@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},
}
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。
1M+ trajectories from 22 robot types pooled by 30+ labs; RT-X models trained on it beat single-robot baselines.30 多个实验室汇集 22 种机器人的 100 多万条轨迹；在其上训练的 RT-X 优于单机器人基线。
@misc{collaboration2023open,
title = {{Open X-Embodiment: Robotic Learning Datasets and RT-X Models}},
author = {Open X-Embodiment Collaboration and Abby O'Neill and Abdul Rehman and Abhinav Gupta and Abhiram Maddukuri and Abhishek Gupta and Abhishek Padalkar and Abraham Lee and Acorn Pooley and Agrim Gupta and Ajay Mandlekar and Ajinkya Jain and Albert Tung and Alex Bewley and Alex Herzog and Alex Irpan and Alexander Khazatsky and Anant Rai and Anchit Gupta and Andrew Wang and Andrey Kolobov and Anikait Singh and Animesh Garg and Aniruddha Kembhavi and Annie Xie and Anthony Brohan and Antonin Raffin and Archit Sharma and Arefeh Yavary and Arhan Jain and Ashwin Balakrishna and Ayzaan Wahid and Ben Burgess-Limerick and Beomjoon Kim and Bernhard Schölkopf and Blake Wulfe and Brian Ichter and Cewu Lu and Charles Xu and Charlotte Le and Chelsea Finn and Chen Wang and Chenfeng Xu and Cheng Chi and Chenguang Huang and Christine Chan and Christopher Agia and Chuer Pan and Chuyuan Fu and Coline Devin and others},
year = {2023},
eprint = {2310.08864},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2310.08864},
}
@misc{liu2023libero,
title = {{LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning}},
author = {Bo Liu and Yifeng Zhu and Chongkai Gao and Yihao Feng and Qiang Liu and Yuke Zhu and Peter Stone},
year = {2023},
eprint = {2306.03310},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2306.03310},
}
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},
}
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。
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},
}
Simulator with embodied NPCs, dialogue and event-driven task states, plus a skill harness for frozen MLLMs; its 360-episode bench reports general MLLMs can beat embodied ones; task updates and HRI stay hard.带具身 NPC、受限对话和事件驱动任务状态机的物理仿真框架，并提供让冻结多模态大模型调用多级技能的执行框架；基于 30 个室内场景构建 360 回合基准，评测 8 个模型，报告通用模型可胜过具身专用模型，任务变更和人机协作仍是难点。
@misc{xu2026tarsphere,
title = {{TARSphere: A Robot-Oriented Full-Stack Framework for Long-Horizon Embodied Agents in Evolving Physical and Social Environments}},
author = {Yicheng Xu and Zhikai Xu and Ziyuan Chen and Yuhang Dong and Tao Zhang and Zhucun Xue and Yong Liu and Jiangning Zhang},
year = {2026},
eprint = {2610.30043},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.30043},
}
200K+ navigation-planning samples annotated from camera trajectories up to instructions; RL post-training inside a video world model, used as a cheap stand-in environment, reportedly helps on unseen scenes.包含 20 万余条导航规划样本的数据集，场景多样，标注从相机运动轨迹到高层语义指令；并把视频世界模型当作低成本交互环境做强化学习后训练，报告称在未见场景上有明显提升。
@misc{feng2026embodiedworld,
title = {{EmbodiedWorld-200K: Toward Embodied World Agents via Embodied-Planning Dataset and Interactive World Models}},
author = {X. Feng and J. Tang and Z. Lin and L. Chen and S. Shao and Q. Lu and K. Huang},
year = {2026},
eprint = {2610.25959},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2610.25959},
}
PhysRSI-eval scores VLM agents on 18 manipulation tasks under a fixed trial budget on five axes, incl. learning from failure; reports similar benchmark averages hide different improvement speeds.PhysRSI-eval 在固定交互预算下用 18 个操作任务、五个维度（含从失败中改进）评测 VLM 智能体；报告公开基准平均分相近的模型改进速度差别很大，且早期物理反馈可用于预测后续进展。
@misc{li2026where,
title = {{Where is Physical RSI capability from? An Observational Scaling Law of LLM-as-policy}},
author = {Zekai Li and Jiayi Yu and Jinlu Zhang and Luyang Niu and Zekun Qi and Jun Zheng and Xuchuan Chen and Dairu Liu and Yintianrun Zhang and He Wang and Li Yi},
year = {2026},
eprint = {2610.26848},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.26848},
}
Packages VLA policies, perception models and know-how as 250 reusable "embodied skills" and measures how much they help LLM agents on physical tasks.将 VLA 策略、感知模型与经验知识封装成 250 个可复用的“具身技能”，评测它们对大模型智能体完成物理任务的帮助。
@misc{han2026embodied,
title = {{Embodied Skill Bench: Benchmarking How Well Agents with Embodied Skills Perform on Physical Tasks}},
author = {Zhi Han and Zihan Guo and Chenxi Zeng and Zicai Cui and Yue Xin and Ming Zhou and Dongzhan Zhou and Weinan Zhang and Yang Li},
year = {2026},
eprint = {2610.16238},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.16238},
}
VLA robustness benchmark on RoboCasa365: paired clean/perturbed rollouts differing in one shift, plus stage-level diagnostics of where long-horizon failures start; evaluated on GR00T N1.5.基于 RoboCasa365 的 VLA 鲁棒性诊断基准：成对比较只差一个分布偏移因素的干净/扰动执行，并定位长时序任务中失败从哪个阶段开始，而不只看总成功率；在 GR00T N1.5 上评测。
@misc{wang2026robocasa,
title = {{RoboCasa-Shift: Stage-Aware Robustness Evaluation of Vision-Language-Action Policies in Long-Horizon Household Manipulation}},
author = {Haocheng Wang and Youquan Liu and Weiyu Guo and Aokai Zhang and Donghang Wu and Hongyu Liu and Sha Zhang and Weidong Yang and Ben Fei},
year = {2026},
eprint = {2610.27861},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.27861},
}
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},
}
The open data engine behind GR00T training: models that write timestamped action captions, dense process rewards and episode outcome rewards for human and robot video; reports SOTA on WGO-Bench, RoboReward and Robometer.GR00T 训练所用的开放数据引擎：一组模型为人类与机器人视频生成带时间戳的动作描述、稠密过程奖励和整段结果奖励；报告在 WGO-Bench、RoboReward 与 Robometer 上达到最优。
@misc{leng2026flora,
title = {{FLORA: An Open Model Suite for Scaling Robotics Data Engines}},
author = {Sicong Leng and Yunze Man and Zhiqi Li and Jing Wang and Yuhe Zhang and Ryan Punamiya and Siddharth Patki and Kaiyuan Zheng and Zi-ang Cao and Zhiding Yu and Sifei Liu and Danfei Xu and Ben Levin and Jan Kautz and Yuke Zhu and Johan Bjorck and Linxi "Jim" Fan},
year = {2026},
eprint = {2610.31779},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31779},
}
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},
}
Benchmark of 410 long-horizon task environments (manipulation, puzzles, household) for foundation-model robot agents, with a curated EP-100 split and a shared protocol checking goals, rules and physical validity.面向基础模型机器人智能体的长程任务基准：410 个任务环境（基础操作、序列谜题、多阶段家务），精选 EP-100 子集，统一协议同时检查目标完成、任务规则与物理有效性。
@misc{chen2026embodied,
title = {{Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning}},
author = {Feng Chen and Pei Zhou and Lixuan He and Yunchao Yao and Li Sun and Shenghua Gao and Mingyu Ding and Kaifeng Zhang and Kaichen Zhou and Yi Ma},
year = {2026},
eprint = {2610.27039},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.27039},
}
BEHAVIOR-Spatial, built on BEHAVIOR-1K: spatial QA, point grounding and robot action tests. A baseline policy drops from 41.6% on held-out layouts to 16.1% when the requested target changes; oracle target cues help most.基于 BEHAVIOR-1K 的 BEHAVIOR-Spatial 基准：包含空间问答、指点定位与机器人动作评测。基线策略在未见布局上成功率 41.6%，但在同一布局中更换目标物体指令时降至 16.1%；提供真值目标提示带来的提升最大。
