yunhan Guo and Xinyu Zhan and Lixin Yang and Weiming Wang and Cewu Lu and Hao-Shu Fang},
year = {2025},
eprint = {2503.03081},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2503.03081},
}
DexUMI — an exoskeleton matched to the robot hand's fingertip workspace, plus inpainting that swaps the human hand for the robot's; reports 86% average success (Inspire, XHand) and ~3.2× faster collection than teleop.DexUMI：按目标机器人手的指尖工作空间设计的可穿戴手部外骨骼，再用视频修补把画面中的人手换成机器人手；论文报告在 Inspire 与 XHand 上平均成功率 86%，采集效率约为遥操作的 3.2 倍。
@misc{xu2025dexumi,
title = {{DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation}},
author = {Mengda Xu and Han Zhang and Yifan Hou and Zhenjia Xu and Linxi Fan and Manuela Veloso and Shuran Song},
year = {2025},
eprint = {2505.21864},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2505.21864},
}
An open-source teleoperation glove buildable in hours for under $600, with 21-DoF hand capture, 5-DoF cable-driven force feedback and fingertip haptics; reports the feedback matters for contact-rich tasks without vision.开源遥操作手套，几小时即可组装、成本低于 600 美元：21 自由度手部动捕、5 自由度绳驱力反馈和指尖振动触觉；报告在无视觉输入时力/触觉反馈对接触密集任务至关重要，采集数据可训练模仿学习策略。
@misc{zhang2025doglove,
title = {{DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove}},
author = {Han Zhang and Songbo Hu and Zhecheng Yuan and Huazhe Xu},
year = {2025},
eprint = {2502.07730},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.07730},
}
A human-sized, tendon-driven hand (5 fingers, 15 underactuated DoF) from 3D-printed and off-the-shelf parts; joint-to-actuator control is learned from MANUS-glove mocap data instead of hand-modelled.人手大小的腱驱动灵巧手（5 指、15 个欠驱动自由度），由 3D 打印件和现成零件制成；关节到电机的控制映射用 MANUS 数据手套的动捕数据学习得到，而非手工建模。
@misc{zorin2025ruka,
title = {{RUKA: Rethinking the Design of Humanoid Hands with Learning}},
author = {Anya Zorin and Irmak Guzey and Billy Yan and Aadhithya Iyer and Lisa Kondrich and Nikhil X. Bhattasali and Lerrel Pinto},
year = {2025},
eprint = {2504.13165},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2504.13165},
}
Puts ALOHA on a mobile base; co-training with static ALOHA data boosts mobile tasks like cooking and opening cabinets.将 ALOHA 装上移动底盘；与静态 ALOHA 数据联合训练显著提升做饭、开柜等移动任务表现。
@misc{fu2024mobile,
title = {{Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation}},
author = {Zipeng Fu and Tony Z. Zhao and Chelsea Finn},
year = {2024},
eprint = {2401.02117},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2401.02117},
}
Conditions a diffusion policy on a compact point-cloud encoding; more data-efficient and robust to viewpoint than images.用紧凑点云编码作为扩散策略条件，比图像输入更省数据、对视角更鲁棒。
@misc{ze2024d,
title = {{3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations}},
author = {Yanjie Ze and Gu Zhang and Kangning Zhang and Chenyuan Hu and Muhan Wang and Huazhe Xu},
year = {2024},
eprint = {2403.03954},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.03954},
}
@misc{wang2024dexcap,
title = {{DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation}},
author = {Chen Wang and Haochen Shi and Weizhuo Wang and Ruohan Zhang and Li Fei-Fei and C. Karen Liu},
year = {2024},
eprint = {2403.07788},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.07788},
}
ReKep — a VLM writes manipulation tasks as Python cost functions over 3D keypoints (relational keypoint constraints), solved by a hierarchical optimizer in real time; zero-shot, no task-specific training.ReKep：由视觉语言模型把操作任务写成定义在三维关键点上的 Python 代价函数（关系关键点约束），再由分层优化器实时求解；零样本，无需针对任务训练。
@misc{huang2024rekep,
title = {{ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation}},
author = {Wenlong Huang and Chen Wang and Yunzhu Li and Ruohan Zhang and Li Fei-Fei},
year = {2024},
eprint = {2409.01652},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2409.01652},
}
Treats egocentric human video with 3D hand tracking (Project Aria glasses) as robot data and co-trains one policy on both; reports an extra hour of human hand data helps more than an extra hour of robot data.把佩戴 Project Aria 眼镜采集、带三维手部跟踪的第一视角人类视频当作机器人数据，与机器人数据共同训练同一个策略；论文报告多 1 小时人手数据比多 1 小时机器人数据更有用。
@misc{kareer2024egomimic,
title = {{EgoMimic: Scaling Imitation Learning via Egocentric Video}},
author = {Simar Kareer and Dhruv Patel and Ryan Punamiya and Pranay Mathur and Shuo Cheng and Chen Wang and Judy Hoffman and Danfei Xu},
year = {2024},
eprint = {2410.24221},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.24221},
}
Seer forecasts the next visual state and feeds it to an inverse dynamics model that outputs actions, trained end to end and pretrained on DROID; reports 4.28 avg. length on CALVIN ABC-D plus LIBERO and real-robot gains.Seer 先预测未来视觉状态，再由逆动力学模型据此输出动作，端到端训练并在 DROID 上预训练；报告 CALVIN ABC-D 平均任务长度 4.28，在 LIBERO-LONG 和真机任务上也有提升。
@misc{tian2024predictive,
title = {{Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation}},
author = {Yang Tian and Sizhe Yang and Jia Zeng and Ping Wang and Dahua Lin and Hao Dong and Jiangmiao Pang},
year = {2024},
eprint = {2412.15109},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.15109},
}
@misc{huang2024d,
title = {{3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing}},
author = {Binghao Huang and Yixuan Wang and Xinyi Yang and Yiyue Luo and Yunzhu Li},
year = {2024},
eprint = {2410.24091},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.24091},
}
GR-1 — a GPT-style model that predicts both actions and future frames; pre-training on Ego4D video prediction before robot fine-tuning lifts CALVIN unseen-scene success from 53.3% to 85.4% (as reported).GR-1：GPT 式模型同时预测动作与未来画面；先在 Ego4D 上做视频预测预训练、再用机器人数据微调，论文报告 CALVIN 未见场景成功率从 53.3% 提升到 85.4%。
@misc{wu2023unleashing,
title = {{Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation}},
author = {Hongtao Wu and Ya Jing and Chilam Cheang and Guangzeng Chen and Jiafeng Xu and Xinghang Li and Minghuan Liu and Hang Li and Tao Kong},
year = {2023},
eprint = {2312.13139},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2312.13139},
}
@misc{liu2024ok,
title = {{OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics}},
author = {Peiqi Liu and Yaswanth Orru and Jay Vakil and Chris Paxton and Nur Muhammad Mahi Shafiullah and Lerrel Pinto},
year = {2024},
eprint = {2401.12202},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2401.12202},
}
An open-source stack for training manipulation policies with RL on a real arm — learned reward classifiers, reset-free training and an impedance controller.在真实机械臂上用强化学习训练操作策略的开源软件栈：学习式奖励分类器、免重置训练与阻抗控制器。
@misc{luo2024serl,
title = {{SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning}},
author = {Jianlan Luo and Zheyuan Hu and Charles Xu and You Liang Tan and Jacob Berg and Archit Sharma and Stefan Schaal and Chelsea Finn and Abhishek Gupta and Sergey Levine},
year = {2024},
eprint = {2401.16013},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2401.16013},
}
DPPO — fine-tunes a pre-trained diffusion policy with PPO by treating the denoising steps as part of the MDP; reports stable gains over other RL fine-tuning methods and sim-to-real transfer.DPPO：把去噪步骤视为 MDP 的一部分，用 PPO 微调预训练扩散策略；报告称比其他强化学习微调方法更稳定，并实现了仿真到真机迁移。
@misc{ren2024diffusion,
title = {{Diffusion Policy Policy Optimization}},
author = {Allen Z. Ren and Justin Lidard and Lars L. Ankile and Anthony Simeonov and Pulkit Agrawal and Anirudha Majumdar and Benjamin Burchfiel and Hongkai Dai and Max Simchowitz},
year = {2024},
eprint = {2409.00588},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2409.00588},
}
Extends MimicGen to humanoids with two dexterous hands, turning 60 human demos into 21K simulated ones across bimanual tasks, plus a real-to-sim-to-real pipeline tested on real can sorting.把 MimicGen 扩展到双灵巧手人形机器人，从 60 条人类演示生成 2.1 万条双臂仿真演示，并给出在真实分拣罐子任务上验证的 real-to-sim-to-real 流程。
@misc{jiang2024dexmimicgen,
title = {{DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning}},
author = {Zhenyu Jiang and Yuqi Xie and Kevin Lin and Zhenjia Xu and Weikang Wan and Ajay Mandlekar and Linxi Fan and Yuke Zhu},
year = {2024},
eprint = {2410.24185},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.24185},
}
@misc{pumacay2024colosseum,
title = {{THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation}},
author = {Wilbert Pumacay and Ishika Singh and Jiafei Duan and Ranjay Krishna and Jesse Thomason and Dieter Fox},
year = {2024},
eprint = {2402.08191},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2402.08191},
}
