机器人 / 具身智能

Robot Learning from a Physical World Model

Jiageng Mao, Sicheng He, Hao-Ning Wu, Yang You, Shuyang Sun, Zhicheng Wang, Yanan Bao, Huizhong Chen, Leonidas Guibas, Vitor Guizilini, Howard Zhou, Yue Wang

机构 * Google DeepMind（谷歌DeepMind） ； USC（美国斯克利普斯大学） ； Stanford（斯坦福大学） ； Toyota Research Institute（丰田研究机构） 纠错

专题命中 机器人学习 ：robot learning(title,abstract)；world model(title,abstract)；robotics(abstract)；manipulation(abstract)

Comments Project page: https://pointscoder.github.io/PhysWorld_Web/

World Model for Robot Learning: A Comprehensive Survey

机器人学习的世界模型：全面综述

Bohan Hou, Gen Li, Jindou Jia, Tuo An, Xinying Guo, Sicong Leng, Haoran Geng, Yanjie Ze, Tatsuya Harada, Philip Torr, Oier Mees, Marc Pollefeys, Zhuang Liu, Jiajun Wu, Pieter Abbeel, Jitendra Malik, Yilun Du, Jianfei Yang

机构 * Nanyang Technological University（南洋理工大学） ； University of California, Berkeley（加州大学伯克利分校） ； Stanford University（斯坦福大学） ； The University of Tokyo（东京大学） ； University of Oxford（牛津大学） ； Microsoft（微软公司） ； ETH Zurich（苏黎世联邦理工学院） ； Princeton University（普林斯顿大学） ； Harvard University（哈佛大学） 纠错

专题命中 机器人学习 ：robot learning(title,abstract)；world model(title,abstract)；embodied agent(abstract)；navigation(abstract)

AI总结 综述从机器人学习角度系统回顾世界模型的快速发展，探讨其与机器人策略的耦合、作为强化学习模拟器的作用以及机器人视频世界模型的发展，总结关键范式与挑战。

Comments 43 pages, 6 figures

Mask World Model: Predicting What Matters for Robust Robot Policy Learning

遮罩世界模型：为鲁棒机器人策略学习预测重要的内容

Yunfan Lou, Xiaowei Chi, Xiaojie Zhang, Zezhong Qian, Chengxuan Li, Rongyu Zhang, Yaoxu Lyu, Guoyu Song, Chuyao Fu, Haoxuan Xu, Pengwei Wang, Shanghang Zhang

机构 * Peking University, Beijing, China ； State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University ； Beijing Academy of Artificial Intelligence, Beijing, China ； National University of Singapore, Singapore ； The Hong Kong University of Science ； Nanjing University, Nanjing, China 纠错

专题命中 机器人学习 ：world model(title,summary_cn)；robot policy(title,abstract)；分类 cs.RO

AI总结 本文提出Mask World Model，通过预测语义遮罩而非像素，提升机器人策略学习的鲁棒性与泛化能力，实验证明其在仿真和现实任务中均优于现有方法。

Comments 16 pages,5 figures

FLARE: Robot Learning with Implicit World Modeling

Ruijie Zheng, Jing Wang, Scott Reed, Johan Bjorck, Yu Fang, Fengyuan Hu, Joel Jang, Kaushil Kundalia, Zongyu Lin, Loic Magne, Avnish Narayan, You Liang Tan, Guanzhi Wang, Qi Wang, Jiannan Xiang, Yinzhen Xu, Seonghyeon Ye, Jan Kautz, Furong Huang, Yuke Zhu, Linxi Fan

机构 * NVIDIA ； University of Maryland, College Park（马里兰大学） ； Nanyang Technological University（南洋理工大学） ； University of Texas, Austin（德克萨斯大学） 纠错

专题命中 机器人学习 ：world model(title,abstract)；robot learning(title)；manipulation(abstract)；robot policy(abstract)

Comments Project Webpage / Blogpost: https://research.nvidia.com/labs/gear/flare

A Survey on Robotics with Foundation Models: toward Embodied AI

Zhiyuan Xu, Kun Wu, Junjie Wen, Jinming Li, Ning Liu, Zhengping Che, Jian Tang

专题命中 机器人学习 ：robotics(title,abstract)；embodied AI(title,abstract)；robot learning(abstract)；manipulation(abstract)

Sample Efficient Robot Learning with Structured World Models

Tuluhan Akbulut, Max Merlin, Shane Parr, Benedict Quartey, Skye Thompson

专题命中 机器人学习 ：world model(title,abstract)；robot learning(title)；robotics(abstract)；manipulation(abstract)

