NYU · embodied AI & robot learning纽约大学 · 具身智能与机器人学习
10 curated papers, code, datasets and hardware from NYU (7 with code), with one-line takeaways.10 个来自纽约大学的精选论文、代码、数据集与硬件（7 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。
The open-source Solo quadruped and its modular torque-controlled actuators, built from off-the-shelf and 3D-printed parts so labs can reproduce a dynamic legged robot.开源四足机器人 Solo 及其模块化力矩控制关节，采用现成件和 3D 打印件，便于各实验室复现动态腿式机器人。
@misc{grimminger2019open,
title = {{An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research}},
author = {Felix Grimminger and Avadesh Meduri and Majid Khadiv and Julian Viereck and Manuel Wüthrich and Maximilien Naveau and Vincent Berenz and Steve Heim and Felix Widmaier and Thomas Flayols and Jonathan Fiene and Alexander Badri-Spröwitz and Ludovic Righetti},
year = {2019},
eprint = {1910.00093},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/1910.00093},
}
An open-source 56-DOF mobile bimanual robot with 7-DOF arms and 17-DOF hands for about US$8.1k in parts, with Apple Vision Pro teleoperation and a demonstrated imitation learning pipeline.开源 56 自由度移动双臂机器人，含 7 自由度手臂和 17 自由度灵巧手，零件成本约 8100 美元，支持 Apple Vision Pro 遥操作，并演示了模仿学习全流程。
@misc{cheng2026mabel,
title = {{MABEL: An Open Anthropomorphic Mobile Bimanual Platform for Teleoperation and Learning}},
author = {Qilong Cheng and Rooholla Khorrambakht and Mohamed Yassine Kabouri and Quang Nam Nguyen and Luca Macesanu and Haoran Zhou and Yipeng Wang and Ludovic Righetti},
year = {2026},
eprint = {2611.04083},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.04083},
}
Learned goal-conditioned inverse kinematics that can hold the offset a position controller needs at rest; reports errors within 1 mm and 0.2° on 93.5% of held-out simulated tasks and training directly on a real arm.学习式目标条件逆运动学，能保持位置控制器静止时所需的偏置；报告在 93.5% 的留出仿真任务上误差低于 1 mm 和 0.2°，并可直接在真实机械臂上训练。
@misc{liu2026policyik,
title = {{PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning}},
author = {Yuhan Liu and Xinqi Liu and Zixuan Dong and Keith W. Ross and Shenji Wan},
year = {2026},
eprint = {2611.05372},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2611.05372},
}
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},
}
@misc{bhirangi2024anyskin,
title = {{AnySkin: Plug-and-play Skin Sensing for Robotic Touch}},
author = {Raunaq Bhirangi and Venkatesh Pattabiraman and Enes Erciyes and Yifeng Cao and Tess Hellebrekers and Lerrel Pinto},
year = {2024},
eprint = {2409.08276},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2409.08276},
}
Predicts future DINOv2 patch features instead of pixels, so a world model trained on offline data can plan to new visual goals at test time.预测未来的 DINOv2 图像块特征而非像素，使基于离线数据训练的世界模型能在测试时规划到新的视觉目标。
@misc{zhou2024dino,
title = {{DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning}},
author = {Gaoyue Zhou and Hengkai Pan and Yann LeCun and Lerrel Pinto},
year = {2024},
eprint = {2411.04983},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2411.04983},
}
@misc{liu2024ok,
title = {{OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics}},
author = {Peiqi Liu and Yaswanth Orru and Jay Vakil and Chris Paxton and Nur Muhammad Mahi Shafiullah and Lerrel Pinto},
year = {2024},
eprint = {2401.12202},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2401.12202},
}
Dobb-E collects home demonstrations with a cheap reacher-grabber "stick", pretrains on them, then adapts to a new household task from a few minutes of data.Dobb-E 用低成本的手持夹取“棒”采集家庭示范数据并预训练，在新家庭任务上只需几分钟数据即可适配。
@misc{shafiullah2023bringing,
title = {{On Bringing Robots Home}},
author = {Nur Muhammad Mahi Shafiullah and Anant Rai and Haritheja Etukuru and Yiqian Liu and Ishan Misra and Soumith Chintala and Lerrel Pinto},
year = {2023},
eprint = {2311.16098},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2311.16098},
}
BeT — clusters actions into k bins plus a continuous offset so a transformer can imitate multimodal demonstrations instead of averaging them.BeT：把动作聚成 k 个类别再加连续偏移，让 Transformer 能模仿多模态示教，而不是把不同模式平均掉。
@misc{shafiullah2022behavior,
title = {{Behavior Transformers: Cloning $k$ modes with one stone}},
author = {Nur Muhammad Mahi Shafiullah and Zichen Jeff Cui and Ariuntuya Altanzaya and Lerrel Pinto},
year = {2022},
eprint = {2206.11251},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2206.11251},
}
DrQ-v2 - a simple model-free actor-critic with image augmentation that learns continuous control from pixels (incl. humanoid tasks in the DeepMind Control Suite) on a single GPU; a common visual-RL baseline.DrQ-v2：带图像增强的简单无模型 actor-critic，单 GPU 即可从像素学习连续控制（包括 DeepMind Control Suite 的人形任务），是常用的视觉强化学习基线。