100 task categories over 2,000+ objects that need common sense, world knowledge and multi-step reasoning, not just instruction following; evaluates VLAs, embodied agents and VLMs in one suite.100 类任务、2000 多个物体，任务需要常识、世界知识与多步推理，而不只是照指令执行；在同一套件中评测 VLA、具身智能体与 VLM。
@misc{zhang2024vlabench,
title = {{VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Tasks}},
author = {Shiduo Zhang and Zhe Xu and Peiju Liu and Xiaopeng Yu and Yuan Li and Qinghui Gao and Zhaoye Fei and Zhangyue Yin and Zuxuan Wu and Yu-Gang Jiang and Xipeng Qiu},
year = {2024},
eprint = {2412.18194},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.18194},
}
Upgraded ALOHA bimanual rig (stronger grippers, gravity compensation, sturdier frame) with open hardware designs and a MuJoCo model for simulation.ALOHA 双臂平台的升级版（更强夹爪、重力补偿、更稳固的框架），开源硬件设计并提供 MuJoCo 仿真模型。
@misc{team2024aloha,
title = {{ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation}},
author = {ALOHA 2 Team and Jorge Aldaco and Travis Armstrong and Robert Baruch and Jeff Bingham and Sanky Chan and Kenneth Draper and Debidatta Dwibedi and Chelsea Finn and Pete Florence and Spencer Goodrich and Wayne Gramlich and Torr Hage and Alexander Herzog and Jonathan Hoech and Thinh Nguyen and Ian Storz and Baruch Tabanpour and Leila Takayama and Jonathan Tompson and Ayzaan Wahid and Ted Wahrburg and Sichun Xu and Sergey Yaroshenko and Kevin Zakka and Tony Z. Zhao},
year = {2024},
eprint = {2405.02292},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2405.02292},
}
Apple Vision Pro teleoperation of two dexterous robot hands with real-time retargeting, collision and singularity avoidance, and low-cost vibration haptics for the operator.用 Apple Vision Pro 实时遥操作双灵巧手，带实时重定向、碰撞与奇异点规避，并为操作者提供低成本振动触觉反馈。
@misc{ding2024bunny,
title = {{Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning}},
author = {Runyu Ding and Yuzhe Qin and Jiyue Zhu and Chengzhe Jia and Shiqi Yang and Ruihan Yang and Xiaojuan Qi and Xiaolong Wang},
year = {2024},
eprint = {2407.03162},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.03162},
}
One modular teleop interface that mixes RGB-D body tracking, VR controllers, keyboard and joystick for whole-body mobile manipulators (Tiago++, HSR, Fetch), in sim and real.模块化遥操作接口，可组合 RGB-D 人体追踪、VR 手柄、键盘与摇杆，控制 Tiago++、HSR、Fetch 等移动操作机器人做全身动作，仿真和真机都支持。
@misc{dass2024telemoma,
title = {{TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation}},
author = {Shivin Dass and Wensi Ai and Yuqian Jiang and Samik Singh and Jiaheng Hu and Ruohan Zhang and Peter Stone and Ben Abbatematteo and Roberto Martín-Martín},
year = {2024},
eprint = {2403.07869},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.07869},
}
HATO — bimanual setup from two UR5e arms and repurposed prosthetic hands with fingertip touch sensors, VR teleop for data collection, and visuotactile policies for tasks like pouring and handovers.HATO：用两台 UR5e 机械臂和改装的假肢手（指尖带触觉传感器）搭建双臂系统，用 VR 遥操作采集数据，并训练视觉-触觉策略完成倒酒、交接等任务。
@misc{lin2024learning,
title = {{Learning Visuotactile Skills with Two Multifingered Hands}},
author = {Toru Lin and Yu Zhang and Qiyang Li and Haozhi Qi and Brent Yi and Sergey Levine and Jitendra Malik},
year = {2024},
eprint = {2404.16823},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2404.16823},
}
An inexpensive open-source mobile base with powered casters, so it is fully holonomic; carries arbitrary arms and comes with phone teleoperation for collecting mobile-manipulation demos for imitation learning.低成本开源移动底盘，采用主动脚轮实现全向（完整约束）运动，可搭载任意机械臂，并配有手机遥操作界面，方便为模仿学习采集移动操作示教数据。
@misc{wu2024tidybot,
title = {{TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning}},
author = {Jimmy Wu and William Chong and Robert Holmberg and Aaditya Prasad and Yihuai Gao and Oussama Khatib and Shuran Song and Szymon Rusinkiewicz and Jeannette Bohg},
year = {2024},
eprint = {2412.10447},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.10447},
}
Robot-free UMI gripper demos train the manipulation policy; a sim-trained whole-body controller tracks its end-effector trajectories on a quadruped. Reports >70% success on all tasks and zero-shot reuse of an arm policy.用手持 UMI 夹爪采集无需机器人的示教来训练操作策略，再由仿真训练的全身控制器在四足机器人上跟踪其输出的末端轨迹；报告所有任务成功率超过 70%，并能零样本复用原为固定机械臂训练的策略。
@misc{ha2024umi,
title = {{UMI-on-Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers}},
author = {Huy Ha and Yihuai Gao and Zipeng Fu and Jie Tan and Shuran Song},
year = {2024},
eprint = {2407.10353},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.10353},
}
@misc{shaw2024bimanual,
title = {{Bimanual Dexterity for Complex Tasks}},
author = {Kenneth Shaw and Yulong Li and Jiahui Yang and Mohan Kumar Srirama and Ray Liu and Haoyu Xiong and Russell Mendonca and Deepak Pathak},
year = {2024},
eprint = {2411.13677},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2411.13677},
}
LLMs write code that composes 3D affordance/constraint maps, which a planner turns into trajectories — no robot training.大模型编写代码组合 3D 可供性/约束地图，再由规划器生成轨迹，无需机器人训练。
@misc{huang2023voxposer,
title = {{VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models}},
author = {Wenlong Huang and Chen Wang and Ruohan Zhang and Yunzhu Li and Jiajun Wu and Li Fei-Fei},
year = {2023},
eprint = {2307.05973},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2307.05973},
}
Dobb-E collects home demonstrations with a cheap reacher-grabber "stick", pretrains on them, then adapts to a new household task from a few minutes of data.Dobb-E 用低成本的手持夹取“棒”采集家庭示范数据并预训练，在新家庭任务上只需几分钟数据即可适配。
@misc{shafiullah2023bringing,
title = {{On Bringing Robots Home}},
author = {Nur Muhammad Mahi Shafiullah and Anant Rai and Haritheja Etukuru and Yiqian Liu and Ishan Misra and Soumith Chintala and Lerrel Pinto},
year = {2023},
eprint = {2311.16098},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2311.16098},
}
RVT — re-renders the point cloud into a few virtual views and uses a multi-view Transformer to predict keyframe poses; reported to train and run much faster than voxel-based PerAct on RLBench. Repo also hosts RVT-2.RVT：把点云重新渲染为若干虚拟视角，再用多视角 Transformer 预测关键帧位姿；论文报告在 RLBench 上训练与推理都比基于体素的 PerAct 快得多。代码库同时包含 RVT-2。
@misc{goyal2023rvt,
title = {{RVT: Robotic View Transformer for 3D Object Manipulation}},
author = {Ankit Goyal and Jie Xu and Yijie Guo and Valts Blukis and Yu-Wei Chao and Dieter Fox},
year = {2023},
eprint = {2306.14896},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2306.14896},
}
RotateIt trains in simulation with privileged object information, then distills to a policy that rotates diverse objects in hand using vision, touch and proprioception on a real hand.RotateIt 先在仿真中利用特权物体信息训练，再蒸馏为仅用视觉、触觉和本体感觉的策略，在真实灵巧手上旋转多种物体。
@misc{qi2023general,
title = {{General In-Hand Object Rotation with Vision and Touch}},
author = {Haozhi Qi and Brent Yi and Sudharshan Suresh and Mike Lambeta and Yi Ma and Roberto Calandra and Jitendra Malik},
year = {2023},
eprint = {2309.09979},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.09979},
}
Covers a robot hand with cheap binary (contact / no contact) touch sensors and trains an RL policy in simulation that rotates unseen objects in hand on a real robot without any vision.在灵巧手上铺设廉价的二值（有/无接触）触觉传感器，在仿真中用强化学习训练策略，迁移到真机后无需视觉即可在手中旋转未见过的物体。
@misc{yin2023rotating,
title = {{Rotating without Seeing: Towards In-hand Dexterity through Touch}},
author = {Zhao-Heng Yin and Binghao Huang and Yuzhe Qin and Qifeng Chen and Xiaolong Wang},
year = {2023},
eprint = {2303.10880},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2303.10880},
}
Turns tactile readings into points in the same 3D frame as the camera point cloud and the hand model, so one visuotactile point-cloud input drives in-hand object rotation.将触觉读数转换为与相机点云、手部模型处于同一三维坐标系的点，用统一的视触觉点云输入驱动手内物体旋转。
@misc{yuan2023robot,
title = {{Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing}},
author = {Ying Yuan and Haichuan Che and Yuzhe Qin and Binghao Huang and Zhao-Heng Yin and Kang-Won Lee and Yi Wu and Soo-Chul Lim and Xiaolong Wang},
year = {2023},
eprint = {2312.01853},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2312.01853},
}
NeuralFeels fuses vision and touch from a multi-fingered hand into an online neural field, reconstructing an object's shape and tracking its pose while the hand manipulates it.NeuralFeels 将多指手的视觉与触觉在线融合为神经场，在操作过程中同时重建物体形状并跟踪其位姿。
@misc{suresh2023neural,
