机器人 / 具身智能 - arXivDaily 专题

1509.06791 2016-02-17 cs.LG cs.RO 62%

Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search

Tianhao Zhang, Gregory Kahn, Sergey Levine, Pieter Abbeel

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

1105.1749 2015-03-18 cs.AI cs.RO cs.SE 62%

A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control

Todd Hester, Michael Quinlan, Peter Stone

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

Comments Added a reference Presents a real-time parallel architecture for model-based reinforcement learning methods

1204.2235 2012-04-11 cs.RO cs.AI cs.DL 62%

Publishing Identifiable Experiment Code And Configuration Is Important, Good and Easy

Richard Vaughan, Jens Wawerla

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

Comments 11 pages

2506.07223 2026-08-11 cs.AI 版本更新 61%

Reflex First, Reflect Later: Latency-Aware Embodied LLM Agents for Dynamic Response

先反射，后反思：面向动态响应的延迟感知具身大语言模型智能体

Yangqing Zheng, Shunqi Mao, Dingxin Zhang, Weidong Cai

机构 * School of Computer Science, The University of Sydney（计算机科学学院，悉尼大学）

专题命中模仿学习与强化学习：embodied agent(abstract)；分类 cs.AI；embodied AI(comments)

AI总结该研究针对动态环境中具身LLM智能体的推理延迟问题，提出RRARA智能体及相关评估指标，通过时间转换机制与预规划器实现决策质量与响应能力的平衡。

Comments Accepted by the CVPR 2025 Embodied AI Workshop

2607.27494 2026-07-31 cs.RO 新提交 61%

Simulation of Surgical Suturing Using Position-Based Dynamics and the Material Point Method for Robot Reinforcement Learning

基于位置动力学（PBD）和物质点法（MPM）的手术缝合模拟用于机器人强化学习

Tleukhan Mussin, Yafei Ou, Mahdi Tavakoli

机构 * University of Alberta（阿尔伯塔大学）

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

AI总结该研究提出基于PBD与MPM的缝合模拟环境，优化GPU执行并构建RL缝合子任务环境，训练的RL智能体在进针、拔针任务中分别达到80%、68%的成功率。

Comments 7 pages, 9 figures, accepted for the IEEE RAS/EMBS 11th International Conference on Biomedical Robotics and Biomechatronics (BioRob 2026)

2602.05608 2026-06-16 cs.RO 版本更新 61%

HiCrowd: Hierarchical Crowd Flow Alignment for Dense Human Environments

HiCrowd：密集人群环境中的分层人群流对齐

Yufei Zhu, Shih-Min Yang, Martin Magnusson, Allan Wang

机构 * Robot Navigation and Perception Lab, AASS Research Center, Örebro University, Sweden（奥雷布罗大学机器人导航与感知实验室，AASS研究中心，瑞典）； Miraikan – The National Museum of Emerging Science and Innovation, Japan（日本新兴科学与创新国家博物馆——Miraikan）

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

AI总结提出HiCrowd分层框架，结合强化学习与模型预测控制，通过跟随人群流解决机器人冻结问题，在真实和合成数据集上提升导航效率与安全性。

Comments 2026 IEEE International Conference on Robotics and Automation (ICRA)

2509.16136 2026-06-09 cs.RO 版本更新 61%

Reward Evolution with Graph-of-Thoughts: A Bi-Level Language Model Framework for Reinforcement Learning

基于思维图的奖励进化：一种用于强化学习的双层语言模型框架

Changwei Yao, Xinzi Liu, Chen Li, Marios Savvides

机构 * Carnegie Mellon University（卡内基梅隆大学）； University of Tokyo（东京大学）

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

AI总结本文提出RE-GoT框架，结合LLM与VLM的图思维推理，通过任务分解和视觉反馈迭代优化奖励函数，实验表明在RoboGen和ManiSkill2任务中均优于现有方法。

Journal ref IEEE International Conference on Robotics and Automation (ICRA 2026)

2507.22345 2026-05-25 cs.RO 61%

A Reconfigured Wheel-Legged Robot for Enhanced Steering and Adaptability

一种增强转向能力和适应性的重构轮腿机器人

Zhicheng Song, Jinglan Xu, Chunxin Zheng, Yulin Li, Zhihai Bi, Jun Ma

机构 * Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou)（机器人与自主系统方向，香港科技大学（广州））

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

AI总结提出一种名为FLORES的新型轮腿机器人，通过将前腿的髋关节横滚自由度替换为偏航自由度，并设计定制强化学习控制器，实现了高效转向和多地形适应。

Journal ref IEEE Robotics and Automation Letters, vol. 11, no. 6, pp. 7444-7451, June 2026

