arXivDaily arXiv每日学术速递 周一至周五更新

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。

2025-11-04 至 2025-11-04 共收录 9 信号源:cs.RO, cs.AI, cs.CV, cs.LG

1. 模仿学习与强化学习 9 篇

2511.01083 2025-11-04 cs.RO 83%

Deployable Vision-driven UAV River Navigation via Human-in-the-loop Preference Alignment

Zihan Wang, Jianwen Li, Li-Fan Wu, Nina Mahmoudian

机构 * School of Mechanical Engineering, Purdue University(机械工程学院,普渡大学)

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

Comments Submitted to ICRA 2026

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2409.07189 2025-11-04 cs.LG cs.AI cs.HC q-bio.BM 73%

AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems

Mohamed Dhouioui, Jonathan Barnoud, Rhoslyn Roebuck Williams, Harry J. Stroud, Phil Bates, David R. Glowacki

机构 * IRL CiTIUS Centro Singular de Investigación en Tecnoloxías Intelixentes(CiTIUS智能技术研究中心) University of Bristol(布里斯托大学)

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

Comments (First presented at the First Workshop on "eXtended Reality \& Intelligent Agents" (XRIA24) @ ECAI24, Santiago De Compostela (Spain), 20 October 2024)

Journal ref SN COMPUT. SCI. 6, 922 (2025)

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2503.21406 2025-11-04 cs.AI cs.LG cs.RO 69%

Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning

Leon Keller, Daniel Tanneberg, Jan Peters

机构 * Intelligent Autonomous Systems, TU Darmstadt, Germany(图腾达姆施塔特大学智能自主系统研究所) German Research Center for AI, Germany(德国人工智能研究中心) Hessian Centre for Artificial Intelligence, Germany(黑森州人工智能中心) Honda Research Institute EU, Germany(本田欧洲研究院)

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

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

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2511.00880 2025-11-04 cs.LG cs.AI 62%

KFCPO: Kronecker-Factored Approximated Constrained Policy Optimization

Joonyoung Lim, Younghwan Yoo

机构 * School of Computer Science and Engineering, Pusan National University, Busan, Korea(计算机科学与工程学院,釜山国立大学,韩国釜山)

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

Comments 12 pages, 8 figures, submitted to ECAI 2025

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2511.00806 2025-11-04 cs.LG cs.AI 62%

Logic-informed reinforcement learning for cross-domain optimization of large-scale cyber-physical systems

Guangxi Wan, Peng Zeng, Xiaoting Dong, Chunhe Song, Shijie Cui, Dong Li, Qingwei Dong, Yiyang Liu, Hongfei Bai

机构 * State Key Laboratory of Robotics and Intelligent Systems(机器人与智能系统国家重点实验室) Shenyang Institute of Automation(沈阳自动化研究所) Chinese Academy of Sciences(中国科学院) University of Chinese Academy of Sciences(中国科学院大学)

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

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2511.00091 2025-11-04 cs.CV cs.RO 62%

Self-Improving Vision-Language-Action Models with Data Generation via Residual RL

Wenli Xiao, Haotian Lin, Andy Peng, Haoru Xue, Tairan He, Yuqi Xie, Fengyuan Hu, Jimmy Wu, Zhengyi Luo, Linxi "Jim" Fan, Guanya Shi, Yuke Zhu

机构 * NVIDIA(NVIDIA公司) CMU(卡内基梅隆大学) UC Berkeley(加州大学伯克利分校) UT Austin(德克萨斯大学奥斯汀分校)

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

Comments 26 pages

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2407.11511 2025-11-04 cs.AI cs.CL cs.LG 62%

Multi-Step Reasoning with Large Language Models, a Survey

Aske Plaat, Annie Wong, Suzan Verberne, Joost Broekens, Niki van Stein, Thomas Back

机构 * LIACS(莱顿大学信息科学研究中心) Leiden University(莱顿大学)

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

Comments ACM Computing Surveys

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2511.00034 2025-11-04 cs.MA cs.LG 57%

On the Fundamental Limitations of Decentralized Learnable Reward Shaping in Cooperative Multi-Agent Reinforcement Learning

Aditya Akella

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

Comments 8 pages, 5 figures, 2 tables

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2501.16436 2025-11-04 quant-ph 50%

Taming quantum systems: A tutorial for using shortcuts-to-adiabaticity, quantum optimal control, and reinforcement learning

Callum W. Duncan, Pablo M. Poggi, Marin Bukov, Nikolaj Thomas Zinner, Steve Campbell

专题命中 模仿学习与强化学习 :manipulation(abstract)

Comments 73 pages, 15 figures. Data associated with this manuscript version are openly available on Zenodo, https://doi.org/10.5281/zenodo.17169846 ; Jupyter notebooks are available on GitHub, https://github.com/nqd-lab/quctrl-tutorial

Journal ref PRX Quantum 6, 040201 (2025)

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