title = {{Neural feels with neural fields: Visuo-tactile perception for in-hand manipulation}},
author = {Sudharshan Suresh and Haozhi Qi and Tingfan Wu and Taosha Fan and Luis Pineda and Mike Lambeta and Jitendra Malik and Mrinal Kalakrishnan and Roberto Calandra and Michael Kaess and Joseph Ortiz and Mustafa Mukadam},
year = {2023},
eprint = {2312.13469},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2312.13469},
}
6D pose estimation and tracking of unseen objects from a CAD model or a few reference images, with no per-object training.只需 CAD 模型或少量参考图像即可对未见过的物体做 6D 位姿估计与跟踪，无需逐物体训练。
@misc{wen2023foundationpose,
title = {{FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects}},
author = {Bowen Wen and Wei Yang and Jan Kautz and Stan Birchfield},
year = {2023},
eprint = {2312.08344},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2312.08344},
}
Tracks the 6-DoF pose of a never-seen object in RGB-D video while building its neural 3D model online, with no CAD model needed; useful for manipulating novel objects.在 RGB-D 视频中跟踪从未见过物体的六自由度位姿，同时在线构建其神经三维模型，无需 CAD 模型；适用于操作新物体。
@misc{wen2023bundlesdf,
title = {{BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects}},
author = {Bowen Wen and Jonathan Tremblay and Valts Blukis and Stephen Tyree and Thomas Muller and Alex Evans and Dieter Fox and Jan Kautz and Stan Birchfield},
year = {2023},
eprint = {2303.14158},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2303.14158},
}
VAMP — vectorizes collision checking with CPU SIMD so sampling-based planners like RRT-Connect solve arm motion-planning queries in microseconds to milliseconds, no GPU needed.VAMP：用 CPU SIMD 指令把碰撞检测向量化，使 RRT-Connect 等基于采样的规划器在微秒到毫秒级求解机械臂运动规划，无需 GPU。
Discretizes each action dimension and predicts Q-values per dimension autoregressively with a Transformer, plus a conservative regularizer; trained offline on large real robot datasets mixing demos and autonomous data.将每个动作维度离散化，用 Transformer 自回归地逐维预测 Q 值，并加入保守正则；在混合演示与自主采集数据的大规模真实机器人数据集上离线训练。
@misc{chebotar2023q,
title = {{Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions}},
author = {Yevgen Chebotar and Quan Vuong and Alex Irpan and Karol Hausman and Fei Xia and Yao Lu and Aviral Kumar and Tianhe Yu and Alexander Herzog and Karl Pertsch and Keerthana Gopalakrishnan and Julian Ibarz and Ofir Nachum and Sumedh Sontakke and Grecia Salazar and Huong T Tran and Jodilyn Peralta and Clayton Tan and Deeksha Manjunath and Jaspiar Singht and Brianna Zitkovich and Tomas Jackson and Kanishka Rao and Chelsea Finn and Sergey Levine},
year = {2023},
eprint = {2309.10150},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.10150},
}
Turns a handful of human demos into thousands by splitting them into object-centric segments and replaying those segments in new scenes; widely reused to scale imitation data.把少量人类演示切分为以物体为中心的片段，并在新场景中变换重放，从而扩增为数千条演示；被广泛用于扩充模仿学习数据。
@misc{mandlekar2023mimicgen,
title = {{MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations}},
author = {Ajay Mandlekar and Soroush Nasiriany and Bowen Wen and Iretiayo Akinola and Yashraj Narang and Linxi Fan and Yuke Zhu and Dieter Fox},
year = {2023},
eprint = {2310.17596},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2310.17596},
}
Camera-only teleoperation for arm + dexterous hand pairs - tracks the operator's hand and retargets it to many different robot hands, in sim or on real hardware.仅用相机即可遥操作"机械臂 + 灵巧手"：跟踪操作者手部并重定向到多种不同的机器人手，可用于仿真或真机。
@misc{qin2023anyteleop,
title = {{AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System}},
author = {Yuzhe Qin and Wei Yang and Binghao Huang and Karl Van Wyk and Hao Su and Xiaolong Wang and Yu-Wei Chao and Dieter Fox},
year = {2023},
eprint = {2307.04577},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2307.04577},
}
A portable dual-arm exoskeleton (about $600 in parts, per the paper) for joint-level teleop and cheap in-the-wild demos; reports few-minute teleop plus wild data rivals much longer teleop-only training.便携式双臂外骨骼（论文称零件成本约 600 美元），可做关节级遥操作并低成本采集野外示教；报告称少量遥操作数据加野外数据即可达到甚至超过长时间纯遥操作训练的效果。
@misc{fang2023airexo,
title = {{AirExo: Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the Wild}},
author = {Hongjie Fang and Hao-Shu Fang and Yiming Wang and Jieji Ren and Jingjing Chen and Ruo Zhang and Weiming Wang and Cewu Lu},
year = {2023},
eprint = {2309.14975},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2309.14975},
}
@misc{chen2022visual,
title = {{Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes}},
author = {Tao Chen and Megha Tippur and Siyang Wu and Vikash Kumar and Edward Adelson and Pulkit Agrawal},
year = {2022},
eprint = {2211.11744},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2211.11744},
}
@misc{nair2022r,
title = {{R3M: A Universal Visual Representation for Robot Manipulation}},
author = {Suraj Nair and Aravind Rajeswaran and Vikash Kumar and Chelsea Finn and Abhinav Gupta},
year = {2022},
eprint = {2203.12601},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2203.12601},
}
Over a million physically validated ShadowHand grasps on thousands of objects, synthesized by optimizing a differentiable force-closure energy; a standard training set for dexterous grasp models.通过优化可微的力封闭能量合成、并经物理仿真验证的百万级 ShadowHand 抓取，覆盖数千个物体，是灵巧抓取模型的常用训练集。
@misc{wang2022dexgraspnet,
title = {{DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation}},
author = {Ruicheng Wang and Jialiang Zhang and Jiayi Chen and Yinzhen Xu and Puhao Li and Tengyu Liu and He Wang},
year = {2022},
eprint = {2210.02697},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2210.02697},
}
PerAct — a Perceiver Transformer over voxelized RGB-D observations that predicts the next keyframe gripper pose as voxel classification; one language-conditioned policy for many RLBench and real tasks.PerAct：在体素化的 RGB-D 观测上运行 Perceiver Transformer，把下一关键帧夹爪位姿当作体素分类来预测；一个语言条件策略即可覆盖众多 RLBench 与真机任务。
AnyGrasp — dense 7-DoF parallel-gripper grasp detection trained on real sensor data with analytic labels and center-of-mass awareness, plus grasp tracking across frames for moving objects; released as an SDK.AnyGrasp：基于真实传感器数据与解析标注训练、考虑物体质心的稠密 7 自由度平行夹爪抓取检测，并能跨帧跟踪抓取以应对运动物体；以 SDK 形式发布。
@misc{fang2022anygrasp,
title = {{AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains}},
author = {Hao-Shu Fang and Chenxi Wang and Hongjie Fang and Minghao Gou and Jirong Liu and Hengxu Yan and Wenhai Liu and Yichen Xie and Cewu Lu},
year = {2022},
eprint = {2212.08333},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2212.08333},
}
Fuses camera, contact microphone and GelSight touch with self-attention in an imitation-learned policy, studied on dense packing and pouring tasks.在模仿学习策略中用自注意力融合相机、接触麦克风和 GelSight 触觉，在密集装箱与倾倒任务上做了研究。
@misc{li2022see,
title = {{See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation}},
author = {Hao Li and Yizhi Zhang and Junzhe Zhu and Shaoxiong Wang and Michelle A Lee and Huazhe Xu and Edward Adelson and Li Fei-Fei and Ruohan Gao and Jiajun Wu},
year = {2022},
eprint = {2212.03858},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2212.03858},
}
LLMs write Python that calls perception and control APIs, turning instructions into reactive robot programs.大模型编写调用感知与控制 API 的 Python 代码，把指令变成可响应的机器人程序。
@misc{liang2022code,
title = {{Code as Policies: Language Model Programs for Embodied Control}},
author = {Jacky Liang and Wenlong Huang and Fei Xia and Peng Xu and Karol Hausman and Brian Ichter and Pete Florence and Andy Zeng},
year = {2022},
eprint = {2209.07753},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2209.07753},
}
A neural policy trained on millions of planner-generated trajectories that moves an arm to goals straight from point clouds.在数百万条规划器生成的轨迹上训练的神经策略，可直接根据点云把机械臂移动到目标位置。
2.4M egocentric RGB-D frames (4,000+ sequences, 800 object instances in 16 categories, 610 rooms) of people handling rigid and articulated objects, with segmentation, hand and object pose labels.240 万帧第一视角 RGB-D（4000 多段序列、16 类 800 个物体实例、610 个房间），记录人操作刚体与铰接物体，带分割、手部与物体位姿标注。
@misc{liu2022hoi,
title = {{HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction}},
author = {Yunze Liu and Yun Liu and Che Jiang and Kangbo Lyu and Weikang Wan and Hao Shen and Boqiang Liang and Zhoujie Fu and He Wang and Li Yi},
year = {2022},
eprint = {2203.01577},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2203.01577},
}
