13 embodied-AI and robotics papers accepted at CoRL 2024, 8 with code.13 篇被 CoRL 2024 接收的具身智能与机器人论文，其中 8 篇附代码。
arXiv papers are listed when their arXiv comment says they were accepted (e.g. 'Accepted to CoRL 2026'); papers that do not say so are missing, and the list grows as authors post camera-ready versions. Curated entries carry a hand-checked venue. For CoRL 2026 the official program is used as well.arXiv 论文仅在其 arXiv 备注写明已被接收（如 “Accepted to CoRL 2026”）时列入；未注明的论文不会出现，列表会随作者上传终稿而增加。精选条目的会议/期刊信息经过人工核对。CoRL 2026 另依据官方日程。
ACE — a 3D-printed bimanual exoskeleton for wrist pose plus hand-facing webcams for finger pose (about $600); one setup teleoperates arm-hand systems, humanoids and quadruped-mounted arms.ACE：3D 打印的双臂外骨骼获取手腕位姿、朝向手部的摄像头估计手指姿态（约 600 美元）；同一套设备可遥操作机械臂+灵巧手、人形机器人以及四足搭载的机械臂。
@misc{yang2024ace,
title = {{ACE: A Cross-Platform Visual-Exoskeletons System for Low-Cost Dexterous Teleoperation}},
author = {Shiqi Yang and Minghuan Liu and Yuzhe Qin and Runyu Ding and Jialong Li and Xuxin Cheng and Ruihan Yang and Sha Yi and Xiaolong Wang},
year = {2024},
eprint = {2408.11805},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2408.11805},
}
@misc{shaw2024bimanual,
title = {{Bimanual Dexterity for Complex Tasks}},
author = {Kenneth Shaw and Yulong Li and Jiahui Yang and Mohan Kumar Srirama and Ray Liu and Haoyu Xiong and Russell Mendonca and Deepak Pathak},
year = {2024},
eprint = {2411.13677},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2411.13677},
}
Generates humanoid whole-body motion from language, starting from human motion priors and refining with LLM commonsense reasoning. Oral at CoRL 2024.从语言描述生成人形全身动作：以人体动作先验初始化，再用 LLM 的常识推理加以修正。CoRL 2024 Oral。
@misc{jiang2024harmon,
title = {{Harmon: Whole-Body Motion Generation of Humanoid Robots from Language Descriptions}},
author = {Zhenyu Jiang and Yuqi Xie and Jinhan Li and Ye Yuan and Yifeng Zhu and Yuke Zhu},
year = {2024},
eprint = {2410.12773},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.12773},
}
Learns a vision-based whole-body parkour policy for the Unitree H1 without motion references, covering skills such as jumping onto platforms and leaping over gaps, and transfers it to the real robot.无需动作参考，为 Unitree H1 学习基于视觉的全身跑酷策略，涵盖跳上平台、跨越沟壑等技能，并迁移到真实机器人。
An 'ear-in-hand' gripper records audio with vision during human demos in the wild; contact sound then helps policies when vision alone is ambiguous.“手上长耳朵”：夹爪在野外人类演示时同步录制音频与视觉；当视觉信息不足时，接触声帮助策略完成接触密集操作。
@misc{liu2024maniwav,
title = {{ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data}},
author = {Zeyi Liu and Cheng Chi and Eric Cousineau and Naveen Kuppuswamy and Benjamin Burchfiel and Shuran Song},
year = {2024},
eprint = {2406.19464},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2406.19464},
}
Learns tactile policies from demonstrations given with human hands, closing the gap of teleop where operators steer by sight while the policy needs touch.从人手直接演示学习触觉策略，弥补遥操作中“人靠视觉控制、策略却要用触觉”的落差。
@misc{yu2023mimictouch,
title = {{MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation}},
author = {Kelin Yu and Yunhai Han and Qixian Wang and Vaibhav Saxena and Danfei Xu and Ye Zhao},
year = {2023},
eprint = {2310.16917},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2310.16917},
}
One RL whole-body controller that a humanoid can follow from a VR headset, verbal commands or an RGB camera, and that also runs learned autonomous policies.一个强化学习全身控制器：人形机器人可跟随 VR 头显、语音指令或 RGB 相机的输入，也可执行学习得到的自主策略。
@misc{he2024omnih,
title = {{OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning}},
author = {Tairan He and Zhengyi Luo and Xialin He and Wenli Xiao and Chong Zhang and Weinan Zhang and Kris Kitani and Changliu Liu and Guanya Shi},
year = {2024},
eprint = {2406.08858},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2406.08858},
}
ReKep — a VLM writes manipulation tasks as Python cost functions over 3D keypoints (relational keypoint constraints), solved by a hierarchical optimizer in real time; zero-shot, no task-specific training.ReKep：由视觉语言模型把操作任务写成定义在三维关键点上的 Python 代价函数（关系关键点约束），再由分层优化器实时求解；零样本，无需针对任务训练。
@misc{huang2024rekep,
title = {{ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation}},
author = {Wenlong Huang and Chen Wang and Yunzhu Li and Ruohan Zhang and Li Fei-Fei},
year = {2024},
eprint = {2409.01652},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2409.01652},
}
Trains VLAs to reason through plans, subtasks, motions and visual features before acting; synthetic reasoning data gives OpenVLA a reported 28% success gain on hard generalization tasks with no extra robot data.训练 VLA 在动作前依次推理计划、子任务、动作和视觉特征；用合成的推理数据，OpenVLA 在困难泛化任务上成功率提升 28%，无需额外机器人数据。
@misc{zawalski2024robotic,
title = {{Robotic Control via Embodied Chain-of-Thought Reasoning}},
author = {Michał Zawalski and William Chen and Karl Pertsch and Oier Mees and Chelsea Finn and Sergey Levine},
year = {2024},
eprint = {2407.08693},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.08693},
}
Sparse Diffusion Policy reuses sparse expert modules across tasks instead of one universal policy, for multitask and continual learning.Sparse Diffusion Policy 在任务间复用稀疏专家模块而非单一通用策略，用于多任务和持续学习。
@misc{wang2024sparse,
title = {{Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning}},
author = {Yixiao Wang and Yifei Zhang and Mingxiao Huo and Ran Tian and Xiang Zhang and Yichen Xie and Chenfeng Xu and Pengliang Ji and Wei Zhan and Mingyu Ding and Masayoshi Tomizuka},
year = {2024},
eprint = {2407.01531},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.01531},
}
ThinkGrasp uses GPT-4o reasoning to pick which object and part to grasp in heavy clutter, a plug-and-play vision-language system.ThinkGrasp 借助 GPT-4o 推理，在严重杂乱中决定抓哪个物体的哪个部位，是即插即用的视觉-语言系统。
@misc{qian2024thinkgrasp,
title = {{ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter}},
author = {Yaoyao Qian and Xupeng Zhu and Ondrej Biza and Shuo Jiang and Linfeng Zhao and Haojie Huang and Yu Qi and Robert Platt},
year = {2024},
eprint = {2407.11298},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.11298},
}
An inexpensive open-source mobile base with powered casters, so it is fully holonomic; carries arbitrary arms and comes with phone teleoperation for collecting mobile-manipulation demos for imitation learning.低成本开源移动底盘，采用主动脚轮实现全向（完整约束）运动，可搭载任意机械臂，并配有手机遥操作界面，方便为模仿学习采集移动操作示教数据。
@misc{wu2024tidybot,
title = {{TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning}},
author = {Jimmy Wu and William Chong and Robert Holmberg and Aaditya Prasad and Yihuai Gao and Oussama Khatib and Shuran Song and Szymon Rusinkiewicz and Jeannette Bohg},
year = {2024},
eprint = {2412.10447},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.10447},
}
Robot-free UMI gripper demos train the manipulation policy; a sim-trained whole-body controller tracks its end-effector trajectories on a quadruped. Reports >70% success on all tasks and zero-shot reuse of an arm policy.用手持 UMI 夹爪采集无需机器人的示教来训练操作策略，再由仿真训练的全身控制器在四足机器人上跟踪其输出的末端轨迹；报告所有任务成功率超过 70%，并能零样本复用原为固定机械臂训练的策略。
@misc{ha2024umi,
title = {{UMI-on-Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers}},
author = {Huy Ha and Yihuai Gao and Zipeng Fu and Jie Tan and Shuran Song},
year = {2024},
eprint = {2407.10353},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2407.10353},
}