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Shanghai AI Lab (InternRobotics) · embodied AI & robot learning上海人工智能实验室(InternRobotics) · 具身智能与机器人学习

13 curated papers, code, datasets and hardware from Shanghai AI Lab (InternRobotics) (9 with code), with one-line takeaways.13 个来自上海人工智能实验室(InternRobotics)的精选论文、代码、数据集与硬件(9 个附代码),均配一句话要点。

Labs are matched from each entry's affiliation field by simple name rules (data/labs.yaml); joint work appears under every lab named. Only curated entries are listed, because daily arXiv papers carry no affiliations.机构由每个条目的机构字段按名称规则匹配(data/labs.yaml),合作工作会出现在所有署名机构下。仅列出精选条目,因为每日 arXiv 论文不含机构信息。

By topic:按主题: VLA & Robot Foundation ModelsVLA 与机器人基础模型 (8) · Manipulation & Dexterous Hands操作与灵巧手 (4) · Tactile & Visuotactile Sensing触觉与视触觉感知 (1) · Locomotion & Humanoids运动控制与人形机器人 (2) · World Models世界模型 (1) · Robot Learning: RL & Imitation机器人学习:强化学习与模仿学习 (1) · Sim-to-Real & Simulators仿真与 Sim-to-Real (1) · Datasets & Benchmarks数据集与基准 (3) · Hardware & Open-Source Robots硬件与开源机器人 (1)

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All entries全部条目 (13)

Dataset数据集 2026 · InternRobotics new新 2026-10-24

EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ

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%。

BibTeX
@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},
}
Paper论文 2026 · InternRobotics new新 2026-10-27

Profiling a Frontier Agent with EBench: From Direct Control to Harness with Policy

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。

BibTeX
@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},
}
Dataset数据集 2025 · AgiBot, Shanghai AI Lab

AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems

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。

BibTeX
@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},
}
Datasets & Benchmarks数据集与基准Robot arm机械臂Humanoid人形realhumanoiddual-armlarge-scale
Paper论文 2025 · Shanghai AI Lab CVPR 2025

GenManip: LLM-driven Simulation for Generalizable Instruction-Following Manipulation

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。

BibTeX
@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},
}
Hardware硬件 2025 · Shanghai AI Lab · CUHK RSS 2025

HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit

Open-source whole-body humanoid teleoperation — an arm exoskeleton plus motion-sensing gloves, with a foot pedal steering an RL walking controller; the cockpit hardware costs about US$500.开源的人形机器人全身遥操作系统:手臂外骨骼加动作捕捉手套,脚踏板驱动强化学习行走控制器;整套座舱硬件约 500 美元。

BibTeX
@misc{ben2025homie,
  title = {{HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit}},
  author = {Qingwei Ben and Feiyu Jia and Jia Zeng and Junting Dong and Dahua Lin and Jiangmiao Pang},
  year = {2025},
  eprint = {2502.13013},
  archivePrefix = {arXiv},
  primaryClass = {cs.RO},
  url = {https://arxiv.org/abs/2502.13013},
}
Paper论文 2025 · Shanghai AI Lab / SJTU / Fudan / AgiBot

Hume: Introducing System-2 Thinking in Visual-Language-Action Model

Dual-system VLA: a value-query head scores sampled action-chunk candidates (System 2, ~4 Hz) and a light cascaded denoiser refines it (System 1, ~90 Hz); reports gains over π0 on LIBERO, SIMPLER and real robots.双系统 VLA:价值查询头对多组候选动作块打分并择优(System 2,约 4 Hz),轻量级级联去噪策略再将其细化为流畅动作(System 1,约 90 Hz);论文报告在 LIBERO、SIMPLER 和真机上均优于 π0。