Affordance labels for ~1,800 household objects plus 50K intent-oriented hand-object interactions recorded on 100 of them; a common source for dexterous grasp synthesis. Follow-up OakInk2 covers bimanual tasks.为约 1800 个日常物体标注可供性，并在其中 100 个物体上记录 5 万次带意图的手-物交互；常用于灵巧抓取生成。后续 OakInk2 扩展到双手任务。
@misc{yang2022oakink,
title = {{OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction}},
author = {Lixin Yang and Kailin Li and Xinyu Zhan and Fei Wu and Anran Xu and Liu Liu and Cewu Lu},
year = {2022},
eprint = {2203.15709},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2203.15709},
}
2.1M video frames of two hands manipulating articulated objects (scissors, laptops…) with accurate 3D hand and object meshes from motion capture; a reference for bimanual hand-object reconstruction.210 万帧双手操作铰接物体（剪刀、笔记本电脑等）的视频，借助动作捕捉提供精确的手与物体三维网格；双手-物体重建的常用基准。
@misc{fan2022arctic,
title = {{ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation}},
author = {Zicong Fan and Omid Taheri and Dimitrios Tzionas and Muhammed Kocabas and Manuel Kaufmann and Michael J. Black and Otmar Hilliges},
year = {2022},
eprint = {2204.13662},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2204.13662},
}
CLIPort — combines CLIP's semantic "what" stream with Transporter's spatial "where" stream for language-conditioned tabletop manipulation; an early bridge from web vision-language models to robot actions.CLIPort：把 CLIP 提供的语义"是什么"通路与 Transporter 的空间"在哪里"通路结合，实现语言条件的桌面操作；是较早把网络视觉语言模型接入机器人动作的工作。
@misc{shridhar2021cliport,
title = {{CLIPort: What and Where Pathways for Robotic Manipulation}},
author = {Mohit Shridhar and Lucas Manuelli and Dieter Fox},
year = {2021},
eprint = {2109.12098},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2109.12098},
}
Contact-GraspNet — predicts 6-DoF parallel-jaw grasps for a whole cluttered scene from one depth view by treating observed points as grasp contacts, which shrinks the problem to 4 DoF per point.Contact-GraspNet：从单帧深度图为整个杂乱场景预测 6 自由度平行夹爪抓取；把观测到的点当作抓取接触点，使每个点只需学习 4 个自由度。
Ruckig — open-source C++/Python library for time-optimal, jerk-limited online trajectories to any target position, velocity and acceleration; a common drop-in for smooth arm motion on real controllers.Ruckig：开源 C++/Python 库，可实时生成到任意目标位置、速度和加速度的时间最优、限加加速度（jerk）轨迹；常被直接用于真实控制器上的机械臂平滑运动。
robomimic — a controlled study of imitation and offline RL on human-collected manipulation demos (demo quality, observation choices, history-aware BC), released with the framework and datasets.robomimic：在人类采集的操作示教数据上系统比较模仿学习与离线强化学习（示教质量、观测选择、带历史的行为克隆），并开源框架与数据集。
@misc{mandlekar2021what,
title = {{What Matters in Learning from Offline Human Demonstrations for Robot Manipulation}},
author = {Ajay Mandlekar and Danfei Xu and Josiah Wong and Soroush Nasiriany and Chen Wang and Rohun Kulkarni and Li Fei-Fei and Silvio Savarese and Yuke Zhu and Roberto Martín-Martín},
year = {2021},
eprint = {2108.03298},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2108.03298},
}
Adds fast rigid-body physics and an interactive apartment dataset (ReplicaCAD) to Habitat, plus a home-rearrangement benchmark for mobile manipulators.为 Habitat 加入快速刚体物理与可交互公寓数据集 ReplicaCAD，并提出面向移动操作机器人的家居物品重排基准。
@misc{szot2021habitat,
title = {{Habitat 2.0: Training Home Assistants to Rearrange their Habitat}},
author = {Andrew Szot and Alex Clegg and Eric Undersander and Erik Wijmans and Yili Zhao and John Turner and Noah Maestre and Mustafa Mukadam and Devendra Chaplot and Oleksandr Maksymets and Aaron Gokaslan and Vladimir Vondrus and Sameer Dharur and Franziska Meier and Wojciech Galuba and Angel Chang and Zsolt Kira and Vladlen Koltun and Jitendra Malik and Manolis Savva and Dhruv Batra},
year = {2021},
eprint = {2106.14405},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2106.14405},
}
@misc{chao2021dexycb,
title = {{DexYCB: A Benchmark for Capturing Hand Grasping of Objects}},
author = {Yu-Wei Chao and Wei Yang and Yu Xiang and Pavlo Molchanov and Ankur Handa and Jonathan Tremblay and Yashraj S. Narang and Karl Van Wyk and Umar Iqbal and Stan Birchfield and Jan Kautz and Dieter Fox},
year = {2021},
eprint = {2104.04631},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2104.04631},
}
Transporter Nets — pick-and-place as spatial matching of deep features cropped around the pick point; very sample-efficient on rearrangement tasks, released with the Ravens benchmark.Transporter 网络：把抓取-放置建模为以抓取点为中心的深度特征的空间匹配，在重排类任务上样本效率很高，并随论文发布 Ravens 基准。
@misc{zeng2020transporter,
title = {{Transporter Networks: Rearranging the Visual World for Robotic Manipulation}},
author = {Andy Zeng and Pete Florence and Jonathan Tompson and Stefan Welker and Jonathan Chien and Maria Attarian and Travis Armstrong and Ivan Krasin and Dan Duong and Ayzaan Wahid and Vikas Sindhwani and Johnny Lee},
year = {2020},
eprint = {2010.14406},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2010.14406},
}
Open-source Gym-style benchmark for manipulating deformable objects such as cloth, rope and liquids; shows that high-dimensional, partially observable states make these tasks hard for standard RL.开源、Gym 接口的可变形物体操作基准（布料、绳子、液体等）；实验表明高维且部分可观测的状态让这类任务对常规强化学习方法很困难。
@misc{lin2020softgym,
title = {{SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation}},
author = {Xingyu Lin and Yufei Wang and Jake Olkin and David Held},
year = {2020},
eprint = {2011.07215},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2011.07215},
}
@misc{openai2019solving,
title = {{Solving Rubik's Cube with a Robot Hand}},
author = {OpenAI and Ilge Akkaya and Marcin Andrychowicz and Maciek Chociej and Mateusz Litwin and Bob McGrew and Arthur Petron and Alex Paino and Matthias Plappert and Glenn Powell and Raphael Ribas and Jonas Schneider and Nikolas Tezak and Jerry Tworek and Peter Welinder and Lilian Weng and Qiming Yuan and Wojciech Zaremba and Lei Zhang},
year = {2019},
eprint = {1910.07113},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1910.07113},
}
A Shadow Hand reorients a cube using RL trained purely in randomized simulation — a sim-to-real milestone.完全在随机化仿真中用强化学习训练，Shadow 灵巧手在真实世界翻转方块，是 sim-to-real 里程碑。
@misc{openai2018learning,
title = {{Learning Dexterous In-Hand Manipulation}},
author = {OpenAI and Marcin Andrychowicz and Bowen Baker and Maciek Chociej and Rafal Jozefowicz and Bob McGrew and Jakub Pachocki and Arthur Petron and Matthias Plappert and Glenn Powell and Alex Ray and Jonas Schneider and Szymon Sidor and Josh Tobin and Peter Welinder and Lilian Weng and Wojciech Zaremba},
year = {2018},
eprint = {1808.00177},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1808.00177},
}
Learns a compact vision + force/torque representation with self-supervised objectives, then trains a peg-insertion policy with RL on top of it on a real robot.用自监督目标学习视觉 + 力/力矩的紧凑联合表征，再在其上用强化学习在真实机器人上训练插孔策略。
@misc{lee2018making,
title = {{Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks}},
author = {Michelle A. Lee and Yuke Zhu and Krishnan Srinivasan and Parth Shah and Silvio Savarese and Li Fei-Fei and Animesh Garg and Jeannette Bohg},
year = {2018},
eprint = {1810.10191},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1810.10191},
}
Off-policy Q-learning with a cross-entropy-method action optimizer, trained on several hundred thousand real grasp attempts collected by a fleet of robots; reports 96% grasp success on unseen objects.使用交叉熵方法优化动作的离策略 Q 学习，在机器人集群采集的数十万次真实抓取上训练；报告对未见物体的抓取成功率为 96%。
@misc{kalashnikov2018qt,
title = {{QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation}},
author = {Dmitry Kalashnikov and Alex Irpan and Peter Pastor and Julian Ibarz and Alexander Herzog and Eric Jang and Deirdre Quillen and Ethan Holly and Mrinal Kalakrishnan and Vincent Vanhoucke and Sergey Levine},
year = {2018},
eprint = {1806.10293},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1806.10293},
}
The YCB object set — 73 everyday objects with scans, textured meshes and simulation models, plus benchmarking protocols; widely used in grasping and pose-estimation work.YCB 物体集：73 个日常物体，附扫描数据、带纹理网格与仿真模型，以及基准测试规程；被抓取与位姿估计研究广泛使用。
@misc{calli2015benchmarking,
title = {{Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols}},
author = {Berk Calli and Aaron Walsman and Arjun Singh and Siddhartha Srinivasa and Pieter Abbeel and Aaron M. Dollar},
year = {2015},
eprint = {1502.03143},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1502.03143},
}
Ian Chuang, Hiya Shah, Vamsi Chilamkur, Iman Soltani et al.