@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},
}
Benchmark where a robot must run physics experiments to find an unknown answer: 30 experiments, 5 disciplines, parameters resampled per instance. Reports even the best frontier agent succeeds under half the time.“为求知而行动”的基准：机器人在物理仿真实验室中自行操作仪器、读数，测出每次随机重采样的未知参数。共 30 个实验、5 个学科；报告最强的前沿模型智能体在原生控制下成功率也不到一半，失败多源于与仪器交互。
@misc{gao2026embodiedlab,
title = {{EmbodiedLab: Benchmarking Embodied Agents That Act to Know}},
author = {Hongcheng Gao and Zelin Zheng and Alex Zhou and Jay Zhu and Shijia Ge and Yazhe Wang and Xiping Li and Jianshu Zeng and Di Wu and Xuan Shangguan and Yexing Wan and Lingyu He and Yongli Wang and Xiaohan Yu and Zhiqi Jia and Yue Liu and Bryan Hooi and Yinpeng Dong and Wentao Zhang and Sihang Wu and Xiao He},
year = {2026},
eprint = {2610.35385},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35385},
}
@misc{mao2026robotagentbench,
title = {{RobotAgentBench: Towards Open-world Robot Autonomy}},
author = {Lingjun Mao and Jinglin Cao and Lukun He and Wenpeng Xu and Yuchen Yan and Hengyu Jin and Zijun Zhang and Jinzhou Tang and Songyao Jin and Ziche Liu and Yifei Shao and Junbo Huang and Fang Nan and Ruobing Han and Ziqiao Xi and Ziming Xu and Shuang Liang and Sibo Zhu and Wenyi Wu and Xinyue Wang and Yiming Huang and Xinkai Zou and Gao Zhu and Kun Zhou and Biwei Huang},
year = {2026},
eprint = {2610.34649},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34649},
}
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%。
Benchmark of 800 human-rated robot/hand videos from 10 generators to test MLLM judges on physical adherence and instruction alignment, plus a 12K-video training set and RoboJudge-9B (Pearson 0.719 with humans).评测多模态大模型能否评判具身视频生成：800 条人工标注视频（10 个生成模型，夹爪/人手/灵巧手），考察物理合理性与指令一致性；另提供 1.2 万条训练视频及 RoboJudge-9B（与人类评分 Pearson 0.719）。
@misc{ma2026robojudge,
title = {{RoboJudge: Multimodal Language Models as Judges for Embodied Video Generation}},
author = {Siyuan Ma and Yu Qi and Cheng Ye and Ruotian Peng and Xinyi Xu and Zitiantao Lin and Zedong Cai and Yijian Huang and Weiyang Liu and Siyuan Wang and Yandong Wen},
year = {2026},
eprint = {2610.27782},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2610.27782},
}
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 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},
}
~500 h / 100K trajectories on one mobile bimanual robot (R1 Lite) across 50 real scenes, plus G0: a VLM planner over a flow-matching VLA; reports that single-embodiment pretraining matters most for few-shot transfer.在单一移动双臂机器人（R1 Lite）上采集约 500 小时、10 万条轨迹、覆盖 50 个真实场景，并提出 G0：VLM 规划器 + 流匹配 VLA 执行器的双系统模型；论文报告单本体预训练对少样本迁移最关键。
@misc{jiang2025galaxea,
title = {{Galaxea Open-World Dataset and G0 Dual-System VLA Model}},
author = {Tao Jiang and Tianyuan Yuan and Yicheng Liu and Chenhao Lu and Jianning Cui and Xiao Liu and Shuiqi Cheng and Jiyang Gao and Huazhe Xu and Hang Zhao},
year = {2025},
eprint = {2509.00576},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2509.00576},
}
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},
}
A million-scale real-robot dataset collected in purpose-built scenes, with the GO-1 generalist policy trained on it.在专门搭建的场景中采集的百万级真实机器人数据集，并基于其训练通用策略 GO-1。
@misc{agibotworldcontributors2025agibot,
title = {{AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems}},
author = {AgiBot-World-Contributors and Qingwen Bu and Jisong Cai and Li Chen and Xiuqi Cui and Yan Ding and Siyuan Feng and Shenyuan Gao and Xindong He and Xuan Hu and Xu Huang and Shu Jiang and Yuxin Jiang and Cheng Jing and Hongyang Li and Jialu Li and Chiming Liu and Yi Liu and Yuxiang Lu and Jianlan Luo and Ping Luo and Yao Mu and Yuehan Niu and Yixuan Pan and Jiangmiao Pang and Yu Qiao and Guanghui Ren and Cheng Ruan and Jiaqi Shan and Yongjian Shen and Chengshi Shi and Mingkang Shi and Modi Shi and Chonghao Sima and Jianheng Song and Huijie Wang and Wenhao Wang and Dafeng Wei and Chengen Xie and Guo Xu and Junchi Yan and Cunbiao Yang and Lei Yang and Shukai Yang and Maoqing Yao and Jia Zeng and Chi Zhang and Qinglin Zhang and Bin Zhao and Chengyue Zhao and others},
year = {2025},
eprint = {2503.06669},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2503.06669},
}
MetaSim puts many simulators (Isaac Sim/Gym, MuJoCo, Genesis, SAPIEN…) behind one interface; adds a large synthetic dataset migrated from public benchmarks and unified IL/RL benchmarks.MetaSim 用统一接口封装多种仿真器（Isaac Sim/Gym、MuJoCo、Genesis、SAPIEN、PyBullet 等）；提供由公开基准迁移而来的大规模合成数据集，以及按泛化层级划分的统一模仿学习/强化学习基准。
@misc{geng2025roboverse,
title = {{RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning}},
author = {Haoran Geng and Feishi Wang and Songlin Wei and Yuyang Li and Bangjun Wang and Boshi An and Charlie Tianyue Cheng and Haozhe Lou and Peihao Li and Yen-Jen Wang and Yutong Liang and Dylan Goetting and Chaoyi Xu and Haozhe Chen and Yuxi Qian and Yiran Geng and Jiageng Mao and Weikang Wan and Mingtong Zhang and Jiangran Lyu and Siheng Zhao and Jiazhao Zhang and Jialiang Zhang and Chengyang Zhao and Haoran Lu and Yufei Ding and Ran Gong and Yuran Wang and Yuxuan Kuang and Ruihai Wu and Baoxiong Jia and Carlo Sferrazza and Hao Dong and Siyuan Huang and Yue Wang and Jitendra Malik and Pieter Abbeel},
year = {2025},
eprint = {2504.18904},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2504.18904},
}
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},
}
TVL pairs tactile and visual observations with language descriptions and trains a touch-vision-language encoder plus a model that describes how things feel.TVL 数据集将触觉与视觉观测配上语言描述，并训练触觉-视觉-语言编码器以及能描述物体触感的模型。
@misc{fu2024touch,
title = {{A Touch, Vision, and Language Dataset for Multimodal Alignment}},
author = {Letian Fu and Gaurav Datta and Huang Huang and William Chung-Ho Panitch and Jaimyn Drake and Joseph Ortiz and Mustafa Mukadam and Mike Lambeta and Roberto Calandra and Ken Goldberg},
year = {2024},
eprint = {2402.13232},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2402.13232},
}
120 kitchen scenes, 2,500+ objects and 100 tasks with generated demonstrations for training generalist policies.120 个厨房场景、2500 多个物体与 100 个任务，并附生成演示，用于训练通用策略。
@misc{nasiriany2024robocasa,
title = {{RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots}},
author = {Soroush Nasiriany and Abhiram Maddukuri and Lance Zhang and Adeet Parikh and Aaron Lo and Abhishek Joshi and Ajay Mandlekar and Yuke Zhu},
year = {2024},
eprint = {2406.02523},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2406.02523},
}
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{khazatsky2024droid,
title = {{DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset}},