A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation

深度网络在机器人学习控制中的应用综述：从强化到模仿

Lei Tai, Jingwei Zhang, Ming Liu, Joschka Boedecker, Wolfram Burgard

机构 * University of Freiburg（弗赖堡大学） 纠错

专题命中 机器人学习 ：manipulation(summary_cn,abstract)；robotics(title,abstract)；navigation(abstract)；分类 cs.RO、cs.AI、cs.LG

AI总结 本文综述了深度学习在机器人学习控制中的应用，探讨了深度强化学习和模仿学习两大主流方法，分析了其在导航、 manipulation 任务中的应用及现实差距挑战。

Comments 19 pages, 1 figures

DayDreamer: World Models for Physical Robot Learning

Philipp Wu, Alejandro Escontrela, Danijar Hafner, Ken Goldberg, Pieter Abbeel

专题命中 机器人学习 ：robot learning(title,abstract)；world model(title,abstract)；robotic(abstract)；分类 cs.RO、cs.AI、cs.LG

Comments Website: https://danijar.com/daydreamer

Targeting World Models to Compromise Robot Learning Pipelines

针对世界模型以破坏机器人学习流程

Ethan Rathbun, Ahmed Agha, Saaduddin Mahmud, Christopher Amato, Alina Oprea, Eugene Bagdasarian

机构 * Northeastern University（东北大学） ； University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校） 纠错

专题命中 机器人学习 ：robot learning(title,abstract)；world model(title,abstract)；robotic(abstract)；分类 cs.RO、cs.AI

AI总结 本文提出针对世界模型的新型数据投毒攻击方法，通过注入恶意提示或转换动态，在看似安全的数据中生成危险训练轨迹，导致下游策略不安全。

Comments 8 Pages, CoRL Preprint

The Treachery of Images: Bayesian Scene Keypoints for Deep Policy Learning in Robotic Manipulation

Jan Ole von Hartz, Eugenio Chisari, Tim Welschehold, Wolfram Burgard, Joschka Boedecker, Abhinav Valada

专题命中 机器人学习 ：manipulation(title,abstract)；robotic(title,abstract)；分类 cs.RO、cs.AI、cs.CV；robotics(journal_ref)

Journal ref IEEE Robotics and Automation Letters, vol. 8, no. 11, pp. 6931-6938, Nov. 2023

Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interaction

Kaiwu：一种用于机器人学习和人机交互的多模态操控数据集和框架

Shuo Jiang, Haonan Li, Ruochen Ren, Yanmin Zhou, Zhipeng Wang, Bin He

专题命中 机器人学习 ：robot learning(title,abstract)；manipulation(title,abstract)；robotics(comments,journal_ref)；分类 cs.RO、cs.AI

AI总结 本文提出Kaiwu多模态数据集，解决复杂装配场景中缺失的真实同步多模态数据问题，通过20名受试者和30个交互对象，记录11,664个整合动作实例，支持机器人学习、精细操作、人类意图研究和人机协作。

Comments 8 pages, 5 figures, Submitted to IEEE Robotics and Automation Letters (RAL)

Journal ref IEEE Robotics and Automation Letters, vol. 10, no. 11, pp. 11482-11489, Nov. 2025

Maximizing Alignment with Minimal Feedback: Efficiently Learning Rewards for Visuomotor Robot Policy Alignment

Ran Tian, Yilin Wu, Chenfeng Xu, Masayoshi Tomizuka, Jitendra Malik, Andrea Bajcsy

专题命中 机器人学习 ：robot policy(title,abstract)；robotics(abstract)；manipulation(abstract)；robotic(abstract)

Comments Submitted to IJRR, this paper is an extended journal version of the conference paper arXiv:2310.07932 with new results and discussion. arXiv admin note: substantial text overlap with arXiv:2310.07932

A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

Ajay Kumar Tanwani, Nitesh Mor, John Kubiatowicz, Joseph E. Gonzalez, Ken Goldberg

专题命中 机器人学习 ：robotics(title,abstract)；robot learning(title,abstract)；分类 cs.RO

Comments IEEE International Conference on Robotics and Automation, ICRA, 2019

PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation

PALM：通过具身理由推理进行长时间 horizon 肢体操作的进展感知策略学习

Yuanzhe Liu, Jingyuan Zhu, Yuchen Mo, Gen Li, Xu Cao, Jin Jin, Yifan Shen, Zhengyuan Li, Tianjiao Yu, Wenzhen Yuan, Fangqiang Ding, Ismini Lourentzou