Abstract摘要
Dexterous manipulation with multi-fingered hands is the next frontier of robotics, yet learned hand skills remain limited to simple grasps or basic repeated motions, such as rotating an object. As robots attempt more demanding in-hand maneuvers, safety, such as not dropping the object, becomes critical, yet it is rarely measured. We study in-hand object pose reaching, a task that demands diverse and difficult grasps: moving an object to almost any position and orientation within reach of the fingers, at any wrist orientation, through long sequences of consecutive goals. We present a recipe …
@misc{chuang2026pushing,
title = {{Pushing Dexterity and Safety for In-Hand Object Pose Reaching That Works Remarkably Well}},
author = {Ian Chuang and Hiya Shah and Vamsi Chilamkur and Iman Soltani and Claire Tomlin},
year = {2026},
eprint = {2610.37652},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37652},
}
Jianyu Hou, Jiawei Fu, Weikang Wan, Minghui Jiang et al.
Abstract摘要
Supernumerary robotic fingers augment human dexterity by enabling a single hand to perform complex "hold-and-manipulate" tasks that typically require bimanual coordination. However, existing devices are often hindered by bulky hardware that limits interaction to large objects and rely on explicit hand motion triggers that lack environmental context. To address these challenges, we present EgoThumb, a fully open-source, vision-driven supernumerary robotic thumb. EgoThumb features a compact, dual tendon-driven finger whose actuation is relocated to the forearm, reducing distal weight while …
@misc{hou2026egothumb,
title = {{EgoThumb: A Vision-Driven Supernumerary Robotic Thumb for Autonomous Grasp and Holding Assistance}},
author = {Jianyu Hou and Jiawei Fu and Weikang Wan and Minghui Jiang and Jiajun Li and Hao Su},
year = {2026},
eprint = {2610.37582},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37582},
}
Threading a quadrotor through a moving narrow gap is a demanding test of agile flight, requiring centimeter-level spatial alignment and precise temporal execution. We address this challenge using a control policy that operates directly on onboard visual and inertial measurements to output collective thrust and body-rate commands. The task is difficult because success lies on a thin feasible manifold: small errors in timing, position, attitude, or visual observability cause collision. Prior planning and learning methods rely on privileged state, teachers, planners, or distillation; learning …
Manipulation policies are evaluated predominantly by binary task success, which cannot tell whether the commanded gesture is the one the object's kinematics admit. We introduce Constraint-Referenced Strategy (CRS), which projects the commanded end-effector twist onto the object's joint axis, separating progress along the joint from off-constraint waste. CRS requires a joint model and a joint-following scripted controller derived from it, but no recorded demonstration or learned judge. On 74 simulated articulated objects, success registers three transfer failures as lower rates but attributes …
Failure diagnosers for robot manipulation are increasingly vision--language models (VLMs), trained and evaluated on single-arm failures produced by perturbing a successful demonstration. Bimanual tool use adds failures of other kinds: a tool that slips out of its socket while still held, two correct gestures made at different times, and attempts that fail for two reasons at once. On a simulated bimanual tool-use task whose failure causes are read from the recorded trace, we measure what each sensing channel lets a diagnoser tell. The tool's slip is read on the tool hand; which hand was late …
@misc{bonin2026which,
title = {{Which Hand Was Late? Diagnosis for Robot Failure Recovery in Bimanual Tool Use}},
author = {Baptiste Bonin and Caro Strickland and Audrey Durand},
year = {2026},
eprint = {2610.37533},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37533},
}
We trained four widely used imitation-learning policies, ACT, Diffusion Policy, SmolVLA and $\pi_{0.5}$, on nested subsets of 12, 25, 50, 100 and 200 teleoperated demonstrations of one pick-and-place task with distractor objects, using one fixed training recipe per policy. Every trained model was then run on a physical SO-101 arm on a fixed set of object placements: 1,340 scored episodes in 126 blocks. ACT, trained from scratch, rose from 1 success in 40 episodes at 12 demonstrations to 37 in 40 at 100. $\pi_{0.5}$ with its action expert fine-tuned was the best policy when data was scarce (16 …
Yang Liu, Xiaoli Liu, Liuzhao Li, Yuduo Song et al.
Abstract摘要
World-action models (WAMs) incorporate the prediction of future observations into robot policy learning. However, most WAMs build on billion-parameter vision-language or video-generation backbones, which incur high training cost and inference latency. In this letter, we present GAWM (Geometry-Aware World Model), a compact multi-task WAM that predicts future observations in the latent space of a frozen DINOv3 encoder. GAWM compresses the features of each camera view into a small set of tokens. A lightweight predictor, conditioned on the task rather than on actions, forecasts these tokens at …
@misc{liu2026gawm,
title = {{GAWM: A Compact Latent World-Action Model for Multi-Task Robot Manipulation}},
author = {Yang Liu and Xiaoli Liu and Liuzhao Li and Yuduo Song and Muyuan Liu and Xiang Gao},
year = {2026},
eprint = {2610.37091},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37091},
}
Maintaining contact in non-prehensile manipulation requires more than nominal friction cone feasibility: a force barely inside the cone and one well inside it are both feasible, but they provide different margins against slip and contact loss. This paper uses signed friction distance to measure friction margin in force space and derives a task-adaptive margin from the force correction needed to balance pivot moment deviations in bimanual tilting. The resulting contact margins are embedded in a sequence of second-order cone programs (SOCPs), each giving a globally optimal contact force …
@misc{lee2026keeping,
title = {{Keeping Contact: Task-Adaptive Friction Margins for Bimanual Non-Prehensile Manipulation}},
author = {Qian Ying Lee and Lin Yang and Domenico Campolo},
year = {2026},
eprint = {2610.36961},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36961},
}
Ri-Zhao Qiu, Bowen Wen, Yifeng Jiang, Chen Tessler et al.
Abstract摘要
Real-to-Sim-to-Real (Real2Sim2Real) enables structured world representations for planning, interpretability, and safe execution. Most existing approaches rely on passive perception, which creates real2sim gaps due to imperfect observation. Inspired by how humans perceive actively through actions, we propose ActiveAlign, a framework that leverages active perception to align the Real2Sim2Real gap from interactions. ActiveAlign introduces two complementary modules: (i) ActiveAlign-Waving, which performs hand-eye calibration by exploiting active hand motions and differentiable rendering, and (ii) …
@misc{qiu2026activealign,
title = {{ActiveAlign: Aligning Egocentric Manipulation via Active Perception}},
author = {Ri-Zhao Qiu and Bowen Wen and Yifeng Jiang and Chen Tessler and Eric Heiden and Xue Bin Peng and Xiaolong Wang and Stan Birchfield and others},
year = {2026},
eprint = {2610.36859},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36859},
}
Jihao Li, Jianguo Wang, Haotian Guo, Yanzhe Wang et al.