2512.08230 2026-04-16 cs.AI 61%

Empowerment Gain and Causal Model Construction: Children and adults are sensitive to controllability and variability in their causal interventions

赋能增益与因果模型构建：儿童和成人对干预可控性和变异性敏感

Eunice Yiu, Kelsey Allen, Shiry Ginosar, Alison Gopnik

机构 * Department of Psychology, University of California, Berkeley（加州大学伯克利分校心理学系）； Department of Computer Science, University of British Columbia（不列颠哥伦比亚大学计算机科学系）； Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）

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

AI总结研究探讨了赋能增益在因果学习中的作用，通过实验验证儿童和成人如何利用赋能信号推断因果关系并设计干预措施。

Comments Accepted to Philosophical Transactions A, Special issue: World models, AGI, and the hard problems of life-mind continuity. Expected publication in 2026

2604.06943 2026-04-09 cs.RO 61%

Sustainable Transfer Learning for Adaptive Robot Skills

可持续的迁移学习用于适应性机器人技能

Khalil Abuibaid, Vinit Hegiste, Nigora Gafur, Achim Wagner, Martin Ruskowski

机构 * Chair of Machine Tools and Control System, RPTU University Kaiserslautern-Landau（机床与控制系统教席，莱茵兰-普法尔茨凯泽斯劳滕-兰道大学）； Innovative Factory Systems, German Institute of Artificial Intelligence（创新工厂系统，德国人工智能研究所）

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

AI总结本文研究了不同机器人平台间的策略迁移，通过强化学习完成peg-in-hole任务，探讨零样本迁移、微调和从头训练的效果，发现微调能显著提升性能，降低训练时间，支持可持续的机器人学习。

Comments Published in RAAD 2025 (Springer). 7 pages, 5 figures

Journal ref Advances in Service and Industrial Robotics, RAAD 2025, Springer, 2025, pp. 389-397

2011.01882 2026-04-07 cs.RO cs.GT 61%

Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning

通过模型无关强化学习实现对隐蔽攻击的安全规划

Alper Kamil Bozkurt, Yu Wang, Miroslav Pajic

机构 * Duke University（杜克大学）

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

AI总结本文提出利用模型无关强化学习在未知随机环境中实现安全规划，通过将攻击者与控制器视为博弈双方，以线性时序逻辑公式表达其目标，解决在未知环境中满足LTL公式的问题。

Journal ref 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, 2021, pp. 10656-10662

2603.23182 2026-03-25 cs.RO cs.SY eess.SY 61%

Path Planning and Reinforcement Learning-Driven Control of On-Orbit Free-Flying Multi-Arm Robots

轨道自由飞行多臂机器人的路径规划与强化学习驱动控制

Álvaro Belmonte-Baeza, José Luis Ramón, Leonard Felicetti, Miguel Cazorla, Jorge Pomares

机构 * University of Alicante（阿利坎特大学）； Cranfield University（克兰菲尔德大学）

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

AI总结本文提出一种结合轨迹优化与强化学习的混合方法，用于自由飞行多臂机器人在在轨服务场景中的路径规划与控制。通过实验验证，该方法在表面运动和自由浮动场景中均优于传统策略，提升了运动平滑度、安全性和效率。

Comments Accepted for publication in The International Journal of Robotics Research (23-Mar-2026)

2603.07800 2026-03-10 cs.RO 61%

Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing

基于偏好条件的强化学习用于空间时间高效的在线3D装箱

Nikita Sarawgi, Omey M. Manyar, Fan Wang, Thinh H. Nguyen, Daniel Seita, Satyandra K. Gupta

机构 * Viterbi School of Engineering, University of Southern California（美国南加州大学维特比工程学院）； Amazon Robotics（亚马逊机器人）

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

AI总结 STEP方法通过偏好条件强化学习，在保持装箱密度的同时将操作时间减少44%。

Comments 8 pages, 5 figures. Accepted to IEEE International Conference on Robotics and Automation 2026. Project Website: https://step-packing.github.io

2512.13514 2025-12-16 cs.RO 61%

Reinforcement Learning based 6-DoF Maneuvers for Microgravity Intravehicular Docking: A Simulation Study with Int-Ball2 in ISS-JEM

基于强化学习的6自由度微重力舱内对接 maneuver：Int-Ball2在ISS-JEM中的仿真研究

Aman Arora, Matteo El-Hariry, Miguel Olivares-Mendez

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

AI总结本文提出基于强化学习的六自由度微重力舱内对接方法，通过仿真研究Int-Ball2机器人在ISS-JEM中的对接性能，验证了其在复杂环境下的稳定性和可靠性。