author = {Alexander Khazatsky and Karl Pertsch and Suraj Nair and Ashwin Balakrishna and Sudeep Dasari and Siddharth Karamcheti and Soroush Nasiriany and Mohan Kumar Srirama and Lawrence Yunliang Chen and Kirsty Ellis and Peter David Fagan and Joey Hejna and Masha Itkina and Marion Lepert and Yecheng Jason Ma and Patrick Tree Miller and Jimmy Wu and Suneel Belkhale and Shivin Dass and Huy Ha and Arhan Jain and Abraham Lee and Youngwoon Lee and Marius Memmel and Sungjae Park and Ilija Radosavovic and Kaiyuan Wang and Albert Zhan and Kevin Black and Cheng Chi and Kyle Beltran Hatch and Shan Lin and Jingpei Lu and Jean Mercat and Abdul Rehman and Pannag R Sanketi and Archit Sharma and Cody Simpson and Quan Vuong and Homer Rich Walke and Blake Wulfe and Ted Xiao and Jonathan Heewon Yang and Arefeh Yavary and Tony Z. Zhao and Christopher Agia and Rohan Baijal and Mateo Guaman Castro and Daphne Chen and Qiuyu Chen and others},
year = {2024},
eprint = {2403.12945},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.12945},
}
@misc{li2024evaluating,
title = {{Evaluating Real-World Robot Manipulation Policies in Simulation}},
author = {Xuanlin Li and Kyle Hsu and Jiayuan Gu and Karl Pertsch and Oier Mees and Homer Rich Walke and Chuyuan Fu and Ishikaa Lunawat and Isabel Sieh and Sean Kirmani and Sergey Levine and Jiajun Wu and Chelsea Finn and Hao Su and Quan Vuong and Ted Xiao},
year = {2024},
eprint = {2405.05941},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2405.05941},
}
Large real-robot teleoperation dataset across several embodiments (single-arm, dual-arm, humanoid) collected under one standardized protocol, and it keeps failed demos with annotated causes.在统一规范下采集的大规模真机遥操作数据集，覆盖单臂、双臂与人形等多种本体，并保留了标注失败原因的失败演示。
@misc{wu2024robomind,
title = {{RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation}},
author = {Kun Wu and Chengkai Hou and Jiaming Liu and Zhengping Che and Xiaozhu Ju and Zhuqin Yang and Meng Li and Yinuo Zhao and Zhiyuan Xu and Guang Yang and Shichao Fan and Xinhua Wang and Fei Liao and Zhen Zhao and Guangyu Li and Zhao Jin and Lecheng Wang and Jilei Mao and Ning Liu and Pei Ren and Qiang Zhang and Yaoxu Lyu and Mengzhen Liu and Jingyang He and Yulin Luo and Zeyu Gao and Chenxuan Li and Chenyang Gu and Yankai Fu and Di Wu and Xingyu Wang and Sixiang Chen and Zhenyu Wang and Pengju An and Siyuan Qian and Shanghang Zhang and Jian Tang},
year = {2024},
eprint = {2412.13877},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.13877},
}
27 simulated tasks (12 locomotion, 15 whole-body manipulation) on a Unitree H1 with two dexterous hands; reports that DreamerV3, TD-MPC2, SAC and PPO fail most of them, especially long-horizon whole-body ones.27 个仿真任务（12 个运动、15 个全身操作），机器人为带两只灵巧手的 Unitree H1；报告 DreamerV3、TD-MPC2、SAC、PPO 在大多数任务上未达标，长时程全身协调任务尤甚。
@misc{sferrazza2024humanoidbench,
title = {{HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation}},
author = {Carmelo Sferrazza and Dun-Ming Huang and Xingyu Lin and Youngwoon Lee and Pieter Abbeel},
year = {2024},
eprint = {2403.10506},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.10506},
}
@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},
}
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},
}
@misc{walke2023bridgedata,
title = {{BridgeData V2: A Dataset for Robot Learning at Scale}},
author = {Homer Walke and Kevin Black and Abraham Lee and Moo Jin Kim and Max Du and Chongyi Zheng and Tony Zhao and Philippe Hansen-Estruch and Quan Vuong and Andre He and Vivek Myers and Kuan Fang and Chelsea Finn and Sergey Levine},
year = {2023},
eprint = {2308.12952},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2308.12952},
}
@misc{fang2023rh,
title = {{RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot}},
author = {Hao-Shu Fang and Hongjie Fang and Zhenyu Tang and Jirong Liu and Chenxi Wang and Junbo Wang and Haoyi Zhu and Cewu Lu},
year = {2023},
eprint = {2307.00595},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2307.00595},
}
Time-synced first-person and third-person video of people performing skilled activities (cooking, sports, repair...), with rich annotations; a large source for learning manipulation skills from humans.人们进行技能活动（烹饪、运动、维修等）时同步采集的第一人称与第三人称视频，附丰富标注，是从人类视频学习操作技能的重要数据来源。
@misc{grauman2023ego,
title = {{Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives}},
author = {Kristen Grauman and Andrew Westbury and Lorenzo Torresani and Kris Kitani and Jitendra Malik and Triantafyllos Afouras and Kumar Ashutosh and Vijay Baiyya and Siddhant Bansal and Bikram Boote and Eugene Byrne and Zach Chavis and Joya Chen and Feng Cheng and Fu-Jen Chu and Sean Crane and Avijit Dasgupta and Jing Dong and Maria Escobar and Cristhian Forigua and Abrham Gebreselasie and Sanjay Haresh and Jing Huang and Md Mohaiminul Islam and Suyog Jain and Rawal Khirodkar and Devansh Kukreja and Kevin J Liang and Jia-Wei Liu and Sagnik Majumder and Yongsen Mao and Miguel Martin and Effrosyni Mavroudi and Tushar Nagarajan and Francesco Ragusa and Santhosh Kumar Ramakrishnan and Luigi Seminara and Arjun Somayazulu and Yale Song and Shan Su and Zihui Xue and Edward Zhang and Jinxu Zhang and Angela Castillo and Changan Chen and Xinzhu Fu and Ryosuke Furuta and Cristina Gonzalez and Prince Gupta and Jiabo Hu and others},
year = {2023},
eprint = {2311.18259},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2311.18259},
}
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},
}
1,000 household activities chosen from a survey of what people want robots to do, defined in a logic language and simulated in OmniGibson with realistic physics and rendering.依据人们希望机器人做什么的调查挑选出 1000 项家务活动，用逻辑语言定义，并在具备真实物理与渲染的 OmniGibson 中仿真。
@misc{li2024behavior,
title = {{BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation}},
author = {Chengshu Li and Ruohan Zhang and Josiah Wong and Cem Gokmen and Sanjana Srivastava and Roberto Martín-Martín and Chen Wang and Gabrael Levine and Wensi Ai and Benjamin Martinez and Hang Yin and Michael Lingelbach and Minjune Hwang and Ayano Hiranaka and Sujay Garlanka and Arman Aydin and Sharon Lee and Jiankai Sun and Mona Anvari and Manasi Sharma and Dhruva Bansal and Samuel Hunter and Kyu-Young Kim and Alan Lou and Caleb R Matthews and Ivan Villa-Renteria and Jerry Huayang Tang and Claire Tang and Fei Xia and Yunzhu Li and Silvio Savarese and Hyowon Gweon and C. Karen Liu and Jiajun Wu and Li Fei-Fei},
year = {2024},
eprint = {2403.09227},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2403.09227},
}
800K+ annotated 3D object models; widely used to populate simulators with diverse objects for grasping, rearrangement and procedural scene generation (Objaverse-XL extends it to 10M+).80 万+ 带标注的 3D 物体模型，常用于为仿真器填充多样物体，用于抓取、重排和程序化场景生成（Objaverse-XL 扩展到 1000 万+）。
@misc{deitke2022objaverse,
title = {{Objaverse: A Universe of Annotated 3D Objects}},
author = {Matt Deitke and Dustin Schwenk and Jordi Salvador and Luca Weihs and Oscar Michel and Eli VanderBilt and Ludwig Schmidt and Kiana Ehsani and Aniruddha Kembhavi and Ali Farhadi},
year = {2022},
eprint = {2212.08051},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2212.08051},
}
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},
}
