机器人 / 具身智能 - arXivDaily 专题

2105.10919 2021-10-29 cs.LG cs.AI cs.RO 82%

Continual World: A Robotic Benchmark For Continual Reinforcement Learning

Maciej Wołczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś

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

Comments NeurIPS 2021

2109.00927 2021-09-03 cs.CV cs.AI cs.RO 82%

Autonomous Curiosity for Real-Time Training Onboard Robotic Agents

Ervin Teng, Bob Iannucci

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

Comments 10 pages, 9 figures. Accepted in IEEE Winter Conference on Applications of Computer Vision (WACV), 2019. arXiv admin note: text overlap with arXiv:1902.01569

Journal ref Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 1486 - 1495

2102.10447 2021-06-29 cs.LG cs.AI cs.RO 82%

Importance of Environment Design in Reinforcement Learning: A Study of a Robotic Environment

Mónika Farsang, Luca Szegletes

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

Journal ref Proceedings of the Automation and Applied Computer Science Workshop 2021

2104.06410 2021-04-15 cs.RO cs.AI cs.LG 82%

Reward Shaping with Subgoals for Social Navigation

Takato Okudo, Seiji Yamada

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

Comments arXiv admin note: substantial text overlap with arXiv:2104.06163

2103.04616 2021-03-09 cs.RO cs.AI cs.LG 82%

Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning

Marian Körber, Johann Lange, Stephan Rediske, Simon Steinmann, Roland Glück

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

2102.04916 2021-03-03 cs.LG cs.AI cs.RO 82%

rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks

Pierre Aumjaud, David McAuliffe, Francisco Javier Rodríguez Lera, Philip Cardiff

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

Comments 7 pages, 5 figures

Journal ref Software Impacts. 8 (2021) 100061

2003.04663 2021-01-08 cs.RO cs.AI cs.LG 82%

Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors

Rituraj Kaushik, Timothée Anne, Jean-Baptiste Mouret

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

Comments 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) | Video: http://tiny.cc/famle_video

2002.11952 2020-10-02 cond-mat.mes-hall cs.AI cs.LG cs.RO 82%

Autonomous robotic nanofabrication with reinforcement learning

Philipp Leinen, Malte Esders, Kristof T. Schütt, Christian Wagner, Klaus-Robert Müller, F. Stefan Tautz

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

Comments 3 figures

Journal ref Sci. Adv. 6, eabb6987 (2020)

1902.09458 2020-02-25 cs.RO cs.AI cs.LG 82%

Long-Range Indoor Navigation with PRM-RL

Anthony Francis, Aleksandra Faust, Hao-Tien Lewis Chiang, Jasmine Hsu, J. Chase Kew, Marek Fiser, Tsang-Wei Edward Lee

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

Comments Accepted to T-RO

1912.12204 2019-12-30 cs.RO cs.AI cs.LG 82%

Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data

Boyi Liu, Lujia Wang, Ming Liu, Cheng-Zhong Xu

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

Comments arXiv admin note: substantial text overlap with arXiv:1909.00895

1905.09683 2019-12-10 cs.LG cs.AI cs.RO 82%

From semantics to execution: Integrating action planning with reinforcement learning for robotic causal problem-solving

Manfred Eppe, Phuong D. H. Nguyen, Stefan Wermter

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

1909.07745 2019-09-18 cs.RO cs.CV cs.LG 82%

Adversarial Feature Training for Generalizable Robotic Visuomotor Control

Xi Chen, Ali Ghadirzadeh, Mårten Björkman, Patric Jensfelt

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

1909.00895 2019-09-17 cs.RO cs.AI cs.LG 82%

Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data

Boyi Liu, Lujia Wang, Ming Liu, Cheng-Zhong Xu

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

1805.06150 2018-09-20 cs.RO cs.AI cs.CL cs.LG 82%

FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning

Pararth Shah, Marek Fiser, Aleksandra Faust, J. Chase Kew, Dilek Hakkani-Tur

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

Comments 7 pages, 8 figures

Journal ref Third Workshop in Machine Learning in the Planning and Control of Robot Motion at ICRA, 2018