Abstract摘要
Humanoid dexterous hands are a core component of embodied intelligence, enabling robots to translate high-level intent into rich and adaptive physical interaction. However, existing designs remain constrained by a persistent trade-off among anthropomorphic kinematic complexity, mechanical robustness, load capacity and control simplicity, limiting dexterous manipulation in unstructured environments. Here we present Torch-Hand, a fully articulated humanoid dexterous hand that replicates the joint structure and motion hierarchy of the human hand with 21 degrees of freedom (DOF), comprising a …
@misc{li2026fully,
title = {{A fully articulated humanoid dexterous hand with human-level joint structure enabled by linkage-based actuation}},
author = {Jihao Li and Jianguo Wang and Haotian Guo and Yanzhe Wang and Guodong Lu and Huixu Dong},
year = {2026},
eprint = {2610.36789},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36789},
}
Zongyuan Zhang, Christopher Lehnert, Will N. Browne, Jonathan M. Roberts
Abstract摘要
Mobile robot manipulators extend contact-task capability beyond the workspace of fixed robot installations, but their bases must also sustain the forces generated by the task. On lightweight platforms, base motion and support compliance can change the force and motion realised at the workpiece. How these non-rigid base effects manifest during sustained contact remains insufficiently characterised. This paper investigates non-rigid base effects in mobile robot manipulators through a sanding case study combining exploratory simulation with experiments on two lightweight, unactuated wheeled …
@misc{zhang2026shaky,
title = {{On Shaky Ground: Characterising Non-Rigid Base Effects in Mobile Manipulation}},
author = {Zongyuan Zhang and Christopher Lehnert and Will N. Browne and Jonathan M. Roberts},
year = {2026},
eprint = {2610.36725},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36725},
}
Tuo An, Chuhao Zhou, Jiahua Dong, Jingliang Li et al.
Abstract摘要
Flow-based policies for language-conditioned multi-task manipulation commonly share a single $\textit{Gaussian}$ source prior and a common linear transport path across tasks. However, reliably executing multiple tasks with distinct motion requirements remains challenging for a shared policy. We introduce $\textbf{TOPS}$ ($\textbf{T}$ask-$\textbf{O}$riented $\textbf{P}$riors and $\textbf{S}$chedules), which adapts the flow source prior and interpolation schedule to each task without modifying the network architecture. TOPS first constructs multimodal task sources from reverse-mapped expert …
@misc{an2026tops,
title = {{TOPS: Reshaping Flow Source and Transport for Multi-Task Robot Manipulation}},
author = {Tuo An and Chuhao Zhou and Jiahua Dong and Jingliang Li and Jiaqi Bai and Jindou Jia and Jianfei Yang},
year = {2026},
eprint = {2610.36694},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36694},
}
Hongyu Wu, Chong Liu, Shaoxuan Xie, Shaqi Luo et al.
Abstract摘要
Bimanual robot manipulation plays an important role in real-world robotic applications, but the high cost of collecting bimanual demonstrations limits the scalable development of bimanual policy learning. Meanwhile, existing single-arm manipulation data are difficult to directly transfer to bimanual systems due to the Single-to-Dual gap in action spaces, coordination requirements, and arm-side spatial preference. To address this issue, we propose RoboSTD, a zero-shot Single-to-Dual data construction method for bimanual robot learning. RoboSTD first applies sagittal-plane mirroring to …
@misc{wu2026robostd,
title = {{RoboSTD: Zero-Shot Single-to-Dual Transfer via Sagittal Mirroring for Bimanual Learning}},
author = {Hongyu Wu and Chong Liu and Shaoxuan Xie and Shaqi Luo and Hanyu Feng and Shihan Wu and Guocai Yao},
year = {2026},
eprint = {2610.36685},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36685},
}
Adithyavairavan Murali, Yash Oza, Karanbir Chahal, Neel Jawale et al.
Abstract摘要
Handheld data-collection interfaces such as the Universal Manipulation Interface (UMI) allow manipulation demonstrations to be gathered in the wild without a robot in the loop, but the quality of the resulting action labels is bounded by the accuracy of vision-based localization. Existing systems recover end-effector pose from monocular visual-inertial odometry, which is subject to scale ambiguity and drift, or delegate tracking to closed-source modules and external infrastructure that reintroduce cost and setup burden. We present OpenJaws, a fully open hardware and software framework for …
@misc{murali2026openjaws,
title = {{OpenJaws: An Open Hardware UMI Framework with Stereo Localization for Generalist Manipulation}},
author = {Adithyavairavan Murali and Yash Oza and Karanbir Chahal and Neel Jawale and Silas De Vries and Hesam Rabeti and Erwin Coumans and Balakumar Sundaralingam and others},
year = {2026},
eprint = {2610.36598},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36598},
}
Cunxi Dai, Botao He, Francois R Hogan, Wei Pan et al.
Abstract摘要
Humans are dexterous, but traditional data collection paradigms such as teleoperation, kinesthetic teaching, and wearable exoskeletons force humans to act through the robot embodiment, bottlenecking their dexterity. Retargeting can avoid this. However, existing methods purely translate kinematics and ignore the real-world contact dynamics essential for task success. We present CARD: Contact-Anchored Retargeting for Dexterous Manipulation, a simulator and object-free framework that retargets human demonstrations to robot actions by explicitly preserving contact force targets. CARD unifies …
@misc{dai2026card,
title = {{CARD: Contact-Anchored Retargeting for Dexterous Manipulation}},
author = {Cunxi Dai and Botao He and Francois R Hogan and Wei Pan and Rao Fu and Wen-Han Hsieh and Ruoshi Liu and Jitendra Malik and others},
year = {2026},
eprint = {2610.36539},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36539},
}
Low-cost arms such as the SO-101 have become the most common entry point to robot learning, yet no released policy performs spatially constrained, language-conditioned tasks on them out of the box, and existing SO-101 benchmarks run only on physical hardware with simple tasks and few held-out objects. We introduce SO-101 Bench, a simulated benchmark for language-conditioned spatial manipulation built on an Isaac Lab digital twin of a real SO-101 setup. It has four tasks scored as five families, 138 objects in seen, unseen-instance and unseen-class splits, 916 evaluation episodes with subsets …
Fanqi Yu, Matteo Tiezzi, Cigdem Beyan, Tommaso Apicella et al.
Abstract摘要
Robotic agents operating in real-world environments must adapt continuously from a stream of multimodal demonstrations, often under constraints that preclude storing or revisiting past data. In this replay-free, single-pass lifelong imitation learning setting, sequential updates can progressively distort the representation space, making expert-based approaches particularly sensitive to routing interference, causing expert selection to degrade over time. We propose HIDRA, a structured routing framework that mitigates this issue through a hierarchical dual-attention mechanism, which decouples …
Simone Manni, Annika Wollschl\"ager, Dario Bellicoso, Abel Gawel et al.
Abstract摘要
Complex whole-body loco-manipulation requires executing diverse maneuvers, such as crouching, grasping, and locomotion, within a single long-horizon task. While performing simple pick-and-place behaviors is well-established, scaling to complex, long-horizon tasks remains difficult. Currently, this challenge is addressed by relying on human motion data and correspondingly humanoid platforms, which limits platform independence and introduces substantial data processing overhead. Instead, we extend proximal policy optimization (PPO) to train a single policy for multi-phase tasks across diverse …
@misc{manni2026learning,
title = {{Learning Multi-Phase Robot Loco-Manipulation via Phase-Conditioned Policy Optimization}},
author = {Simone Manni and Annika Wollschl\"ager and Dario Bellicoso and Abel Gawel and Marco Hutter and Simon Le Cleac'h and Jan Br\"udigam},
year = {2026},
eprint = {2610.36270},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36270},
}
Philip Huang, Fabio Ramos, Jiaoyang Li, Caelan Garrett et al.
Abstract摘要
Multi-arm manipulation has the potential to dramatically improve the throughput, efficiency, and dexterity of many manipulation tasks. Recent work coordinates multiple robot arms in a shared workcell by allocating and scheduling tasks for greater parallelism, jointly planning multi-arm motions, or directly learning multi-arm policies. So far, these approaches are either too slow, rely on domain-specific assumptions, or scale poorly with the number of robots and task horizon. Thus, we introduce cuArms, a conflict-based planner co-designed with GPU parallelism that efficiently decomposes the …
@misc{huang2026cuarms,
title = {{cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation}},
author = {Philip Huang and Fabio Ramos and Jiaoyang Li and Caelan Garrett and Lars Johannsmeier},
year = {2026},
eprint = {2610.36250},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.36250},
}
Sihang Li, Xiaofeng Guo, Jinkun Cao, Charlie Cheng et al.
Abstract摘要
Many everyday tasks require controlling contact force as well as motion. On a humanoid, this force must often be coordinated across both hands and the whole body, and cannot be specified by kinematic trajectories alone. Therefore, we present FLORE, a framework for learning force-reactive humanoid loco-manipulation. We collect robot-free demonstrations using two force-sensing UMI-style handheld grippers for hand motion and contact wrenches, and wearable trackers for body motion. A force-reactive diffusion policy takes vision, proprioception, and measured wrenches as input, then predicts …
@misc{li2026learning,
title = {{Learning Force-Reactive Humanoid Loco-Manipulation}},
author = {Sihang Li and Xiaofeng Guo and Jinkun Cao and Charlie Cheng and Ruihan Yang and Chen Feng and Guanya Shi and Haozhi Qi},
year = {2026},
eprint = {2610.35862},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35862},
}
Jooyoung Hong, Noboru Myers, Obin Kwon, Yixian Yang et al.