Gym-style benchmark with cart-pole and quadrotor tasks, constraints and disturbances for comparing safe control and safe RL methods.Gym 风格基准，含倒立摆与四旋翼任务、约束与扰动，用于比较安全控制与安全强化学习方法。
@misc{yuan2021safe,
title = {{safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics}},
author = {Zhaocong Yuan and Adam W. Hall and Siqi Zhou and Lukas Brunke and Melissa Greeff and Jacopo Panerati and Angela P. Schoellig},
year = {2021},
eprint = {2109.06325},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2109.06325},
}
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},
}
@misc{grauman2021ego,
title = {{Ego4D: Around the World in 3,000 Hours of Egocentric Video}},
author = {Kristen Grauman and Andrew Westbury and Eugene Byrne and Zachary Chavis and Antonino Furnari and Rohit Girdhar and Jackson Hamburger and Hao Jiang and Miao Liu and Xingyu Liu and Miguel Martin and Tushar Nagarajan and Ilija Radosavovic and Santhosh Kumar Ramakrishnan and Fiona Ryan and Jayant Sharma and Michael Wray and Mengmeng Xu and Eric Zhongcong Xu and Chen Zhao and Siddhant Bansal and Dhruv Batra and Vincent Cartillier and Sean Crane and Tien Do and Morrie Doulaty and Akshay Erapalli and Christoph Feichtenhofer and Adriano Fragomeni and Qichen Fu and Abrham Gebreselasie and Cristina Gonzalez and James Hillis and Xuhua Huang and Yifei Huang and Wenqi Jia and Weslie Khoo and Jachym Kolar and Satwik Kottur and Anurag Kumar and Federico Landini and Chao Li and Yanghao Li and Zhenqiang Li and Karttikeya Mangalam and Raghava Modhugu and Jonathan Munro and Tullie Murrell and Takumi Nishiyasu and Will Price and others},
year = {2021},
eprint = {2110.07058},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2110.07058},
}
@misc{mees2021calvin,
title = {{CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks}},
author = {Oier Mees and Lukas Hermann and Erick Rosete-Beas and Wolfram Burgard},
year = {2021},
eprint = {2112.03227},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2112.03227},
}
1,000 3D scans of real indoor spaces for Habitat; the main scene set behind ObjectNav and many embodied navigation benchmarks.1000 个真实室内空间的 3D 扫描，供 Habitat 使用；ObjectNav 等众多具身导航基准的主要场景集。
@misc{ramakrishnan2021habitat,
title = {{Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI}},
author = {Santhosh K. Ramakrishnan and Aaron Gokaslan and Erik Wijmans and Oleksandr Maksymets and Alex Clegg and John Turner and Eric Undersander and Wojciech Galuba and Andrew Westbury and Angel X. Chang and Manolis Savva and Yili Zhao and Dhruv Batra},
year = {2021},
eprint = {2109.08238},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2109.08238},
}
@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},
}
VLN-CE moves Room-to-Room instructions from a fixed navigation graph into continuous Habitat scenes with low-level actions; the standard setting for realistic VLN.VLN-CE 把 Room-to-Room 指令从固定导航图搬到 Habitat 连续环境中、使用底层动作执行，是更贴近真实的 VLN 标准设定。
@misc{krantz2020beyond,
title = {{Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments}},
author = {Jacob Krantz and Erik Wijmans and Arjun Majumdar and Dhruv Batra and Stefan Lee},
year = {2020},
eprint = {2004.02857},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2004.02857},
}
@misc{zhu2020robosuite,
title = {{robosuite: A Modular Simulation Framework and Benchmark for Robot Learning}},
author = {Yuke Zhu and Josiah Wong and Ajay Mandlekar and Roberto Martín-Martín and Abhishek Joshi and Kevin Lin and Abhiram Maddukuri and Soroush Nasiriany and Yifeng Zhu},
year = {2020},
eprint = {2009.12293},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2009.12293},
}
EPIC-KITCHENS-100 — 100 hours (20M frames, ~90K action segments) of unscripted first-person kitchen video from 45 environments, with dense action labels and challenges such as action anticipation.EPIC-KITCHENS-100：来自 45 个厨房环境、100 小时（2000 万帧、约 9 万个动作片段）的非脚本第一视角视频，动作标注密集，并设有动作预测等挑战任务。
@misc{damen2020rescaling,
title = {{Rescaling Egocentric Vision}},
author = {Dima Damen and Hazel Doughty and Giovanni Maria Farinella and Antonino Furnari and Evangelos Kazakos and Jian Ma and Davide Moltisanti and Jonathan Munro and Toby Perrett and Will Price and Michael Wray},
year = {2020},
eprint = {2006.13256},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2006.13256},
}
@misc{james2019rlbench,
title = {{RLBench: The Robot Learning Benchmark \& Learning Environment}},
author = {Stephen James and Zicong Ma and David Rovick Arrojo and Andrew J. Davison},
year = {2019},
eprint = {1909.12271},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1909.12271},
}
@misc{yu2019meta,
title = {{Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning}},
author = {Tianhe Yu and Deirdre Quillen and Zhanpeng He and Ryan Julian and Avnish Narayan and Hayden Shively and Adithya Bellathur and Karol Hausman and Chelsea Finn and Sergey Levine},
year = {2019},
eprint = {1910.10897},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/1910.10897},
}
@misc{savva2019habitat,
title = {{Habitat: A Platform for Embodied AI Research}},
author = {Manolis Savva and Abhishek Kadian and Oleksandr Maksymets and Yili Zhao and Erik Wijmans and Bhavana Jain and Julian Straub and Jia Liu and Vladlen Koltun and Jitendra Malik and Devi Parikh and Dhruv Batra},
year = {2019},
eprint = {1904.01201},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/1904.01201},
}
An early shared dataset of robot interaction videos from seven robot platforms, used to pretrain visual dynamics models that transfer to new robots - a forerunner of Open X-Embodiment.早期的共享机器人交互视频数据集，涵盖七种机器人平台，用于预训练可迁移到新机器人的视觉动力学模型，是 Open X-Embodiment 的先驱。
@misc{dasari2019robonet,
title = {{RoboNet: Large-Scale Multi-Robot Learning}},
author = {Sudeep Dasari and Frederik Ebert and Stephen Tian and Suraj Nair and Bernadette Bucher and Karl Schmeckpeper and Siddharth Singh and Sergey Levine and Chelsea Finn},
year = {2019},
eprint = {1910.11215},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1910.11215},
}
@misc{anderson2017vision,
title = {{Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments}},
author = {Peter Anderson and Qi Wu and Damien Teney and Jake Bruce and Mark Johnson and Niko Sünderhauf and Ian Reid and Stephen Gould and Anton van den Hengel},
year = {2017},
eprint = {1711.07280},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/1711.07280},
}
RGB-D scans of 1,500+ indoor scenes with 3D reconstructions, camera poses and semantic labels; the standard benchmark for 3D scene understanding that embodied perception builds on.1500+ 个室内场景的 RGB-D 扫描，含三维重建、相机位姿与语义标注；具身感知所依赖的三维场景理解标准基准。
@misc{dai2017scannet,
title = {{ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes}},
author = {Angela Dai and Angel X. Chang and Manolis Savva and Maciej Halber and Thomas Funkhouser and Matthias Nießner},
year = {2017},
eprint = {1702.04405},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/1702.04405},
}
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},
}
Tudor Braicu, Joseph DelPreto, Jared Boyer, M\'onika Farsang et al.