Comments Presented at AI4OPA Workshop at the International Conference on Space Robotics (iSpaRo) 2025 at Sendai, Japan

2511.15358 2025-11-20 cs.RO 61%

Platform-Agnostic Reinforcement Learning Framework for Safe Exploration of Cluttered Environments with Graph Attention

Gabriele Calzolari, Vidya Sumathy, Christoforos Kanellakis, George Nikolakopoulos

机构 * Robotics and AI Group, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology（机器人与人工智能组，计算机科学、电气与空间工程系，吕勒奥技术大学）

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

Comments 8 pages, 6 figures, submitted to the 2026 IEEE International Conference on Robotics & Automation

2505.02293 2025-10-09 cs.RO cs.MA cs.SY eess.SY 61%

Resolving Conflicting Constraints in Multi-Agent Reinforcement Learning with Layered Safety

Jason J. Choi, Jasmine Jerry Aloor, Jingqi Li, Maria G. Mendoza, Hamsa Balakrishnan, Claire J. Tomlin

机构 * University of California, Berkeley（加州大学伯克利分校）； Massachusetts Institute of Technology（麻省理工学院）

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

Comments Accepted for publication at the 2025 Robotics: Science and Systems Conference. 18 pages, 8 figures

2509.20095 2025-09-25 cs.AI 61%

From Pheromones to Policies: Reinforcement Learning for Engineered Biological Swarms

Aymeric Vellinger, Nemanja Antonic, Elio Tuci

机构 * Department of Computer Science, University of Namur（南姆大学计算机科学系）

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

Comments Contribution to the 9th International Symposium on Swarm Behavior and Bio-Inspired Robotics 2025

2505.16084 2025-09-01 cs.RO 61%

Motion Priors Reimagined: Adapting Flat-Terrain Skills for Complex Quadruped Mobility

Zewei Zhang, Chenhao Li, Takahiro Miki, Marco Hutter

机构 * Department of Mechanical Engineering, EPFL（瑞士联邦理工学院机械工程系）； ETH AI Center, ETH Zurich（苏黎世联邦理工学院人工智能中心）； Robotic Systems Lab, ETH Zurich（苏黎世联邦理工学院机器人系统实验室）

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

Comments Conference on Robot Learning (CoRL)

2502.03822 2025-04-29 cs.RO 61%

Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible Training

Xiatao Sun, Shuo Yang, Yinxing Chen, Francis Fan, Yiyan Liang, Daniel Rakita

机构 * Yale University（耶鲁大学）； University of Pennsylvania（宾夕法尼亚大学）

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

Comments Accepted to Robotics: Science and Systems (RSS) 2025

2403.09583 2025-04-18 cs.RO 61%

ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models

Runyu Ma, Jelle Luijkx, Zlatan Ajanovic, Jens Kober

机构 * Delft University of Technology（代尔夫特理工大学）； RWTH Aachen University（亚琛工业大学）

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

Comments 6 pages, 6 figures, IEEE International Conference on Robotics and Automation (ICRA) 2025

2406.09120 2025-03-11 cs.RO 61%

Imitation Learning-based Direct Visual Servoing using the Large Projection Formulation

Sayantan Auddy, Antonio Paolillo, Justus Piater, Matteo Saveriano

机构 * University of Innsbruck（因斯布鲁克大学）； Dalle Molle Institute for Artificial Intelligence (IDSIA)（达勒·莫勒人工智能研究所（IDSIA））； University of Trento（特伦托大学）

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

Comments To appear in Robotics and Autonomous Systems. First two authors contributed equally

2409.15634 2025-02-25 cs.RO 61%

NavRL: Learning Safe Flight in Dynamic Environments

Zhefan Xu, Xinming Han, Haoyu Shen, Hanyu Jin, Kenji Shimada

机构 * Carnegie Mellon University（卡内基梅隆大学）

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

Comments 8 pages, 9 figures, 3 tables. Experiment video: https://youtu.be/EbeJW8-YlvI. Github Repo: https://github.com/Zhefan-Xu/NavRL

Journal ref IEEE Robotics and Automation Letters, 2025

2401.14554 2025-02-10 cs.RO math.OC 61%

GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control

Songyuan Zhang, Oswin So, Kunal Garg, Chuchu Fan

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

Comments 20 pages, 15 figures; Accepted by IEEE Transactions on Robotics (T-RO)