机构 * University of Pennsylvania（宾夕法尼亚大学） ； University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校） ； Nanyang Technological University（南洋理工大学） ； University of Oxford（牛津大学） ； Massachusetts Institute of Technology（麻省理工学院） 纠错

专题命中 机器人学习 ：manipulation(title,abstract)；robotic(title,abstract)；分类 cs.RO

AI总结 PALM通过具身理由推理和子任务进展线索，改进长horizon机器人操作的策略学习，实现91.8%的成功率和更长的任务长度。

Comments CVPR 2026

ROSER: Few-Shot Robotic Sequence Retrieval for Scalable Robot Learning

ROSER：用于可扩展机器人学习的少样本机器人序列检索

Zillur Rahman, Eddison Pham, Alejandro Daniel Noel, Cristian Meo

机构 * University of Nevada, Las Vegas（内华达大学拉斯维加斯分校） ； University of Toronto（多伦多大学） ； LatentWorlds AI 纠错

专题命中 机器人学习 ：robot learning(title,abstract)；robotic(title,abstract)；分类 cs.RO

AI总结 ROSER通过少样本检索框架有效解决机器人序列数据稀缺问题，提升机器人学习的数据可用性。

Comments 2026 ICLR DATA-FM Workshop

MP1: MeanFlow Tames Policy Learning in 1-step for Robotic Manipulation

MP1: 均值流使政策学习在1步中适用于机器人操作

Juyi Sheng, Ziyi Wang, Peiming Li, Mengyuan Liu

专题命中 机器人学习 ：manipulation(title,abstract)；robotic(title)；robot learning(abstract)；分类 cs.RO

AI总结 MP1通过MeanFlow范式和轻量级分散损失，在1-NFE中实现更精确且可控的动作轨迹生成，优于现有方法。

Comments This paper has been accepted by AAAI 2026

What Foundation Models can Bring for Robot Learning in Manipulation : A Survey

Dingzhe Li, Yixiang Jin, Yuhao Sun, Yong A, Hongze Yu, Jun Shi, Xiaoshuai Hao, Peng Hao, Huaping Liu, Xiang Li, Xinde Li, Fuchun Sun, Jianwei Zhang, Bin Fang

机构 * Beijing University of Posts and Telecommunications（北京邮电大学） ； Tsinghua University（清华大学） ； Southeast University（东南大学） ； Universität Hamburg（汉堡大学） 纠错

专题命中 机器人学习 ：robot learning(title,abstract)；manipulation(title,abstract)；分类 cs.RO

BaRiFlex: A Robotic Gripper with Versatility and Collision Robustness for Robot Learning

Gu-Cheol Jeong, Arpit Bahety, Gabriel Pedraza, Ashish D. Deshpande, Roberto Martín-Martín

机构 * Department of Mechanical Engineering, The University of Texas at Austin（机械工程系，德克萨斯大学奥斯汀分校） ； Department of Computer Science, The University of Texas at Austin（计算机科学系，德克萨斯大学奥斯汀分校） 纠错

专题命中 机器人学习 ：robot learning(title,abstract)；robotic(title,abstract)；分类 cs.RO

Comments 8 pages, 6 figures, project website: https://robin-lab.cs.utexas.edu/bariflex/

BWM: A Low-Cost High-Fidelity World Simulator for Robot Learning

BWM：面向机器人学习的低成本高保真世界模拟器

BWM Team

专题命中 机器人学习 ：robot learning(title,abstract)；world model(abstract,abstract_cn)；manipulation(abstract)；分类 cs.RO、cs.CV

AI总结 本文提出面向机器人操纵的开源世界模拟器BWM，通过动作条件化自回归预测实现高保真，兼具数据引擎与策略评估器功能，在WorldArena挑战赛中表现最优并开源相关资源。

Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policies

测试时对抗接管：针对机器人扩散策略的实时劫持接口

Zi Yin, Peilin Chai, Siyuan Huang, Zhanhao Hu

机构 * Tsinghua University（清华大学） ； Independent Researcher（独立研究员） ； Johns Hopkins University（约翰霍普金斯大学） ； UC Berkeley（加州大学伯克利分校） 纠错

专题命中 机器人学习 ：robotic(title)；embodied AI(abstract)；manipulation(abstract)；navigation(abstract)