Abstract摘要
Agile robots require spatiotemporal intelligence to interpret scenes, reason about interactions, and anticipate future events. Yet it remains unclear how well foundation vision models support these capabilities, particularly from a moving egocentric camera, which is a natural view for robots. To investigate this, we collect an annotated tennis dataset and train a suite of vision models on spatiotemporal tasks. Our dataset contains egocentric video synchronized with five allocentric videos, estimated global positions, labeled contact times, and annotated stroke types. We then evaluate seven …
@misc{braicu2026perceiving,
title = {{Perceiving and Predicting Dynamic Sports: Spatiotemporal Intelligence in Vision Models for Egocentric Tennis Video}},
author = {Tudor Braicu and Joseph DelPreto and Jared Boyer and M\'onika Farsang and Maximilian St\"olzle and Tim Niklas Seyde and Daniela Rus},
year = {2026},
eprint = {2610.37619},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.37619},
}
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 …
Visual Place Recognition (VPR) is an important means for loop closure detection in visual Simultaneous Localization and Mapping (SLAM). While state-of-the-art VPR and SLAM approaches excel in urban and feature-rich natural environments, the vast and feature-poor environments of extraterrestrial surfaces like on the Moon or Mars pose severe challenges for existing approaches. This paper presents a novel benchmark, VPR-Space, to evaluate VPR capabilities on extraterrestrial surfaces. There are three contributions: (1) We collected sixteen VPR database-query datasets based on existing data from …
@misc{fechner2026vpr,
title = {{VPR-Space: A Benchmark for Visual Place Recognition in Space Analog Domains}},
author = {Richard Fechner and Peer Neubert},
year = {2026},
eprint = {2610.35642},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35642},
}
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},
}
Hongcheng Gao, Zelin Zheng, Alex Zhou, Jay Zhu et al.
Abstract摘要
Embodied agents are expected to accomplish real-world goals by deciding for themselves how and when to interact with their surroundings. For many such goals, changing the physical state of the world is the means rather than the end: the agent changes the state of a system through interaction, observes the feedback, and explores until it either achieves the goal itself (e.g., discovering physical laws) or learns what the goal requires; it acts to know. Manipulation benchmarks stop at acting to change, with the target state given; scientific discovery benchmarks evaluate knowing but replace …
@misc{gao2026embodiedlab,
title = {{EmbodiedLab: Benchmarking Embodied Agents That Act to Know}},
author = {Hongcheng Gao and Zelin Zheng and Alex Zhou and Jay Zhu and Shijia Ge and Yazhe Wang and Xiping Li and Jianshu Zeng and others},
year = {2026},
eprint = {2610.35385},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.35385},
}
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},
}
Hyunwoong Bae, Jisu Han, Jiyeon Choi, Dohoon Kim et al.
Abstract摘要
Safe household navigation requires robots to regulate clearance and speed according to what an obstacle is and how close it is, without unnecessary conservatism. Yet existing evaluations do not test whether semantic safety distinctions are reflected in continuous motion. To that end, we introduce OSMA-Bench, an Obstacle-Conditioned Safe Motion Adaptation Benchmark built on spatially matched scenario sets that differ only in obstacle type. We also propose the \emph{Semantic Sensitivity Index} (SSI), which measures whether clearance and distance-dependent speed and smoothness follow the caution …
@misc{bae2026benchmarking,
title = {{Benchmarking Obstacle-Conditioned Safety in Household Robot Navigation}},
author = {Hyunwoong Bae and Jisu Han and Jiyeon Choi and Dohoon Kim and Taesup Moon},
year = {2026},
eprint = {2610.34674},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.34674},
}
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},
}
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},
}
Doanh Le Thien, Viet-Thanh Nguyen, Pham Tri Quang, Nhi Pham et al.
Abstract摘要
Video world models (VWMs) are rapidly emerging as scalable neural simulators for robotic manipulation. However, most benchmarks only evaluate them as open-world video generators, using Vision-Language Models (VLMs) that lack strict 3D spatial and kinematic reasoning. To bridge this gap, we introduce RoboWorM, a comprehensive, deployment-aware benchmark that leverages known hardware priors such as robot kinematic trees and fixed camera configurations, to systematically examine structural and spatiotemporal fidelity. RoboWorM curates 1,605 episodes across single-arm, bimanual, and humanoid …
@misc{thien2026roboworm,
title = {{RoboWorM: Evaluating Movement Realism, Consistency and Acceleration Affects in Embodied Video World Models}},
author = {Doanh Le Thien and Viet-Thanh Nguyen and Pham Tri Quang and Nhi Pham and Christopher Wewer and Minh-Loi Nguyen and Hoang H. Tran and Tho Le and others},
year = {2026},
eprint = {2610.33807},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.33807},
}
Robust reinforcement learning (RL) is critical for reliable real-world deployment, where policies may fail under diverse shifts and disruptions. Existing robust RL algorithms offer a rich set of mechanisms and principles, yet two major barriers limit their broader potential: limited algorithm reusability and an unclear empirical capability boundary. We introduce RobustRLlib, an algorithm-centric library and benchmark that addresses both challenges. It integrates 17 representative robust online, offline, and safe RL algorithms under a unified interface for substantially improved reusability, …
@misc{chen2026robustrllib,
title = {{RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms}},
author = {Xi Chen and Fengqi Hu and Zeyu Shen and Jiaying Xu and Shangding Gu and Pan Xu and Anqi Liu and Laixi Shi},
year = {2026},
eprint = {2610.32971},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.32971},
}
Amber Xie, Jiawei Fu, Xiaofeng Guo, Carmelo Sferrazza et al.
Abstract摘要
Collecting high-quality demonstrations remains a central bottleneck for learning dexterous manipulation. Teleoperation records executable robot actions, but it requires a complete robot setup and places the burden of coordinating many finger joints on the operator. Human demonstrations are easier to collect, but their motions must be estimated and retargeted to the robot, and they do not directly provide robot actions. In this paper, we introduce DexPort, a portable data-collection interface for high-DoF robot hands. It uses the same hand hardware for collection and deployment, avoiding a …
@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},
}
Giovanni Minelli, Antonio Langella, Giovanni B. Dessy, Lorenzo Amatucci et al.
