机器人 / 具身智能

L2D2: Robot Learning from 2D Drawings

Shaunak A. Mehta, Heramb Nemlekar, Hari Sumant, Dylan P. Losey

机构 * Mechanical Engineering, Virginia Tech（弗吉尼亚理工大学机械工程学院） 纠错

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

Robot Learning Using Multi-Coordinate Elastic Maps

Brendan Hertel, Reza Azadeh

机构 * Persistent Autonomy and Robot Learning (PeARL) Lab, University of Massachusetts Lowell（持续自主与机器人学习实验室，马萨诸塞大学洛厄尔分校） 纠错

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

Comments 7 pages, 6 figures. Accepted to UR 2025. Code available at: https://github.com/brenhertel/MC-Elmap, Accompanying video at: https://youtu.be/KU-ldkTa9UE

Robotic Grinding Skills Learning Based on Geodesic Length Dynamic Motion Primitives

Shuai Ke, Huan Zhao, Xiangfei Li, Zhiao Wei, Yecan Yin, Han Ding

机构 * State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Department of Mechanical Science and Engineering, Huazhong University of Science and Technology（智能制造装备与技术国家重点实验室，机械科学与工程学院，华中科技大学） 纠错

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

Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing

Siddharth Singh, Tian Yu, Qing Chang, John Karigiannis, Shaopeng Liu

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

Comments 35 Pages, 11 Figures

Scaling Robot Policy Learning via Zero-Shot Labeling with Foundation Models

Nils Blank, Moritz Reuss, Marcel Rühle, Ömer Erdinç Yağmurlu, Fabian Wenzel, Oier Mees, Rudolf Lioutikov

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

Comments Project Website at https://robottasklabeling.github.io/

E2H: A Two-Stage Non-Invasive Neural Signal Driven Humanoid Robotic Whole-Body Control Framework

Yiqun Duan, Qiang Zhang, Jinzhao Zhou, Jingkai Sun, Xiaowei Jiang, Jiahang Cao, Jiaxu Wang, Yiqian Yang, Wen Zhao, Gang Han, Yijie Guo, Chin-Teng Lin

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

Extended Reality System for Robotic Learning from Human Demonstration

Isaac Ngui, Courtney McBeth, Grace He, André Corrêa Santos, Luciano Soares, Marco Morales, Nancy M. Amato

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

Comments In submission

RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning

Lawrence Yunliang Chen, Chenfeng Xu, Karthik Dharmarajan, Muhammad Zubair Irshad, Richard Cheng, Kurt Keutzer, Masayoshi Tomizuka, Quan Vuong, Ken Goldberg

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

Comments CoRL 2024 (Oral). Project website: https://rovi-aug.github.io

A Practical Roadmap to Learning from Demonstration for Robotic Manipulators in Manufacturing

Alireza Barekatain, Hamed Habibi, Holger Voos

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

Comments 26 pages, 6 figures

DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks

Alireza Barekatain, Hamed Habibi, Holger Voos

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

Comments 20 pages, 14 figures

A Framework for Learning and Reusing Robotic Skills

Brendan Hertel, Nhu Tran, Meriem Elkoudi, Reza Azadeh

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

Comments 4 pages, 4 figures. Accepted for publication as work-in-progress paper at Ubiquitous Robots (UR) 2024. Code available here: https://github.com/brenhertel/Probabilistic-Segmentation and here: https://github.com/brenhertel/Elastic-Clustering

Journal ref 2024 20th International Conference on Ubiquitous Robots (UR) 801-804

Robot Learning from Demonstration Using Elastic Maps

Brendan Hertel, Matthew Pelland, S. Reza Ahmadzadeh

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

Comments 7 pages, 9 figures, 3 tables. Accepted to IROS 2022. Code available at: https://github.com/brenhertel/ElMapTrajectories Accompanying video at: https://youtu.be/rZgN9Pkw0tg

Journal ref 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 7407-7413

Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environments

Yan Zhang, Teng Xue, Amirreza Razmjoo, Sylvain Calinon

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

Comments Accepted by IEEE RA-L

A Review of Communicating Robot Learning during Human-Robot Interaction

Soheil Habibian, Antonio Alvarez Valdivia, Laura H. Blumenschein, Dylan P. Losey

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

LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, Peter Stone

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

PyPose v0.6: The Imperative Programming Interface for Robotics

Zitong Zhan, Xiangfu Li, Qihang Li, Haonan He, Abhinav Pandey, Haitao Xiao, Yangmengfei Xu, Xiangyu Chen, Kuan Xu, Kun Cao, Zhipeng Zhao, Zihan Wang, Huan Xu, Zihang Fang, Yutian Chen, Wentao Wang, Xu Fang, Yi Du, Tianhao Wu, Xiao Lin, Yuheng Qiu, Fan Yang, Jingnan Shi, Shaoshu Su, Yiren Lu, Taimeng Fu, Karthik Dantu, Jiajun Wu, Lihua Xie, Marco Hutter, Luca Carlone, Sebastian Scherer, Daning Huang, Yaoyu Hu, Junyi Geng, Chen Wang