Abstract摘要
Existing manipulation data collection methods typically offer either accurate robot-state acquisition through kinematic-chain-based teleoperation or convenient, portable operation through vision-based approaches. We present DEX-PHD, a portable handheld device that combines these advantages for dexterous manipulation by integrating a dexterous robot hand, continuous per-finger control, and multimodal logging. The system records proprioceptive information, fingertip contact data, and wrist-camera video, while the operator directly controls the robot hand through joint-level mapping. Its …
@misc{hong2026dex,
title = {{DEX-PHD: A Dexterous Portable Handheld Device for Multimodal Data Collection}},
author = {Jooyoung Hong and Noboru Myers and Obin Kwon and Yixian Yang and Joohyung Kim},
year = {2026},
eprint = {2610.35462},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35462},
}
Data-enabled predictive control has recently emerged as a promising control paradigm, enabling a transparent optimal-control formulation without the need to learn an explicit dynamics model. This paper presents a number of extensions paving the path towards its use in real-time robotics tasks. Following recent work, we utilize a nonlinear predictor based on Gaussian kernels to represent the relationship between input and output data. We propose a damped Gauss-Newton method that enables real-time control by efficiently exploiting the least-squares structure of the control problem. In addition, …
@misc{sharma2026rokon,
title = {{RoKON: Efficient and Adaptive Data-Enabled Predictive Control for Robotic Manipulation}},
author = {Akshun Sharma and Zhongqi Wei and Buqing Ou and Frederike D\"umbgen},
year = {2026},
eprint = {2610.35458},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35458},
}
Shitong Liu, Jingwei Zhang, Sheng Lian, Xinyu Zhuang et al.
Abstract摘要
Realizing Embodied AI for Science requires robots to translate experimental goals into reliable physical procedures, reducing hazardous and repetitive human work while generating evidence for scientific discovery. Although language-based planning and learned manipulation provide complementary capabilities, their integration requires robots to adapt subsequent operations to actual execution outcomes. For example, an unsuccessful placement changes what must be done next, even when the experimental goal remains unchanged. Existing benchmarks assess planning, manipulation, and laboratory …
@misc{liu2026labference,
title = {{Labference: Benchmarking the Reasoning-Action Connection in Robotic Laboratories}},
author = {Shitong Liu and Jingwei Zhang and Sheng Lian and Xinyu Zhuang and Kaiwen Guo and Quan Liu and Chunhai Zhao and Jiaxin He and others},
year = {2026},
eprint = {2610.35252},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35252},
}
Ignacio Cuiral-Zueco, David Hardman, Michele Pierallini, Erfan Shahriari
Abstract摘要
Dexterous manipulation of rigid objects has reached a level of maturity where grasp stability, control, and planning strategies are successfully applied in industry and logistics. In contrast, manipulating deformable objects (cables, plastic pieces, cloth) remains far more challenging, as rigid-object methods do not account for their additional complexity. This paper identifies semantic failure analysis as a key step toward mature deformable-object manipulation and supports this claim with proof-of-concept experiments. Unlike rigid-object manipulation, where goals are well-defined in terms of …
@misc{cuiralzueco2026language,
title = {{The Language of Deformation: Semantic Failure Analysis as a Prerequisite for Dexterous Manipulation of Deformable Objects}},
author = {Ignacio Cuiral-Zueco and David Hardman and Michele Pierallini and Erfan Shahriari},
year = {2026},
eprint = {2610.35103},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35103},
}
Dianyi Yang, Mengya Liu, Zhumei Wang, Yu Liu et al.
Abstract摘要
Learning generalizable humanoid loco-manipulation remains bottlenecked by costly real-world demonstrations. Simulation provides a scalable alternative, but requires visually faithful, physics-ready scenes and executable whole-body motions. We present Zero-HLM, a real-to-sim-to-real framework that transforms smartphone captures and simulated source demonstrations into scalable training data for humanoid loco-manipulation. Gaussian-guided agentic real-to-sim reconstruction aligns visual and physical scenes; motion-environment co-adaptation uses rollout feedback to correct whole-body references …
@misc{yang2026zero,
title = {{Zero-HLM: Generalizable Humanoid Loco-Manipulation without Real Robot Data}},
author = {Dianyi Yang and Mengya Liu and Zhumei Wang and Yu Liu and Xiaochen Zhang and Yixuan Li and Le Ma and Yuhang Yang and others},
year = {2026},
eprint = {2610.35075},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35075},
}
Annika Delucchi, Vincenzo Di Paola, Bruno Marco Quadrelli, Matteo Zoppi
Abstract摘要
Estimating forces acting on Deformable Linear Objects (DLOs) is challenging due to their underactuated nature. However, reliable contact estimation is crucial to precisely control them and enable safe manipulation tasks. Differently from the various approaches presented in the literature, this work adopts a probabilistic formulation where the DLO state and sensor data are related through conditional distributions. In particular, factor graphs are used to determine the magnitude, direction, and location of a 3D force acting on a DLO, while simultaneously reconstructing its shape. Since DLOs …
@misc{delucchi2026d,
title = {{3D-Force Estimation for Dual-arm Manipulation of Deformable Linear Objects via Factor Graphs}},
author = {Annika Delucchi and Vincenzo Di Paola and Bruno Marco Quadrelli and Matteo Zoppi},
year = {2026},
eprint = {2610.35058},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35058},
}
Wei Wang, Wenqiao Zhang, Zheqi Lv, Zheng Dong et al.
Abstract摘要
Vision-language-action (VLA) policies can select among discrete candidates for pick-and-place manipulation or generate continuous action chunks for motion control. Improving both forms of decision making through a common mechanism while retaining the capabilities of a pretrained policy is an important adaptation problem. Inspired by Jev's structured, probabilistic decision interface, we propose JevVLA, a lightweight energy-based adaptation framework for VLA policies. JevVLA instantiates this inspiration by modeling the relative quality of action proposals: with the base policy frozen, it …
@misc{wang2026jevvla,
title = {{JevVLA: Jev-Inspired Energy-Based Adaptation for Vision-Language-Action Policies}},
author = {Wei Wang and Wenqiao Zhang and Zheqi Lv and Zheng Dong and Yingjie Niu and Jiaqi Zhu and Jun Xiao and Chao Li and others},
year = {2026},
eprint = {2610.35010},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35010},
}
Rui Cao, Jia-Hui Pan, Yichuan Li, Yunfan Yu et al.
Abstract摘要
Grasping is a foundational capability in robotic manipulation. Scene-level 6-DoF grasp synthesis relies on point clouds for metric geometry, yet real-world point observations are often noisy, incomplete, and over-smoothed. RGB images provide object-region, boundary, and appearance cues, yet two bottlenecks limit their effective use in scene representation and local grasp reasoning. We present MMGNet, a Multi-Modal Grasp Synthesis Network that addresses these bottlenecks through hierarchical image-to-3D feature injection and rotational graspness modeling. Hierarchical injection integrates …
@misc{cao2026mmgnet,
title = {{MMGNet: Multi-Modal 6-DoF Grasp Synthesis for Cluttered Scenes}},
author = {Rui Cao and Jia-Hui Pan and Yichuan Li and Yunfan Yu and Chi-Wing Fu and Jiangliu Wang and Yun-Hui Liu},
year = {2026},
eprint = {2610.34893},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34893},
}
Haibao Yu, Jiaqi Meng, Zijian Zhang, Bowen Jing et al.
Abstract摘要
We introduce PhysLadder, a hierarchical Sim-Real benchmark for evaluating physical interaction capabilities in contact-rich manipulation. Its 16 household tasks span three cumulative levels: contact selection and geometric alignment (L1), force regulation within sustained contact (L2), and coordinated contact reconfiguration and force regulation (L3). Each task is instantiated in physically and visually matched simulated and real-world environments, with shared task definitions and success criteria. We evaluate GPT-6 Astra zero-shot with three trials per task in each environment. Real-world …
@misc{yu2026how,
title = {{How High Can GPT-6 Astra Climb in Contact-Rich Manipulation? PhysLadder: A Hierarchical, Matched Sim-Real Benchmark for Physical Interaction}},
author = {Haibao Yu and Jiaqi Meng and Zijian Zhang and Bowen Jing and Lei Yang and Yue Hu and Chenchen Ge and Yiming Hou and others},
year = {2026},
eprint = {2610.34527},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34527},
}
Yanjinghao Xu, Jiexin Zhang, Huan Zhao, Jianjia Mo et al.
Abstract摘要
Large-scale vision, language, and video pretraining has substantially improved the perception and generalization capabilities of robotic manipulation models, but action generation for complex dexterous manipulation remains constrained by the scarcity of robot demonstrations. Limited robot demonstrations must be used to learn both action representations and task-conditioned action distributions, making it difficult to adequately learn action representations and, in turn, limiting the ability of downstream generative models to generate complex dexterous actions. To address this limitation, we …
@misc{xu2026scaling,
title = {{Scaling Dexterous Manipulation via Action Representation Pretraining}},
author = {Yanjinghao Xu and Jiexin Zhang and Huan Zhao and Jianjia Mo and Zhenghao Yin and Dingkang Liang and Xiang Bai and Han Ding},
year = {2026},
eprint = {2610.34453},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34453},
}
Xinchen Yu, Yoonkyo Jung, Haoran Chen, Zikui Cai et al.