BibTeX
@misc{song2025hume,
  title = {{Hume: Introducing System-2 Thinking in Visual-Language-Action Model}},
  author = {Haoming Song and Delin Qu and Yuanqi Yao and Qizhi Chen and Qi Lv and Yiwen Tang and Modi Shi and Guanghui Ren and Maoqing Yao and Bin Zhao and Dong Wang and Xuelong Li},
  year = {2025},
  eprint = {2505.21432},
  archivePrefix = {arXiv},
  primaryClass = {cs.RO},
  url = {https://arxiv.org/abs/2505.21432},
}
Paper论文 2025 · Shanghai AI Lab (Intern Robotics)

InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy

Pretrains spatial grounding on 2.3M samples ('where to act'), then post-trains actions with spatial prompts ('how to act'); reports +14.6% on SimplerEnv Google Robot over the same model without spatial guidance.先在 230 万条空间推理样本上做空间定位预训练("在哪里做"),再用空间提示做动作后训练("怎么做");论文报告在 SimplerEnv Google Robot 上比无空间引导的同一模型高 14.6%。

BibTeX
@misc{chen2025internvla,
  title = {{InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy}},
  author = {Xinyi Chen and Yilun Chen and Yanwei Fu and Ning Gao and Jiaya Jia and Weiyang Jin and Hao Li and Yao Mu and Jiangmiao Pang and Yu Qiao and Yang Tian and Bin Wang and Bolun Wang and Fangjing Wang and Hanqing Wang and Tai Wang and Ziqin Wang and Xueyuan Wei and Chao Wu and Shuai Yang and Jinhui Ye and Junqiu Yu and Jia Zeng and Jingjing Zhang and Jinyu Zhang and Shi Zhang and Feng Zheng and Bowen Zhou and Yangkun Zhu},
  year = {2025},
  eprint = {2510.13778},
  archivePrefix = {arXiv},
  primaryClass = {cs.RO},
  url = {https://arxiv.org/abs/2510.13778},
}
VLA & Robot Foundation ModelsVLA 与机器人基础模型realsimarmspatialgroundingsynthetic-data
Paper论文 2025 · Tsinghua / Shanghai Qi Zhi / Shanghai AI Lab / Fudan / Spirit AI ICLR 2026

OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoning

One model that decides when to think and when to act: it reasons at key moments (subtask done, error, human input) and otherwise acts on its latest reasoning; co-trained with synthetic reasoning-rich VL data.单一模型自主决定何时“想”、何时“做”:在子任务完成、出错或人类插话等关键时刻进行推理,其余时间依据最新推理结果生成动作;并与合成的推理型视觉-语言数据共同训练。

BibTeX
@misc{lin2025onetwovla,
  title = {{OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoning}},
  author = {Fanqi Lin and Ruiqian Nai and Yingdong Hu and Jiacheng You and Junming Zhao and Yang Gao},
  year = {2025},
  eprint = {2505.11917},
  archivePrefix = {arXiv},
  primaryClass = {cs.RO},
  url = {https://arxiv.org/abs/2505.11917},
}
Paper论文 2025 · Shanghai Jiao Tong University, Peking University, HKU, Shanghai AI Lab ICLR 2026

SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning

Online RL for VLAs with GRPO and only binary success rewards on OpenVLA-OFT, plus exploration tweaks; reports 99.1% on LIBERO and 70.4% on RoboTwin 1.0 (vs 39.8% for the SFT baseline).在 OpenVLA-OFT 上用 GRPO 与仅"成功/失败"二值奖励做在线强化学习,并加入若干促进探索的改动;论文报告 LIBERO 成功率 99.1%、RoboTwin 1.0 为 70.4%(SFT 基线 39.8%)。

