11 embodied-AI and robotics papers accepted at RSS 2025, 10 with code.11 篇被 RSS 2025 接收的具身智能与机器人论文，其中 10 篇附代码。
Feed 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.日常 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。
An open-source teleoperation glove buildable in hours for under $600, with 21-DoF hand capture, 5-DoF cable-driven force feedback and fingertip haptics; reports the feedback matters for contact-rich tasks without vision.开源遥操作手套，几小时即可组装、成本低于 600 美元：21 自由度手部动捕、5 自由度绳驱力反馈和指尖振动触觉；报告在无视觉输入时力/触觉反馈对接触密集任务至关重要，采集数据可训练模仿学习策略。
@misc{zhang2025doglove,
title = {{DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove}},
author = {Han Zhang and Songbo Hu and Zhecheng Yuan and Huazhe Xu},
year = {2025},
eprint = {2502.07730},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2502.07730},
}
Open-source humanoid with 3D-printed modular gearboxes, buildable for under US$5,000; hardware designs, embedded code and training code are released.开源人形机器人，采用 3D 打印的模块化减速器，在美国可用不到 5000 美元搭建；硬件设计、嵌入式代码与训练框架均已开源。
@misc{chi2025demonstrating,
title = {{Demonstrating Berkeley Humanoid Lite: An Open-source, Accessible, and Customizable 3D-printed Humanoid Robot}},
author = {Yufeng Chi and Qiayuan Liao and Junfeng Long and Xiaoyu Huang and Sophia Shao and Borivoje Nikolic and Zhongyu Li and Koushil Sreenath},
year = {2025},
eprint = {2504.17249},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2504.17249},
}
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。
@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},
}
A VLA outputs mid-level navigation commands in language that an RL locomotion policy executes, so legged robots follow natural-language routes; adds Isaac Lab VLN benchmarks.VLA 先用语言输出中层导航指令，再由强化学习运动策略执行，让腿式机器人按自然语言指令导航；并提供 Isaac Lab 上的 VLN 基准。
@misc{cheng2024navila,
title = {{NaVILA: Legged Robot Vision-Language-Action Model for Navigation}},
author = {An-Chieh Cheng and Yandong Ji and Zhaojing Yang and Zaitian Gongye and Xueyan Zou and Jan Kautz and Erdem Bıyık and Hongxu Yin and Sifei Liu and Xiaolong Wang},
year = {2024},
eprint = {2412.04453},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.04453},
}
A human-sized, tendon-driven hand (5 fingers, 15 underactuated DoF) from 3D-printed and off-the-shelf parts; joint-to-actuator control is learned from MANUS-glove mocap data instead of hand-modelled.人手大小的腱驱动灵巧手（5 指、15 个欠驱动自由度），由 3D 打印件和现成零件制成；关节到电机的控制映射用 MANUS 数据手套的动捕数据学习得到，而非手工建模。
@misc{zorin2025ruka,
title = {{RUKA: Rethinking the Design of Humanoid Hands with Learning}},
author = {Anya Zorin and Irmak Guzey and Billy Yan and Aadhithya Iyer and Lisa Kondrich and Nikhil X. Bhattasali and Lerrel Pinto},
year = {2025},
eprint = {2504.13165},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2504.13165},
}
A slow latent diffusion policy plans action chunks while a fast policy reacts to tactile/force feedback; reports >35% higher scores than visual imitation baselines on three contact-rich tasks.慢速扩散策略在隐空间规划动作块，快速策略根据触觉/力反馈高频调整；论文报告在三个接触丰富任务上比纯视觉模仿学习基线总分高 35% 以上。
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},
}
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。
@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},
}
Extends NaVid into one video VLA model for several navigation tasks (instruction following, object search, question answering, human following).把 NaVid 扩展为单一视频 VLA 模型，统一处理多种导航任务（指令导航、目标搜索、具身问答、跟随人）。
@misc{zhang2024uni,
title = {{Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks}},
author = {Jiazhao Zhang and Kunyu Wang and Shaoan Wang and Minghan Li and Haoran Liu and Songlin Wei and Zhongyuan Wang and Zhizheng Zhang and He Wang},
year = {2024},
eprint = {2412.06224},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2412.06224},
}
Learns task-centric latent actions from videos (in DINO feature space, filtered by language) so one policy can be pretrained across robots and human video; reports beating OpenVLA with far less pretraining compute.在 DINO 特征空间中、借助语言指令过滤，从视频里学习“任务相关”的隐动作，使策略可跨机器人与人类视频预训练；报告以少得多的预训练算力超过 OpenVLA。
@misc{bu2025univla,
title = {{UniVLA: Learning to Act Anywhere with Task-centric Latent Actions}},
author = {Qingwen Bu and Yanting Yang and Jisong Cai and Shenyuan Gao and Guanghui Ren and Maoqing Yao and Ping Luo and Hongyang Li},
year = {2025},
eprint = {2505.06111},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2505.06111},
}
UVA — one shared latent for video and actions with two light diffusion heads; masked training lets the same model act as policy, video generator and forward/inverse dynamics model, while keeping policy inference fast.UVA：视频与动作共享一个潜表示，配两个轻量扩散头解码；通过掩码训练，同一模型既能当策略，也能做视频生成和正/逆动力学，且策略推理仍然较快。