Abstract摘要
Vision-language-action (VLA) models are typically pretrained on broad, diverse datasets and subsequently fine-tuned on a comparatively small dataset tailored to a specific target robot. Such adaptation recipe is now widely established, achieving its most mature results on fixed-base arms due to their static base dynamics and consistent reference frame. Mounting the same arm on a quadruped extends the workspace to the entire floor, but fundamentally changes the embodiment: rather than controlling the arm in isolation, the policy must jointly coordinate base navigation, body posture, and …
@misc{minelli2026from,
title = {{From Tabletop to Walking Bot: Deploying a VLA Model on a Legged Manipulator Robot}},
author = {Giovanni Minelli and Antonio Langella and Giovanni B. Dessy and Lorenzo Amatucci and Giulio Turrisi and Victor Barasuol and Claudio Semini},
year = {2026},
eprint = {2610.32271},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.32271},
}
Sicong Leng, Yunze Man, Zhiqi Li, Jing Wang et al.
Abstract摘要
The life-cycle of robotics foundation models crucially depends on data labels. Outcome reward labels are needed for monitoring real-world deployments. Process reward labels are needed during RL post-training. Timestamped captions are needed for filtering and balancing pretraining data. We present Flora, an industrial-quality data engine used for training GR00T models, that simultaneously covers these capabilities. Flora achieves SOTA scores on the captioning benchmark WGO-Bench and reward benchmarks RoboReward and Robometer. On internal benchmarks it is competitive with GPT-6 Astra while …
@misc{leng2026flora,
title = {{FLORA: An Open Model Suite for Scaling Robotics Data Engines}},
author = {Sicong Leng and Yunze Man and Zhiqi Li and Jing Wang and Yuhe Zhang and Ryan Punamiya and Siddharth Patki and Kaiyuan Zheng and others},
year = {2026},
eprint = {2610.31779},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31779},
}
Task and Motion Planning (TAMP) stands as a fundamental challenge in robotics, essential for enabling autonomous agents to execute complex, long-horizon tasks in diverse environments. While Large Language Models (LLMs) introduce a new paradigm for TAMP by leveraging extensive common sense, their inherent reasoning limitations hinder direct application, and fine-tuning strategies to enhance these capabilities are currently bottlenecked by the scarcity of high-quality datasets. Existing automated generation methods often suffer from homogeneity, limiting model generalization, and unsolvability, …
@misc{li2026scope,
title = {{SCOPE: Scene-graph Construction Optimized by Plan Error-correction for Robot Long-term Planning Datasets}},
author = {Chaoyue Li and Zeyu Gao and Yanfeng Lu and Fan Luo},
year = {2026},
eprint = {2610.31518},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31518},
}
Zizhe Wang (Astroup Lab, Beijing, China), Jia Deng (Astroup Lab et al.
Abstract摘要
Capturing a loose object inside a spacecraft requires a robot to approach a target that can translate and rotate, and whose motion changes when contact is made. These conditions motivate evaluation beyond manipulation of supported objects. We introduce AstroBench, an open benchmark for free-floating capture and space robotic manipulation with a fixed-base arm. The task suite combines four initial-motion tracks with sample-storage operations based on MELFI hardware and cap and captive-screw servicing based on the Robotic Refueling Mission. The two workcells provide nine task definitions and 29 …
@misc{lab2026astrobench,
title = {{AstroBench: Benchmarking Free-Floating Capture and Space Robotic Manipulation}},
author = {Zizhe Wang (Astroup Lab and Beijing and China) and Jia Deng (Astroup Lab and Beijing and China) and Xin Lu (Astroup Lab and Beijing and others},
year = {2026},
eprint = {2610.31064},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.31064},
}
Recent advances in frontier AI models have demonstrated remarkable capabilities in visual understanding, reasoning, planning, and tool use, raising the possibility of using general-purpose AI models for robot manipulation without task-specific training. However, their manipulation capabilities have not been systematically evaluated on standardized robotics benchmarks. In this report, we present a unified closed-loop manipulation framework for evaluating frontier AI models on the LIBERO-10 benchmark. Our framework enables models to control a robot directly through visual observations, robot …
Rangya Zhang, Yujing Shang, Zhuoyuan Yu, Mingsheng Li et al.
Abstract摘要
Large-scale aerial resources provide a foundation for learning navigation across diverse environments. However, existing resources remain limited in providing (1) densely sampled flight demonstrations for learning different policy formulations and (2) broad environment coverage for standardized closed-loop evaluation. To address these limitations, we introduce AirPlayground, a unified benchmark for training and evaluating aerial navigation policies across diverse environments. We first construct 94 photorealistic 3D Gaussian Splatting (3DGS) environments spanning indoor, urban outdoor, and …
@misc{zhang2026airplayground,
title = {{AirPlayground: Benchmarking Aerial Navigation across Diverse Reconstructed Environments}},
author = {Rangya Zhang and Yujing Shang and Zhuoyuan Yu and Mingsheng Li and Qingsong Yan and Chao Yan and Yuhang Zhang and Mir Feroskhan},
year = {2026},
eprint = {2610.29699},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.29699},
}
Charbel Abi Hana (Idealworks), Rayan Zaitouni (Idealworks), Kameel Amareen (Idealworks), Mohamad Mostafa (Idealworks) et al.
Abstract摘要
Classical robot navigation stacks are reliable but often require re-engineering for each new robot type and site. Learning-based navigation shifts this effort from hand-tuned parameters to data generation and training, where a policy adapts to each new embodiment or facility by fine-tuning in simulation. Whether policies adapted this way stay reliable on real industrial robots has seen little systematic testing. We study this for NVIDIA's X-Mobility and COMPASS workflow for training learned navigation policies on Idealworks robots. In simulation, we extend the COMPASS benchmark with two …
@misc{idealworks2026physical,
title = {{Physical AI for Intralogistics: Benchmarking the NVIDIA Sim-to-Real Ecosystem for Cosmos-Enhanced COMPASS Navigation on Industrial Mobile Robots}},
author = {Charbel Abi Hana (Idealworks) and Rayan Zaitouni (Idealworks) and Kameel Amareen (Idealworks) and Mohamad Mostafa (Idealworks) and Hasan Sayour (Idealworks) and Wei Liu (NVIDIA) and Yan Chang (NVIDIA) and Mihir Acharya (NVIDIA) and others},
year = {2026},
eprint = {2610.28432},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.28432},
}
Youmna Farag, Svetlana Stoyanchev, Simon Keizer, Mohan Li et al.
Abstract摘要
Reasoning about safety is essential for embodied agents powered by large language models (LLMs). We introduce the Contextual Action and Plan Safety benchmark (CAPSafe), designed to evaluate safety reasoning at two levels: (1) an action-level dataset that tests whether a single action is safe given its environmental context, and (2) a plan-level dataset that evaluates whether agents can generate safe long-horizon plans for complex hazardous household tasks. CAPSafe models safety conditions where hazards are situational (inferred from object properties), multiple hazards can co-occur within the …
@misc{farag2026capsafe,
title = {{CAPSafe: A Benchmark for Contextual Action and Plan Safety Reasoning in Embodied Agents}},
author = {Youmna Farag and Svetlana Stoyanchev and Simon Keizer and Mohan Li and Rama Doddipatla},
year = {2026},
eprint = {2610.28381},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.28381},
}
Video generation models are promising foundations for embodied world models, but only if their generated rollouts are physically valid and faithful to task instructions. Multimodal large language models (MLLMs) are increasingly used to judge these properties, yet their agreement with human judgments on embodied videos has not been systematically evaluated. To address this gap, we introduce RoboJudge-Bench, a benchmark for evaluating MLLMs as judges of embodied video generation. The benchmark measures two fundamental dimensions, Physical Adherence and Instruction Alignment, across 800 …
@misc{ma2026robojudge,
title = {{RoboJudge: Multimodal Language Models as Judges for Embodied Video Generation}},
author = {Siyuan Ma and Yu Qi and Cheng Ye and Ruotian Peng and Xinyi Xu and Zitiantao Lin and Zedong Cai and Yijian Huang and others},
year = {2026},
eprint = {2610.27782},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.27782},
}
Vision-language-action (VLA) models transfer knowledge from large-scale pretraining into robot control, but their manipulation performance remains sensitive to changes in observation conditions, which recent work addresses by fine-tuning the visual module on target-domain data. However, it remains unclear how such adaptation affects robustness beyond the target condition, as existing robustness benchmarks only evaluate models without fine-tuning and adaptation studies only report the target gain. Measuring this cross-dimension effect requires separating target gains from off-target changes, …
@misc{xu2026vla,
title = {{VLA-Retain: Benchmarking Cross-Dimension Effects of Vision Tuning in VLAs}},
author = {Borui Xu and Han Chen and Bingsheng He and Ming Wu and Jason Zeng and Michael Heinrich},
year = {2026},
eprint = {2610.27698},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2610.27698},
}
Huihua Zhu, Shuyang Zhang, Dongqing Chen, Zhuo Yu et al.