BibTeX
@misc{li2025simplevla,
  title = {{SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning}},
  author = {Haozhan Li and Yuxin Zuo and Jiale Yu and Yuhao Zhang and Zhaohui Yang and Kaiyan Zhang and Xuekai Zhu and Yuchen Zhang and Tianxing Chen and Ganqu Cui and Dehui Wang and Dingxiang Luo and Yuchen Fan and Youbang Sun and Jia Zeng and Jiangmiao Pang and Shanghang Zhang and Yu Wang and Yao Mu and Bowen Zhou and Ning Ding},
  year = {2025},
  eprint = {2509.09674},
  archivePrefix = {arXiv},
  primaryClass = {cs.RO},
  url = {https://arxiv.org/abs/2509.09674},
}
Paper论文 2025 · Shanghai AI Lab, ShanghaiTech, TeleAI RSS 2025

SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model

Adds 3D-aware position encoding to visual tokens and adaptive action grids, pretrained on 1.1M real robot episodes; new robot setups are handled by re-discretizing the grids.为视觉 token 加入 3D 位置编码,并用自适应动作网格表示动作,在 110 万条真机轨迹上预训练;迁移到新机器人时只需重新划分动作网格。

BibTeX
@misc{qu2025spatialvla,
  title = {{SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model}},
  author = {Delin Qu and Haoming Song and Qizhi Chen and Yuanqi Yao and Xinyi Ye and Yan Ding and Zhigang Wang and JiaYuan Gu and Bin Zhao and Dong Wang and Xuelong Li},
  year = {2025},
  eprint = {2501.15830},
  archivePrefix = {arXiv},
  primaryClass = {cs.RO},
  url = {https://arxiv.org/abs/2501.15830},
}
Paper论文 2025 · Shanghai AI Lab / SJTU ICCV 2025

VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers

Replaces OpenVLA's per-dimension action bins with a convolutional residual VQ-VAE tokenizer trained on 100x more (real + synthetic) data; reports higher LIBERO-90 and real Franka success and ~2.8x faster inference.用卷积残差 VQ-VAE 动作分词器(以百倍规模的真实+合成数据训练)替换 OpenVLA 的逐维离散分箱;论文报告 LIBERO-90 和真实 Franka 任务成功率提升,推理速度约快 2.8 倍。

BibTeX
@misc{wang2025vq,
  title = {{VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers}},
  author = {Yating Wang and Haoyi Zhu and Mingyu Liu and Jiange Yang and Hao-Shu Fang and Tong He},
  year = {2025},
  eprint = {2507.01016},
  archivePrefix = {arXiv},
  primaryClass = {cs.RO},
  url = {https://arxiv.org/abs/2507.01016},
}
VLA & Robot Foundation ModelsVLA 与机器人基础模型Robot arm机械臂realsimarmaction-tokenizer
Paper论文 2025 · Tsinghua AIR, Shanghai AI Lab ICLR 2026

X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model

Gives each data source its own small set of learnable soft-prompt embeddings, so one plain-Transformer flow-matching policy (0.9B) trains across embodiments; evaluated on 6 sim benchmarks and 3 real robots.为每个数据来源配一组可学习的软提示嵌入,使一个基于标准 Transformer 的流匹配策略(0.9B)能跨本体联合训练;在 6 个仿真基准和 3 台真实机器人上评测。

BibTeX
@misc{zheng2025x,
  title = {{X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model}},
  author = {Jinliang Zheng and Jianxiong Li and Zhihao Wang and Dongxiu Liu and Xirui Kang and Yuchun Feng and Yinan Zheng and Jiayin Zou and Yilun Chen and Jia Zeng and Ya-Qin Zhang and Jiangmiao Pang and Jingjing Liu and Tai Wang and Xianyuan Zhan},
  year = {2025},
  eprint = {2510.10274},
  archivePrefix = {arXiv},
  primaryClass = {cs.RO},
  url = {https://arxiv.org/abs/2510.10274},
}
VLA & Robot Foundation ModelsVLA 与机器人基础模型realsimcross-embodimentflow-matchingopen-weights
Paper论文 2024 · Shanghai AI Lab / PKU / CUHK ICLR 2025

Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation

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。

BibTeX
@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},
}