AI总结 提出测试时对抗接管（TAKO）方法，通过可微扩散推理学习可重复使用的通用补丁，在测试时切换补丁以劫持机器人策略，实现远程操控，在多种任务和模型上达到100%接管成功率。

Robot Learning from Human Videos: A Survey

从人类视频学习机器人：综述

Junyi Ma, Erhang Zhang, Haoran Yang, Ditao Li, Chenyang Xu, Guangming Wang, Hesheng Wang

机构 * Shanghai Jiao Tong University（上海交通大学） ； University of Cambridge（剑桥大学） 纠错

专题命中 机器人学习 ：robot learning(title)；robotics(abstract)；embodied AI(abstract)；manipulation(abstract)

AI总结 本文综述了通过人类视频学习机器人技能的方法，探讨了政策学习基础、人类视频接口、技能转移层次分类及数据基础，分析了挑战与未来研究方向。

Comments Paper list: https://github.com/IRMVLab/awesome-robot-learning-from-human-videos

Towards Human-level Dexterity via Robot Learning

Gagan Khandate

专题命中 机器人学习 ：robot learning(title,abstract)；robotics(abstract)；manipulation(abstract)；robotic(abstract)

Comments PhD thesis

Generalized Robot Learning Framework

Jiahuan Yan, Zhouyang Hong, Yu Zhao, Yu Tian, Yunxin Liu, Travis Davies, Luhui Hu

专题命中 机器人学习 ：robot learning(title,abstract)；robotics(abstract)；manipulation(abstract)；robotic(abstract)

Comments 6 pages, 2 figures. cs.RO

Learning Generalizable Robot Policy with Human Demonstration Video as a Prompt

以人类演示视频为提示学习可泛化的机器人策略

Xiang Zhu, Yichen Liu, Hezhong Li, Jianyu Chen

机构 * Tsinghua University, China（清华大学，中国） ； Shanghai Qi Zhi Institute, China（上海启智研究院，中国） 纠错

专题命中 机器人学习 ：robot policy(title,abstract)；robot learning(abstract)；manipulation(abstract)；robotic(abstract)

AI总结 提出两阶段框架，利用人类演示视频学习可泛化机器人策略，无需遥操作数据或微调即可执行新任务。

Comments Accepted to the IEEE International Conference on Robotics and Automation (ICRA), 2026

KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning

KinDER：机器人学习与规划的运动学与动力学推理基准

Yixuan Huang, Bowen Li, Vaibhav Saxena, Yichao Liang, Utkarsh Aashu Mishra, Liang Ji, Lihan Zha, Jimmy Wu, Nishanth Kumar, Sebastian Scherer, Danfei Xu, Tom Silver

机构 * Princeton University（普林斯顿大学） ； Carnegie Mellon University（卡内基梅隆大学） ； Georgia Tech（佐治亚理工学院） ； University of Cambridge（剑桥大学） ； NVIDIA（英伟达） ； MIT（麻省理工学院） 纠错

专题命中 机器人学习 ：robot learning(title,abstract)；robotics(abstract,comments)；manipulation(abstract)；robotic(abstract)

AI总结 KinDER基准针对机器人学习与规划中的物理推理挑战，包含25个生成环境、Python库和评估套件，旨在通过系统比较不同方法提升机器人物理推理能力。

Comments Project website: https://prpl-group.com/kinder-site/. 21 pages, 8 figures. Accepted to Robotics Science and Systems (RSS), 2026

RoCoDA: Counterfactual Data Augmentation for Data-Efficient Robot Learning from Demonstrations

Ezra Ameperosa, Jeremy A. Collins, Mrinal Jain, Animesh Garg

机构 * Georgia Institute of Technology（佐治亚理工学院） ； Apptronik, Inc.（Apptronik公司） 纠错

专题命中 机器人学习 ：robot learning(title)；robotics(abstract,comments)；manipulation(abstract)；robotic(abstract)

Comments Accepted to 2025 IEEE International Conference on Robotics and Automation (ICRA)

