机器人 / 具身智能

Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Reinforcement Learning

Younggyo Seo, Pieter Abbeel

机构 * UC Berkeley（伯克利大学） 纠错

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

Comments 18 Pages. Website: https://younggyo.me/cqn-as/

Are LLMs The Way Forward? A Case Study on LLM-Guided Reinforcement Learning for Decentralized Autonomous Driving

Timur Anvar, Jeffrey Chen, Yuyan Wang, Rohan Chandra

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

Structured Imitation Learning of Interactive Policies through Inverse Games

Max M. Sun, Todd Murphey

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

Comments Presented at the "Workshop on Generative Modeling Meets Human-Robot Interaction" at Robotics: Science and Systems 2025. Workshop website: https://sites.google.com/view/gai-hri/

Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teams

Hung Du, Hy Nguyen, Srikanth Thudumu, Rajesh Vasa, Kon Mouzakis

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

Comments Accepted poster at the IEEE Consumer Communications & Networking Conference (CCNC) 2026

Task-Aware Morphology Optimization of Planar Manipulators via Reinforcement Learning

Arvind Kumar Mishra, Sohom Chakrabarty

机构 * Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee 247667,India（印度理工学院罗奥尔基分校电子工程系） 纠错

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

Comments 10 pages, 11 figures, It is submitted as a journal option paper associated with the IFAC World Congress 2026

Generating Causal Explanations of Vehicular Agent Behavioural Interactions with Learnt Reward Profiles

Rhys Howard, Nick Hawes, Lars Kunze

机构 * Oxford Robotics Institute, Dept. of Eng. Sci., University of Oxford（牛津大学机器人研究所） 纠错

专题命中 模仿学习与强化学习 ：robotics(comments,journal_ref)；分类 cs.RO、cs.AI

Comments 8 Pages, 5 Figures, To be published in the Proceedings of the 2025 IEEE International Conference on Robotics & Automation, Initial upload of accepted paper

Journal ref 2025 IEEE International Conference on Robotics and Automation (ICRA), Atlanta, GA, USA, 2025

Anytime Safe Reinforcement Learning

Pol Mestres, Arnau Marzabal, Jorge Cortés

专题命中 模仿学习与强化学习 ：navigation(abstract)