1803.07729 2018-07-27 cs.CV cs.AI cs.CL cs.RO 82%

Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation

Xin Wang, Wenhan Xiong, Hongmin Wang, William Yang Wang

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

Comments 21 pages, 7 figures, with supplementary material

1709.06917 2018-03-14 cs.RO cs.AI cs.LG cs.NE stat.ML 82%

Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics

Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret

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

Comments Accepted at ICRA 2018; 8 pages, 4 figures, 2 algorithms, 1 table; Video at https://youtu.be/HFkZkhGGzTo ; Spotlight ICRA presentation at https://youtu.be/_MZYDhfWeLc

2509.19538 2026-05-14 cs.LG cs.AI 82%

DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions

DAWM：基于扩散的世界模型用于通过动作推断的离线强化学习

Zongyue Li, Xiao Han, Yusong Li, Niklas Strauss, Matthias Schubert

机构 * Department of Computer Science, University of Munich, Munich, Germany（慕尼黑大学计算机科学系）； Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML））

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

AI总结 DAWM通过动作推断生成未来状态-奖励轨迹，结合逆动力学模型，为离线强化学习提供高效训练方案，提升算法性能。

Comments ICML2025 workshop Building Physically Plausible World Models

2602.12351 2026-02-16 cs.RO cs.CV 82%

LongNav-R1: Horizon-Adaptive Multi-Turn RL for Long-Horizon VLA Navigation

LongNav-R1: 基于水平自适应的多轮强化学习用于长周期视觉语言导航

Yue Hu, Avery Xi, Qixin Xiao, Seth Isaacson, Henry X. Liu, Ram Vasudevan, Maani Ghaffari

机构 * University of Michigan（密歇根大学）

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

AI总结 LongNav-R1通过多轮强化学习提升长周期视觉语言导航性能，实现高效样本利用和多样化导航行为。

Comments VLA, Navigation

2601.16866 2026-01-26 cs.RO cs.AI 82%

Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators

通过语义知识提升机器人操作机的深度强化学习

Lucía Güitta-López, Vincenzo Suriani, Jaime Boal, Álvaro J. López-López, Daniele Nardi

机构 * Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University（研究技术研究所（IIT）、ICAI工程学院、科利马斯天主教大学）； University of Basilicata（巴利卡塔大学）； Sapienza University of Rome（罗马萨皮恩扎大学）

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

AI总结本文提出通过整合语义知识图嵌入提升机器人操作机深度强化学习效率，实验显示学习时间减少60%且任务准确性提升15个百分点。

Journal ref Robotics, published 24 June 2025

2509.04069 2026-01-09 cs.RO cs.LG 82%

Solving Robotics Tasks with Prior Demonstration via Exploration-Efficient Deep Reinforcement Learning

通过探索高效的深度强化学习解决机器人任务中的先验示范

Chengyandan Shen, Christoffer Sloth

机构 * Unicontrol ； Maersk McKinney Møller Institute, University of Southern Denmark（马士基麦金尼·莫勒研究所，南丹麦大学）

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

AI总结本文提出DRLR框架，通过改进动作选择模块和使用SAC策略，有效缓解引导误差和防止过拟合，成功应用于真实机器人任务。

Comments This paper has been accepted for Journal publication in Frontiers in Robotics and AI

2504.18253 2025-10-28 cs.RO cs.AI 82%

Depth-Constrained ASV Navigation with Deep RL and Limited Sensing

Amirhossein Zhalehmehrabi, Daniele Meli, Francesco Dal Santo, Francesco Trotti, Alessandro Farinelli

机构 * Department of Computer Science, University of Verona（计算机科学系，威尼斯大学）

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

Comments 8 pages, 8 figures, Accepted to IEEE Robotics and Automation Letters (this is not the final version)

2409.11238 2025-05-05 cs.RO cs.LG cs.SY eess.SY 82%

Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems

Jake Welde, Nishanth Rao, Pratik Kunapuli, Dinesh Jayaraman, Vijay Kumar

机构 * GRASP Laboratory at the University of Pennsylvania（宾夕法尼亚大学GRASP实验室）

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

Comments The first three authors contributed equally to this work. This updated version reflects the final version to appear at IEEE International Conference on Robotics and Automation (ICRA) 2025

