视觉与机器人

机器人 / 具身智能

机器人、具身智能、机器人学习、操作、导航和具身世界模型。

2025-09-09 至 2025-09-09 共收录 5 篇信号源：cs.RO, cs.AI, cs.CV, cs.LG

1. 模仿学习与强化学习 5 篇

2509.06714 2025-09-09 cs.LG 79%

RT-HCP: Dealing with Inference Delays and Sample Efficiency to Learn Directly on Robotic Platforms

Zakariae El Asri, Ibrahim Laiche, Clément Rambour, Olivier Sigaud, Nicolas Thome

机构 * Sorbonne Université, CNRS, ISIR（索邦大学、法国国家科学研究中心、ISIR研究所）； Institut Universitaire de France (IUF)（法国大学机构（IUF））

专题命中模仿学习与强化学习：robotic(title,abstract)；分类 cs.LG

Comments IROS 2025

2506.11948 2025-09-09 cs.RO cs.AI 73%

SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies

Nadun Ranawaka Arachchige, Zhenyang Chen, Wonsuhk Jung, Woo Chul Shin, Rohan Bansal, Pierre Barroso, Yu Hang He, Yingyang Celine Lin, Benjamin Joffe, Shreyas Kousik, Danfei Xu

专题命中模仿学习与强化学习：manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.AI

Comments The first two authors contributed equally. Accepted to CoRL 2025

2509.05397 2025-09-09 cs.RO cs.LG 66%

RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning

Matthew Lai, Keegan Go, Zhibin Li, Torsten Kroger, Stefan Schaal, Kelsey Allen, Jonathan Scholz

机构 * Google DeepMind（谷歌DeepMind）； University College London（伦敦大学学院）； Intrinsic

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO、cs.LG；robotics(comments)

Comments Published in Science Robotics

Journal ref RoboBallet: Planning for multirobot reaching with graph neural networks and reinforcement learning. Sci. Robot. 10, eads1204(2025)

2509.05735 2025-09-09 cs.LG cs.AI 62%

Offline vs. Online Learning in Model-based RL: Lessons for Data Collection Strategies

Jiaqi Chen, Ji Shi, Cansu Sancaktar, Jonas Frey, Georg Martius

专题命中模仿学习与强化学习：world model(abstract)；分类 cs.AI、cs.LG

Comments Accepted at Reinforcement Learning Conference (RLC 2025); Code available at: https://github.com/swsychen/Offline_vs_Online_in_MBRL

2410.00757 2025-09-09 cs.RO 57%

Collaborative motion planning for multi-manipulator systems through Reinforcement Learning and Dynamic Movement Primitives

Siddharth Singh, Tian Xu, Qing Chang

机构 * Department of Mechanical & Aerospace Engineering, University of Virginia（机械与航空航天工程系，弗吉尼亚大学）

专题命中模仿学习与强化学习：robotic(abstract)；分类 cs.RO

Comments 7 pages, 7 figures, conference submission