Abstract摘要
Online LiDAR-camera calibration remains a fundamental yet challenging task in the field of robotics. Recently, deep learning-based data-driven approaches have emerged, offering high-precision, targetless calibration capabilities. However, existing methods often suffer from a critical dataset shortcut phenomenon due to the lack of extrinsic diversity in their training data. Networks tend to overfit to fixed nominal extrinsic configurations, leading to severe performance degradation and poor generalization in real-world deployments. To address this limitation, we propose a highly generalizable …
@misc{zhu2026syncalib,
title = {{SynCalib: Breaking Dataset Shortcuts in LiDAR-Camera Calibration via Extrinsic-Controllable Synthesis}},
author = {Huihua Zhu and Shuyang Zhang and Dongqing Chen and Zhuo Yu and Jianhao Jiao and Yilong Zhu and Siyu Teng and Xiaopin Zhong and others},
year = {2026},
eprint = {2610.27547},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.27547},
}
The Jetson AGX Orin is a common computer for robots and vehicles, yet measured accuracy, speed and energy of current depth estimators on it are hard to find. We benchmark nine model families as of October 2026: the monocular Depth Anything V2 and 3, Depth Pro, MoGe-2, Metric3D v2 and UniDepth V2, and the stereo methods libSGM, NVIDIA ESS and Fast-FoundationStereo. Every method runs on the Orin as a TensorRT FP16 engine or native CUDA code at up to three input sizes, up to the full 5.7 MP, and is scored for metric accuracy against Middlebury 2014 ground truth (non-occluded pixels of the 10 …
Feng Chen, Pei Zhou, Lixuan He, Yunchao Yao et al.
Abstract摘要
Foundation models offer a promising basis for robotic agents, but long-horizon tasks require them to sustain coherent reasoning and reliable physical execution across interdependent stages. We introduce Embodied Playground, a benchmark for evaluating how foundation-model agents coordinate physical, strategic, and spatial reasoning throughout such tasks. Agents must track task progress, satisfy intermediate constraints, and revise their plans using execution feedback as earlier actions shape subsequent decisions. The playground comprises 410 task environments spanning basic manipulation, …
@misc{chen2026embodied,
title = {{Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning}},
author = {Feng Chen and Pei Zhou and Lixuan He and Yunchao Yao and Li Sun and Shenghua Gao and Mingyu Ding and Kaifeng Zhang and others},
year = {2026},
eprint = {2610.27039},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.27039},
}
Tianwei Luo, Yiming He, Hongjin Wu, Ting Pan et al.
Abstract摘要
General-purpose manipulation requires reliable behavior across embodiments, objects, and task demands. We introduce HandsOn, a composable simulation platform and benchmark for cross-embodiment dexterous manipulation. It supports five robotic arms, six dexterous hands, and approximately 200 rigid, articulated, and deformable assets in randomized scenes. The benchmark pairs 18,000 curated human demonstrations with 66 human-verified tasks: Core-50 supports demonstration-based learning and execution tests, while Transfer-16 evaluates asset and relation transfer, skill composition, and deformable …
@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 others},
year = {2026},
eprint = {2610.26765},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.26765},
}
Shengbang Liu, Zhengye Du, Zhilong Wan, Honghao Su et al.
Abstract摘要
Robotic value models assess task execution from visual observations and instructions, providing feedback for policy learning and execution monitoring. However, existing evaluations cover only selected aspects of execution understanding, leaving the reliability of value feedback insufficiently tested. We introduce RoboValue, a fine-grained sim-and-real benchmark for unified evaluation of robotic value models. Its framework evaluates four complementary capability dimensions through shared interfaces: task-state understanding, temporal progress monitoring, failure and recovery reasoning, and …
@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 others},
year = {2026},
eprint = {2610.26663},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.26663},
}
This article presents an analytical-DES benchmark for intra-aisle robotic picking in automated warehousing. The investigated cycle combines horizontal shuttle travel, tote handling and item picking at storage locations. Simulation results are compared with the analytical formulation of Lerher et al. under the same rack geometry, shuttle-motion, handling and picking conditions, and the same definitions of cycle time and derived item throughput. Reproducing the published analytical tables requires the distance interpretation dES = 1.5 m and dTB = 0 m. The analytical-DES benchmark comprises 180 …
@misc{moursi2026reconstruction,
title = {{Reconstruction and Discrete-Event Simulation Assessment of an Analytical}},
author = {Mohsen Reda Mahmoud Moursi and Ahmed Stohy},
year = {2026},
eprint = {2610.25967},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25967},
}
Building embodied agents that can perceive, reason, and act across diverse 3D worlds is a central research direction toward artificial general intelligence. Yet most existing work remains confined to specific tasks in a single scene and transfers poorly to new environments, leaving general embodied agents for open-world settings under-explored. We refer to such agents as Embodied World Agents (EWAs), whose defining property is the ability to understand diverse visual environments and execute general actions in open-world conditions. To advance EWA research, we study open-world embodied …
@misc{feng2026embodiedworld,
title = {{EmbodiedWorld-200K: Toward Embodied World Agents via Embodied-Planning Dataset and Interactive World Models}},
author = {X. Feng and J. Tang and Z. Lin and L. Chen and S. Shao and Q. Lu and K. Huang},
year = {2026},
eprint = {2610.25959},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2610.25959},
}
Yutong Chen, Jingxin Mao, Zhiwei Wei, Bing Li et al.
Abstract摘要
Reliable recognition of mission-level UAV intent from multimodal observations is essential for low-altitude airspace surveillance and security. Existing multimodal UAV datasets primarily support detection, tracking, and localization, leaving limited support for evaluating mission-intent recognition across held-out trajectory programs and incomplete radar histories. We introduce AeroIntent, a simulation-based benchmark comprising 990 synchronized multimodal sequences generated by 22 trajectory programs across five mission intents. The benchmark provides radar, RGB, infrared, and depth …
@misc{chen2026aerointent,
title = {{AeroIntent: A Synchronized Multimodal Benchmark for UAV Mission Intent Recognition}},
author = {Yutong Chen and Jingxin Mao and Zhiwei Wei and Bing Li and Jianzhong Ni and Rongqing Zhang},
year = {2026},
eprint = {2610.25869},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25869},
}
Zhenqi Wu, Trung Tien Dong, Aditya Penumarti, Mohamed A. Fouad et al.