Abstract摘要
Simulation enables robot-learning experiments at scale, but acquiring physics-informed digital twins often requires human manipulation and manual annotation of recorded interactions. To reduce this reliance on human effort, we present AuRA, a system that automates robot interaction and data preparation for digital-twin reconstruction. AuRA combines execution-aware grasp selection, gripper-motion measurements derived from robot states, and registered pre-interaction geometry and appearance priors. These choices enable autonomous collection, avoid reliance on visual gripper tracking, and …
@misc{yu2026aura,
title = {{AuRA: From Atoms to Bits through Autonomous Robot Interaction}},
author = {Xinchen Yu and Yoonkyo Jung and Haoran Chen and Zikui Cai and Aayush Talreja and Seungjae Lee and Hyunjin Kim and Furong Huang},
year = {2026},
eprint = {2610.34452},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34452},
}
Nuo Chen, Shizeng Zhang, Yujie Yang, Jinghong Wang et al.
Abstract摘要
In this paper, we propose ThinkLocoManip, an agentic harness for legged loco-manipulation designed to tackle complex, long-horizon tasks in large-scale environments. Existing approaches typically follow either direct action generation or a plan-then-act paradigm, both of which can struggle with the generalization and error-recovery capabilities required for extended task execution. In contrast, ThinkLocoManip adopts an interleaved reasoning-and-acting paradigm, allowing the agent to continuously reason about its current state, execute actions, observe their outcomes, and adapt its strategy …
@misc{chen2026thinklocomanip,
title = {{ThinkLocoManip: A Whole-Body Control Agentic Harness for Legged Loco-Manipulation}},
author = {Nuo Chen and Shizeng Zhang and Yujie Yang and Jinghong Wang and Zhenyang Qu and Xiao-Ming Wu and Yunfei Guo and Zhongwen Lu and others},
year = {2026},
eprint = {2610.34370},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34370},
}
Haoyu Zhao, Xingyue Zhao, Zhian Su, Bohan Hou et al.
Abstract摘要
Scaling vision-language-action (VLA) models is constrained by scarce action-labeled demonstrations and incompatible control spaces across robot embodiments. We present RynnVLA-Latent, a framework that turns heterogeneous interaction videos into scalable supervision for robot policy pretraining. At its core, RynnLAM learns continuous, 3D-grounded latent actions from visual transitions without requiring native robot controls. By combining residual future-feature prediction, camera-compensated 3D-flow supervision, and camera-motion decoupling, RynnLAM encourages representations that capture …
@misc{zhao2026rynnvla,
title = {{RynnVLA-Latent: Scaling Robotic Manipulation Foundation Models with 3D-Grounded Latent Actions}},
author = {Haoyu Zhao and Xingyue Zhao and Zhian Su and Bohan Hou and Yinzhou Tang and Fengyi Wang and Kehan Li and Yuming Jiang and others},
year = {2026},
eprint = {2610.34356},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34356},
}
Mengdi Li, Boxian Yao, Xiyu Zhu, Yifan Yang et al.
Abstract摘要
Deformable linear objects (DLO) pose a coupled perception and control challenge: their slender geometry makes precise localization difficult, while their deformations depend on object-specific physical properties. To address this challenge, we propose PS-DLO, a property- and spatially aware DLO manipulation policy. First, contrastive image-text learning maps the initial observation to categorical descriptions of DLO properties, providing explicit physical and structural priors for action generation. Second, SAM3 extracts mask-guided features from future observations, which are distilled into …
@misc{li2026ps,
title = {{PS-DLO: Property- and Spatially Aware Deformable Linear Object Manipulation Policy}},
author = {Mengdi Li and Boxian Yao and Xiyu Zhu and Yifan Yang and Yunfan Lou and Xuan Han and Tao Liu and Zhibo Pang},
year = {2026},
eprint = {2610.34147},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34147},
}
Sirui Chen, Huang Huang, Qineng Wang, Wenlong Huang et al.
Abstract摘要
Robot tasks require grounding spatial language in physical scenes, but task success alone cannot establish whether spatial instructions are followed. We present BEHAVIOR-Spatial, a benchmark built on BEHAVIOR-1K that measures spatial question answering, point-based target grounding, and robot action to examine how VLM spatial understanding relates to VLA behavior. It comprises 3,256 spatial questions across 49 activities, 1,240 grounding queries across 14 manipulation tasks, and action evaluations covering those 14 tasks and 10 navigation tasks. Our manipulation protocol evaluates held-out …
@misc{chen2026behavior,
title = {{Behavior Spatial Benchmark: Evaluating Spatial Understanding for Robotics}},
author = {Sirui Chen and Huang Huang and Qineng Wang and Wenlong Huang and Manling Li and Ruohan Zhang and Li Fei-Fei and Jiajun Wu},
year = {2026},
eprint = {2610.34051},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34051},
}
Long-horizon manipulation often depends on object properties that cannot be reliably inferred from appearance alone. Physical interaction can reveal these properties, but it can also change the world in ways that invalidate subsequent actions. We present a task and motion planning (TAMP) framework that integrates task-directed interactive information-gathering with interleaved planning and execution. Building on PDDLStream, our framework plans optimistically with these interactive actions, verifies their outcomes during execution, and adapts the remaining plan by retaining still-applicable …
Jingwen Li, Levin Kuhlmann, Jason Friedman, Gideon Kowadlo
Abstract摘要
Bimanual robotic control requires simultaneous management of distinct functional demands under motor noise, energetic costs, and communication delays. We investigate how learning responsibility should be organised across modular controllers and the two arms in a simulated musculoskeletal system with two communicating recurrent controllers. Using a 2 x 2 design, we compare differential versus shared controller objectives and fixed versus balanced hand-task assignments. Task-aligned functional specialisation was strongest when differential objectives were combined with a fixed assignment …
@misc{li2026learning,
title = {{Learning Functional Specialisation for Bimanual Robotic Control}},
author = {Jingwen Li and Levin Kuhlmann and Jason Friedman and Gideon Kowadlo},
year = {2026},
eprint = {2610.33947},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.33947},
}
Vision-language-action (VLA) models enable robots to follow instructions in typed text, limiting broader deployment in natural speech. The traditional solution by prepending a speech recognition before VLA to convert speech into texts would incur additional latency and propagate errors. To address this, we present Speech-$\pi_{0.5}$, which maps raw speech directly to motor commands without explicit textual transcriptions. Specifically, built on $\pi_{0.5}$, we use a cross-modal projection to convert voice frames directly into audio tokens with significant response latency reduction. To …
@misc{nan2026late,
title = {{Late Breaking Results: Audio-Language-Action Models with Direct Speech Conditioning for Robotic Manipulation}},
author = {Enfu Nan and Pu Zhao and Yixiao Chen and Lin Zhao and Juyi Lin and Chen Wang and Weiwei Chen and Yanzhi Wang},
year = {2026},
eprint = {2610.33866},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2610.33866},
}
Zixuan Chen, Lin Cao, Fenggang Zhang, Haotong Zhang et al.
Abstract摘要
Long-horizon manipulation requires actions whose validity depends on earlier interactions, yet visual progress can conceal missing task prerequisites. We propose RoboLongR, an offline-to-online framework that learns history-dependent rewards to guide policy adaptation to new tasks. Its central objective is causal consistency: aligning rewarded progress with prerequisites supported by execution history. Stage-shuffle supervision encourages execution-order sensitivity, recurrent memory preserves evidence of earlier interactions, and two-round training refines trajectory segments using predicted …
@misc{chen2026robolongr,
title = {{RoboLongR: Causally Consistent Rewards Teach Robots to Progressively Master Long-Horizon Manipulation}},
author = {Zixuan Chen and Lin Cao and Fenggang Zhang and Haotong Zhang and Yangtao Chen and Jieqi Shi and Jing Huo and Yang Gao},
year = {2026},
eprint = {2610.33863},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.33863},
}
Jianibieke Adalibieke, Qianwei Han, Xueyi Liu, Yuzhe Qin et al.
Abstract摘要
Language is a natural way to command robots, but converting a single instruction into a long-horizon, contact-rich hand-object interaction remains challenging: synthesized references are noisy, human-to-robot retargeting introduces embodiment bias, and fixed-reference tracking lets small errors snowball. We address this with AdaDexTrack, a modulator-in-the-loop framework for language-conditioned manipulation tracking. A distilled generalist tracker serves as the skill carrier, while a tightly aligned modulator performs three feedback corrections: reference modulation (continual adjustment of …
@misc{adalibieke2026adadextrack,
title = {{AdaDexTrack: Dynamic Modulation for Adaptive and Generalizable Dexterous Manipulation Tracking}},
author = {Jianibieke Adalibieke and Qianwei Han and Xueyi Liu and Yuzhe Qin and Li Yi},
year = {2026},
eprint = {2610.33852},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.33852},
}