2412.09466 2024-12-13 cs.RO 61%

Distributional Reinforcement Learning based Integrated Decision Making and Control for Autonomous Surface Vehicles

Xi Lin, Paul Szenher, Yewei Huang, Brendan Englot

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

Comments IEEE Robotics and Automation Letters (RA-L)

2410.14117 2024-10-21 cs.RO 61%

MarineGym: Accelerated Training for Underwater Vehicles with High-Fidelity RL Simulation

Shuguang Chu, Zebin Huang, Mingwei Lin, Dejun Li, Ignacio Carlucho

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

Comments Accepted by the 40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40)

2410.06401 2024-10-10 cs.RO 61%

Trajectory Improvement and Reward Learning from Comparative Language Feedback

Zhaojing Yang, Miru Jun, Jeremy Tien, Stuart J. Russell, Anca Dragan, Erdem Bıyık

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

Comments 8th Annual Conference of Robot Learning (2024)

2309.11124 2024-08-29 cs.RO cs.SY eess.SY 61%

Receding-Constraint Model Predictive Control using a Learned Approximate Control-Invariant Set

Gianni Lunardi, Asia La Rocca, Matteo Saveriano, Andrea Del Prete

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

Comments 7 pages, 3 figures, 3 tables, 2 pseudo-algo, conference

Journal ref "Receding-Constraint Model Predictive Control using a Learned Approximate Control-Invariant Set," 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan, 2024, pp. 11626-11632

2405.04082 2024-07-18 cs.RO 61%

Logic-Skill Programming: An Optimization-based Approach to Sequential Skill Planning

Teng Xue, Amirreza Razmjoo, Suhan Shetty, Sylvain Calinon

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

Comments In Proc. Robotics: Science and Systems (RSS), 2024

2402.17768 2024-06-06 cs.RO cs.AI cs.CV cs.LG 61%

Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning

Xiaoyu Zhang, Matthew Chang, Pranav Kumar, Saurabh Gupta

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

Comments Accepted by Robotics: Science and Systems (RSS) 2024. project website with video, see https://sites.google.com/view/diffusion-meets-dagger

2308.16874 2024-04-09 cs.RO 61%

D-VAT: End-to-End Visual Active Tracking for Micro Aerial Vehicles

Alberto Dionigi, Simone Felicioni, Mirko Leomanni, Gabriele Costante

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

Journal ref IEEE Robotics and Automation Letters 2024

1. 模仿学习与强化学习 4134 篇

Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search

A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control

Publishing Identifiable Experiment Code And Configuration Is Important, Good and Easy

Reflex First, Reflect Later: Latency-Aware Embodied LLM Agents for Dynamic Response

Simulation of Surgical Suturing Using Position-Based Dynamics and the Material Point Method for Robot Reinforcement Learning

HiCrowd: Hierarchical Crowd Flow Alignment for Dense Human Environments

Reward Evolution with Graph-of-Thoughts: A Bi-Level Language Model Framework for Reinforcement Learning

A Reconfigured Wheel-Legged Robot for Enhanced Steering and Adaptability

Empowerment Gain and Causal Model Construction: Children and adults are sensitive to controllability and variability in their causal interventions

Sustainable Transfer Learning for Adaptive Robot Skills

Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning

Path Planning and Reinforcement Learning-Driven Control of On-Orbit Free-Flying Multi-Arm Robots

Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing

Reinforcement Learning based 6-DoF Maneuvers for Microgravity Intravehicular Docking: A Simulation Study with Int-Ball2 in ISS-JEM

Platform-Agnostic Reinforcement Learning Framework for Safe Exploration of Cluttered Environments with Graph Attention

Resolving Conflicting Constraints in Multi-Agent Reinforcement Learning with Layered Safety

From Pheromones to Policies: Reinforcement Learning for Engineered Biological Swarms

Motion Priors Reimagined: Adapting Flat-Terrain Skills for Complex Quadruped Mobility

Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible Training

ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models

Imitation Learning-based Direct Visual Servoing using the Large Projection Formulation

NavRL: Learning Safe Flight in Dynamic Environments

GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control

Distributional Reinforcement Learning based Integrated Decision Making and Control for Autonomous Surface Vehicles

MarineGym: Accelerated Training for Underwater Vehicles with High-Fidelity RL Simulation

Trajectory Improvement and Reward Learning from Comparative Language Feedback

Receding-Constraint Model Predictive Control using a Learned Approximate Control-Invariant Set

Logic-Skill Programming: An Optimization-based Approach to Sequential Skill Planning

Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning

D-VAT: End-to-End Visual Active Tracking for Micro Aerial Vehicles