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

Robotic LEGO Assembly and Disassembly from Human Demonstration

Ruixuan Liu, Yifan Sun, Changliu Liu

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

Failure-aware Policy Learning for Self-assessable Robotics Tasks

Kechun Xu, Runjian Chen, Shuqi Zhao, Zizhang Li, Hongxiang Yu, Ci Chen, Yue Wang, Rong Xiong

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

Gaussian-Process-based Robot Learning from Demonstration

Miguel Arduengo, Adrià Colomé, Joan Lobo-Prat, Luis Sentis, Carme Torras

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

Comments 8 pages, 10 figures

Journal ref Journal of Ambient Intelligence and Humanized Computing, 2023

Residual Robot Learning for Object-Centric Probabilistic Movement Primitives

Joao Carvalho, Dorothea Koert, Marek Daniv, Jan Peters

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

Model Hierarchy Predictive Control of Robotic Systems

He Li, Robert J. Frei, Patrick M. Wensing

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

Comments Author pre-print of accepted RA-L/ICRA 2021 manuscript

Compare Contact Model-based Control and Contact Model-free Learning: A Survey of Robotic Peg-in-hole Assembly Strategies

Jing Xu, Zhimin Hou, Zhi Liu, Hong Qiao

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

Skill Learning by Autonomous Robotic Playing using Active Learning and Creativity

Simon Hangl, Vedran Dunjko, Hans J. Briegel, Justus Piater

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

Scalable Accelerated Decentralized Multi-Robot Policy Search in Continuous Observation Spaces

Shayegan Omidshafiei, Christopher Amato, Miao Liu, Michael Everett, Jonathan P. How, John Vian

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

Proceedings of the 1st International Workshop on Robot Learning and Planning (RLP 2016)

Nancy Amato, Charles Anderson, Gregory Chirikjian, Hamidreza Chitsaz, Vishnu Desaraju, Chinwe Ekenna, Kris Hauser, Geoff Hollinger, Reza Iraji, Minwoo Lee, Qianli Ma, Seth McCammon, Nathan Michael, Shawna Thomas, Diane Uwacu, Yan Yan

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

Learning a natural-language to LTL executable semantic parser for grounded robotics

Christopher Wang, Candace Ross, Yen-Ling Kuo, Boris Katz, Andrei Barbu

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

Comments 10 pages, 2 figures, Accepted in Conference on Robot Learning (CoRL) 2020

BIFROST: Bridging Invariant Feature Representation for Observation-space Sim2Real Transfer

BIFROST: 桥接不变特征表示用于观测空间Sim2Real迁移

Yunfu Deng, Josiah P. Hanna

机构 * University of Wisconsin–Madison（威斯康星大学麦迪逊分校） 纠错

专题命中 机器人学习 ：manipulation(abstract)；navigation(abstract)；robot policy(abstract)；分类 cs.RO、cs.LG

AI总结 提出BIFROST方法，通过跨域双模拟目标学习共享历史编码器，将导致等价长期行为的观测-动作序列映射到相近潜状态，实现零样本sim2real迁移。

Verifiable Foundation Models for Robot Safety

机器人安全的可验证基础模型

Davide Corsi, Kyungmin Kim, Roy Fox

机构 * University of California, Irvine（加州大学尔湾分校） 纠错

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

AI总结 提出FEARL框架，将策略分解为大控制器和小安全模块，使形式化验证仅应用于安全模块，从而在保持控制器表达能力的同时实现可验证的机器人安全控制。

Task Robustness via Re-Labelling Vision-Action Robot Data

通过重新标注视觉-动作机器人数据的任务鲁棒性

Artur Kuramshin, Özgür Aslan, Cyrus Neary, Glen Berseth

机构 * Mila — Quebec AI Institute（Mila — 魁北克人工智能研究所） ； Université de Montréal（蒙特利尔大学） ； The University of British Columbia（不列颠哥伦比亚大学） 纠错

专题命中 机器人学习 ：robotics(abstract)；robot learning(abstract)；manipulation(abstract)；分类 cs.RO、cs.LG

AI总结 提出TREAD框架，利用大型视觉语言模型对机器人数据集进行语义子任务分解和多样化指令生成，无需额外数据收集，提升策略在未见任务上的泛化能力。

Comments Project website: https://akuramshin.github.io/tread

Traj2Action: A Co-Denoising Framework for Trajectory-Guided Human-to-Robot Skill Transfer

Traj2Action: 一种用于轨迹引导的人机技能转移协同去噪框架

Han Zhou, Jinjin Cao, Liyuan Ma, Xueji Fang, Guo-jun Qi

机构 * MAPLE Lab, Westlake University（西湖大学MAPLE实验室） ； Zhejiang University（浙江大学） ； Huazhong University of Science and Technology（华中科技大学） ； Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖大学先进技术研究所） 纠错

专题命中 机器人学习 ：manipulation(abstract)；robot policy(abstract)；robotic(abstract)；分类 cs.RO、cs.AI

AI总结 本文提出Traj2Action框架，通过3D操作端点轨迹作为统一中间表示，解决人机技能转移中的形态差距问题，提升机器人动作性能。