2209.11094 2024-10-28 cs.RO cs.AI 82%

Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation

Jack Saunders, Sajad Saeedi, Wenbin Li

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

Comments This work has been submitted to the IEEE International Conference on Robotics and Automation (ICRA) for possible publication

2409.03107 2024-09-06 cs.RO cs.LG cs.SY eess.SY 82%

RoboKoop: Efficient Control Conditioned Representations from Visual Input in Robotics using Koopman Operator

Hemant Kumawat, Biswadeep Chakraborty, Saibal Mukhopadhyay

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

Comments Accepted to the $8^{th}$ Conference on Robot Learning (CoRL 2024)

2407.06056 2024-08-14 cs.RO cs.AI 82%

Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot Navigation

Sara Pohland, Alvin Tan, Prabal Dutta, Claire Tomlin

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

Journal ref 2024 IEEE International Conference on Robotics and Automation (ICRA)

2202.09574 2024-02-27 cs.RO cs.AI 82%

Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer

Heecheol Kim, Yoshiyuki Ohmura, Akihiko Nagakubo, Yasuo Kuniyoshi

专题命中模仿学习与强化学习：robot policy(title)；manipulation(abstract)；分类 cs.RO、cs.AI；robotics(journal_ref)

Comments 8 pages

Journal ref IEEE Robotics and Automation Letters 8.5 (2023): 2906-2913

2109.13005 2021-09-28 cs.LG cs.HC cs.RO cs.SY eess.SY 82%

Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration

Zhaorun Chen, Binhao Chen, Shenghan Xie, Liang Gong, Chengliang Liu, Zhengfeng Zhang, Junping Zhang

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

Comments This paper is accepted at The 3rd International Symposium on Robotics & Intelligent Manufacturing Technology (ISRIMT 2021) (https://conferences.ieee.org/conferences_events/conferences/conferencedetails/53730) and nominated as the "best paper"

2011.05715 2020-11-12 cs.RO cs.AI 82%

Reinforcement Learning with Time-dependent Goals for Robotic Musicians

Thilo Fryen, Manfred Eppe, Phuong D. H. Nguyen, Timo Gerkmann, Stefan Wermter

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

Comments Preprint, submitted to IEEE Robotics and Automation Letters (RA-L) 2021 with International Conference on Robotics and Automation Conference Option (ICRA) 2021

2001.02366 2020-06-15 cs.RO cs.CV 82%

What can robotics research learn from computer vision research?

Peter Corke, Feras Dayoub, David Hall, John Skinner, Niko Sünderhauf

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

Comments 15 pages, to appear in the proceeding of the International Symposium on Robotics Research (ISRR) 2019

2003.02638 2020-03-06 cs.RO cs.LG stat.ML 82%

Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms

Marcus Ebner von Eschenbach, Binyamin Manela, Jan Peters, Armin Biess

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

Comments 8 pages, 5 figures, submitted to IEEE Robotics and Automation Letters/IROS 2020

1. 模仿学习与强化学习 4106 篇

Continual World: A Robotic Benchmark For Continual Reinforcement Learning

Autonomous Curiosity for Real-Time Training Onboard Robotic Agents

Importance of Environment Design in Reinforcement Learning: A Study of a Robotic Environment

Reward Shaping with Subgoals for Social Navigation

Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning

rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks

Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors

Autonomous robotic nanofabrication with reinforcement learning

Long-Range Indoor Navigation with PRM-RL

Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data

From semantics to execution: Integrating action planning with reinforcement learning for robotic causal problem-solving

Adversarial Feature Training for Generalizable Robotic Visuomotor Control

Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data

FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning

Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation

Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics

DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions

LongNav-R1: Horizon-Adaptive Multi-Turn RL for Long-Horizon VLA Navigation

Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators

Solving Robotics Tasks with Prior Demonstration via Exploration-Efficient Deep Reinforcement Learning

Depth-Constrained ASV Navigation with Deep RL and Limited Sensing

Leveraging Symmetry to Accelerate Learning of Trajectory Tracking Controllers for Free-Flying Robotic Systems

Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation

RoboKoop: Efficient Control Conditioned Representations from Visual Input in Robotics using Koopman Operator

Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot Navigation

Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer

Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration

Reinforcement Learning with Time-dependent Goals for Robotic Musicians

What can robotics research learn from computer vision research?

Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms