5 embodied-AI and robotics papers accepted at ICRA 2025, 2 with code.5 篇被 ICRA 2025 接收的具身智能与机器人论文，其中 2 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。
CANVAS lets people guide navigation with abstract cues such as verbal commands or rough sketches, even when they are noisy.CANVAS 让人用语音指令或粗略草图等抽象线索引导导航，即使线索不精确。
@misc{choi2024canvas,
title = {{CANVAS: Commonsense-Aware Navigation System for Intuitive Human-Robot Interaction}},
author = {Suhwan Choi and Yongjun Cho and Minchan Kim and Jaeyoon Jung and Myunchul Joe and Yubeen Park and Minseo Kim and Sungwoong Kim and Sungjae Lee and Hwiseong Park and Jiwan Chung and Youngjae Yu},
year = {2024},
eprint = {2410.01273},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.01273},
}
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},
}
Distills one policy that supports several humanoid control modes (root velocity, joint angles, keypoint tracking) instead of training a separate controller per mode.蒸馏出单一策略，支持多种人形控制模式（根部速度、关节角、关键点跟踪），不必为每种模式各训练一个控制器。
@misc{he2024hover,
title = {{HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots}},
author = {Tairan He and Wenli Xiao and Toru Lin and Zhengyi Luo and Zhenjia Xu and Zhenyu Jiang and Jan Kautz and Changliu Liu and Guanya Shi and Xiaolong Wang and Linxi Fan and Yuke Zhu},
year = {2024},
eprint = {2410.21229},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2410.21229},
}
HATO — bimanual setup from two UR5e arms and repurposed prosthetic hands with fingertip touch sensors, VR teleop for data collection, and visuotactile policies for tasks like pouring and handovers.HATO：用两台 UR5e 机械臂和改装的假肢手（指尖带触觉传感器）搭建双臂系统，用 VR 遥操作采集数据，并训练视觉-触觉策略完成倒酒、交接等任务。
@misc{lin2024learning,
title = {{Learning Visuotactile Skills with Two Multifingered Hands}},
author = {Toru Lin and Yu Zhang and Qiyang Li and Haozhi Qi and Brent Yi and Sergey Levine and Jitendra Malik},
year = {2024},
eprint = {2404.16823},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2404.16823},
}
Keeps IK and retargeting for precise upper-body control, and uses RL with Predictive Motion Priors for robust legs.上半身保留 IK 与重定向以求精确，腿部用结合预测运动先验的 RL 保证鲁棒。
@misc{lu2024mobile,
title = {{Mobile-TeleVision: Predictive Motion Priors for Humanoid Whole-Body Control}},
author = {Chenhao Lu and Xuxin Cheng and Jialong Li and Shiqi Yang and Mazeyu Ji and Chengjing Yuan and Ge Yang and Sha Yi and Xiaolong Wang},
year = {2024},
eprint = {2412.07773},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.07773},
}