Abstract摘要
Underwater robots support a growing range of work, from inspecting offshore structures and pipelines to surveying reefs and accompanying divers. Most of these tasks, however, are still flown by hand by a pilot, which raises the question of whether they can be carried out autonomously instead. On land, vision-language-action (VLA) models already let robots carry out tasks autonomously from language instructions, which makes them a natural candidate for taking the pilot's place. Underwater, however, there is little data to train such models. We take inspiration from how these tasks are flown …
@misc{wu2026aquaact,
title = {{AQUAACT: A Joystick-Level Underwater Teleoperation Dataset and Closed-Loop Benchmark}},
author = {Zhenqi Wu and Trung Tien Dong and Aditya Penumarti and Mohamed A. Fouad and Menna A. Fouad and Mingyang Mao and Jane Jaejeong Shin and Xiaomin Lin},
year = {2026},
eprint = {2610.25534},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.25534},
}
Yong Wang, Kecheng Zheng, Xing Zhu, Chao Xu et al.
Abstract摘要
Bimanual manipulation with dexterous hands has attracted growing attention in research on robotic action models. However, few studies have developed benchmarks specifically tailored for dexterous manipulation, leaving the dexterous manipulation capabilities of existing models insufficiently characterized. In this paper, we present BiDex-100, a simulation benchmark for bimanual dexterous manipulation that combines broad task coverage, multi-dimensional evaluation, and high-DoF hand embodiments. Concretely, the benchmark comprises 106 tasks organized along five capability axes: generalization, …
@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},
}
Xueyang Zhou, Yangming Xu, Qianjiang Li, Guiyao Tie et al.
Abstract摘要
LIBERO has emerged as a widely adopted benchmark for evaluating robotic manipulation policies. However, strong performance on LIBERO does not necessarily indicate robust embodied capabilities, as models may exploit benchmark-specific regularities rather than generalize to diverse operating conditions. In this work, we introduce LIBERO-Pro2.0, a comprehensive benchmark for systematically evaluating the robustness of embodied foundation models under distribution shifts. LIBERO-Pro2.0 encompasses 22 static perturbations and 20 runtime interventions across six dimensions: Condition, Environment, …
@misc{zhou2026libero,
title = {{LIBERO-Pro2.0: A Benchmark for Evaluating the Robustness of Embodied Foundation Models}},
author = {Xueyang Zhou and Yangming Xu and Qianjiang Li and Guiyao Tie and Junhao Wu and Ziheng Huang and Guowen Zhang and Zhijie Yuan and others},
year = {2026},
eprint = {2610.24587},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.24587},
}
Shaojie Ren, Jingwen Sun, Zezhi Liu, Hongjin Chen et al.
Abstract摘要
Most simulation benchmarks for robotic manipulation keep target objects static and treat policy inference as instantaneous. These assumptions simplify evaluation but hide how a moving target evolves during computation and how stale an observation is when an action is executed. We introduce LIBERO-Dynamic, a dynamic extension of all 40 LIBERO tasks. It drives objects through contact friction along three trajectory families and speed tiers, while advancing the physical world according to calibrated, model-specific inference latency. The same mechanism supports training by replaying verified …
@misc{ren2026libero,
title = {{LIBERO-Dynamic: Benchmarking Robotic Manipulation in Dynamic Environments}},
author = {Shaojie Ren and Jingwen Sun and Zezhi Liu and Hongjin Chen and Xin Jin and Wei Zou and Zhibo Chen},
year = {2026},
eprint = {2610.24579},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.24579},
}
Enabling humanoid robots to participate effectively in social interactions is important for their integration into human-centered environments. Existing methods, however, typically map pre-generated speech directly to continuous motion trajectories but have limited awareness of complex social environments and lack explicit high-level semantic action planning. We introduce SocialCoAct, a unified agent-centered framework for VLM-based egocentric social co-action. We formulate egocentric social co-action as an end-to-end agent task and develop SocialCoAct-Agent, which transforms egocentric …
@misc{ma2026socialcoact,
title = {{SocialCoAct: Enabling and Benchmarking Egocentric Social Co-Action for VLM-Based Humanoid Robots}},
author = {Jiancong Ma and Kun Li and Jin Jiang and Shengcai Liao},
year = {2026},
eprint = {2610.24559},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.24559},
}
Contact-rich manipulation is essential to everyday activities, yet evaluating robots' ability to perform these activities remains challenging because of their diverse contact behaviors and the difficulty of reproducing tasks across laboratories. Drawing on observations of everyday human manipulation, we identify eight recurring skill categories that provide a practical framework for studying these capabilities. We introduce ContactBench 1.0, an extensible benchmark that instantiates these skills through 36 reference tasks and more than 40 object assets. The assets capture task-relevant …
Vision-language-action (VLA) policies must ground language instructions in the right objects, contact points, and target locations for reliable manipulation. Human gaze provides a natural signal for this grounding, but existing robot datasets rarely pair real human gaze with the multi-camera observations used by modern policies. We introduce GazeAct, a gaze-equipped real-robot manipulation dataset and training framework for multi-view VLA policies. Using screen-based teleoperation, we record gaze directly on the robot camera feed viewed by the operator, yielding 12000 demonstrations across 40 …
@misc{pani2026gazeact,
title = {{GazeAct: A Human-Gaze-Annotated Manipulation Dataset for Attention-Regularized Policies}},
author = {Anupam Pani and Wang Yuan and Qifan Wang and Yanchao Yang},
year = {2026},
eprint = {2610.24507},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.24507},
}
Reliable 3D person detection is important for rescue robotics to give first responders situational awareness. Yet most multimodal 3D detection research targets automotive scenarios in which pedestrians mostly appear in a narrow range of upright, near-road poses. It is therefore unclear how much of the reported performance transfers to rescue settings, as a person may be lying, partially occluded, or in unstructured indoor and outdoor environments. To investigate this gap, we introduce TriSAR-3D, a multimodal person detection dataset recorded with a high-resolution 128-channel LiDAR, an RGB …
@misc{kramer2026trisar,
title = {{TriSAR-3D and PaintFusion: A Novel Trimodal LiDAR-RGB-Thermal Dataset and Method for 3D Person Detection in Search and Rescue}},
author = {Markus Kramer and Jan Wachenfeld and Oskar von Stryk},
year = {2026},
eprint = {2610.22509},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.22509},
}
Zhixu Li, Keqian Tang, Litian Gong, Jingyu Yao et al.
Abstract摘要
Personalization is essential for general-purpose robots to transition into household environments and achieve true practicality. While general robotic manipulation has been extensively studied, personalized robotic manipulation (PeRM) lacks a controlled benchmark for separating task completion from preference satisfaction. We introduce LIBERO-PeRM, a benchmark for preference learning in embodied agents. LIBERO-PeRM studies five questions: 1) how policies follow preferences in zero-shot settings; 2) how they learn single preferences from demonstrations; 3) how efficiently they acquire …
@misc{li2026libero,
title = {{LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation}},
author = {Zhixu Li and Keqian Tang and Litian Gong and Jingyu Yao and Wenqian Zhang and Tianze Xu and Zehao Wang and Yuping Wang and others},
year = {2026},
eprint = {2610.22372},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.22372},
}
General-purpose dexterous manipulation requires policies to transfer knowledge from related tasks to new tasks with only a few target demonstrations. Existing benchmarks often provide too many target demonstrations. They also leave the differences between source and target tasks uncontrolled. As a result, they cannot clearly distinguish knowledge transfer from task memorization. Our key insight is the Core-Gen principle. Core tasks are related tasks that share a functional interaction and provide source knowledge, while a Gen task is a novel target within the same functional family that has …
@misc{cheng2026evo,
title = {{Evo-DexBench: Benchmarking Generalization across Related Tasks for Dexterous Manipulation}},
author = {Bing Cheng and Yiran Mao and Zewei Ye and Yifan Jin and Xinyi Chen and Ruifeng Tu and Shuhuai Wang and Tao Lin and others},
year = {2026},
eprint = {2610.21180},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2610.21180},
}