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Embodiment Collaboration, Abby O'Neill, Abdul Rehman, Abhinav Gupta, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew Wang, Andrey Kolobov, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu, Charlotte Le, Chelsea Finn, Chen Wang, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Driess, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Foster, Fangchen Liu, Federico Ceola, Fei Xia, Feiyu Zhao, Felipe Vieira Frujeri, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guangwen Yang, Guanzhi Wang, Hao Su, Hao-Shu Fang, Haochen Shi, Henghui Bao, Heni Ben Amor, Henrik I Christensen, Hiroki Furuta, Homanga Bharadhwaj, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim, Jaimyn Drake, Jan Peters, Jan Schneider, Jasmine Hsu, Jay Vakil, Jeannette Bohg, Jeffrey Bingham, Jeffrey Wu, Jensen Gao, Jiaheng Hu, Jiajun Wu, Jialin Wu, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jingyun Yang, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kaiyuan Wang, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Ken Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Lin, Kevin Zhang, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Lawrence Yunliang Chen, Lerrel Pinto, Li Fei-Fei, Liam Tan, Linxi "Jim" Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma, Moo Jin Kim, Muhammad Zubair Irshad, Naoaki Kanazawa, Nicklas Hansen, Nicolas Heess, Nikhil J Joshi, Niko Suenderhauf, Ning Liu, Norman Di Palo, Nur Muhammad Mahi Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R Sanketi, Patrick "Tree" Miller, Patrick Yin, Paul Wohlhart, Peng Xu, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Quan Vuong, Rafael Rafailov, Ran Tian, Ria Doshi, Roberto Martín-Martín, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Ruohan Zhang, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante, Sean Kirmani, Sergey Levine, Shan Lin, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham Sonawani, Shubham Tulsiani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vikash Kumar, Vincent Vanhoucke, Vitor Guizilini, Wei Zhan, Wenxuan Zhou, Wolfram Burgard, Xi Chen, Xiangyu Chen, Xiaolong Wang, Xinghao Zhu, Xinyang Geng, Xiyuan Liu, Xu Liangwei, Xuanlin Li, Yansong Pang, Yao Lu, Yecheng Jason Ma, Yejin Kim, Yevgen Chebotar, Yifan Zhou, Yifeng Zhu, Yilin Wu, Ying Xu, Yixuan Wang, Yonatan Bisk, Yongqiang Dou, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yue Cao, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zhuo Xu, Zichen Jeff Cui, Zichen Zhang, Zipeng Fu, Zipeng Lin

专题命中 机器人学习 ：robotic(title,abstract)；robotics(abstract,comments)；manipulation(abstract)；robot policy(abstract)

Comments Project website: https://robotics-transformer-x.github.io

The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning

Ransalu Senanayake

专题命中 机器人学习 ：robot learning(title)；embodied AI(title)；robotics(abstract)；分类 cs.RO、cs.AI、cs.CV

PyPose: A Library for Robot Learning with Physics-based Optimization

PyPose：一个结合物理优化的机器人学习库

Chen Wang, Dasong Gao, Kuan Xu, Junyi Geng, Yaoyu Hu, Yuheng Qiu, Bowen Li, Fan Yang, Brady Moon, Abhinav Pandey, Aryan, Jiahe Xu, Tianhao Wu, Haonan He, Daning Huang, Zhongqiang Ren, Shibo Zhao, Taimeng Fu, Pranay Reddy, Xiao Lin, Wenshan Wang, Jingnan Shi, Rajat Talak, Kun Cao, Yi Du, Han Wang, Huai Yu, Shanzhao Wang, Siyu Chen, Ananth Kashyap, Rohan Bandaru, Karthik Dantu, Jiajun Wu, Lihua Xie, Luca Carlone, Marco Hutter, Sebastian Scherer

专题命中 机器人学习 ：robot learning(title,abstract)；robotics(abstract)；navigation(abstract)；robotic(abstract)

AI总结 PyPose结合深度感知模型与物理优化，提供高效的机器人学习库，实现计算速度提升超过10倍，适用于SLAM、规划、控制等任务。

Comments Project Website: https://pypose.org Documentation: https://pypose.org/docs/ Tutorial: https://pypose.org/tutorials/ Source code: https://github.com/pypose/pypose

Journal ref IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

EfficientFlow: Efficient Equivariant Flow Policy Learning for Embodied AI

EfficientFlow: 为具身AI高效的等价流策略学习

Jianlei Chang, Ruofeng Mei, Wei Ke, Xiangyu Xu

机构 * Xi'an Jiaotong University（西安交通大学） 纠错

专题命中 机器人学习 ：embodied AI(title,abstract)；manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.AI、cs.CV

AI总结 EfficientFlow通过引入等价性与加速正则化策略，提升具身AI的效率与性能。

Comments Accepted by AAAI 2026. Project Page: https://efficientflow.github.io/