机器人 / 具身智能

Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning

Yuexin Wu, Xiujun Li, Jingjing Liu, Jianfeng Gao, Yiming Yang

专题命中 机器人学习 ：world model(abstract)；分类 cs.AI、cs.LG

Comments 8 pages, 9 figures, AAAI 2019

State Representation Learning for Control: An Overview

Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou, David Filliat

专题命中 机器人学习 ：robotics(abstract)；分类 cs.AI、cs.LG

Learning Task Specifications from Demonstrations

Marcell Vazquez-Chanlatte, Susmit Jha, Ashish Tiwari, Mark K. Ho, Sanjit A. Seshia

专题命中 机器人学习 ：robotics(abstract)；分类 cs.AI、cs.LG

Comments NIPS 2018

Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze

Diego Romeres, Devesh Jha, Alberto Dalla Libera, William Yerazunis, Daniel Nikovski

专题命中 机器人学习 ：robot learning(abstract)；分类 cs.RO、cs.LG

Comments 7 pages including the references, 5 figures. Changed title, improved the structure of the article and the images

Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robots

Xin Ye, Zhe Lin, Haoxiang Li, Shibin Zheng, Yezhou Yang

专题命中 机器人学习 ：navigation(abstract)；分类 cs.RO、cs.AI

Comments 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Kam-Fai Wong, Shang-Yu Su

专题命中 机器人学习 ：world model(abstract)；分类 cs.AI、cs.LG

Comments 11 pages, 8 figures, Accepted in ACL 2018

Distributed Distributional Deterministic Policy Gradients

Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy Lillicrap

专题命中 机器人学习 ：manipulation(abstract)；分类 cs.AI、cs.LG

Exploring Implicit Human Responses to Robot Mistakes in a Learning from Demonstration Task

Cory J. Hayes, Maryam Moosaei, Laurel D. Riek

专题命中 机器人学习 ：robot learning(abstract)；分类 cs.RO、cs.AI

Comments 7 pages, 2 figures, IEEE RO-MAN 2016, IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2016)

Co-Creating Buildable and Open Social Robot Study Companions with University Students

与大学生共同创造可构建且开放的社会机器人学习伙伴

Farnaz Baksh, Matevž B. Zorec, Feiazie Baksh, Karl Kruusamäe

机构 * University of Tartu（塔尔图大学） ； University of Guyana Robotics Club（圭亚那大学机器人俱乐部） 纠错

专题命中 机器人学习 ：navigation(abstract)；分类 cs.RO；robotics(comments)

AI总结 针对开源机器人构建门槛高的问题，采用双钻石框架与大学生共同设计机器人学习伴侣v4.1，通过扭锁、卡扣等可装配/拆卸设计，将系统可用性从差提升至优（SUS 59.4→89.4），并降低感知工作负荷。

Comments Accepted for 18th International Conference on Social Robotics (ICSR + ART 2026), London, UK | 1-4 July 2026

Near-Optimal Belief Space Planning via T-LQG

通过T-LQG实现接近最优的信念空间规划

Mohammadhussein Rafieisakhaei, Suman Chakravorty, P. R. Kumar

专题命中 机器人学习 ：robotics(abstract,comments)；分类 cs.RO

AI总结 本文提出T-LQG方法，用于非线性机器人系统在观测和运动不确定性下的规划问题，提供近优反馈控制策略，解决POMDP问题。

Comments 3 pages, 3 figures, In Robotics: Science and Systems (RSS) 2017 Workshop of "POMDPs in Robotics: State of The Art, Challenges, and Opportunities"

Learning Compositional Symbolic Task Rules from Demonstrations with Inductive Logic Programming

通过归纳逻辑编程从演示中学习组合符号任务规则

Oleh Borys, Karla Stepanova

机构 * Czech Institute of Informatics, Robotics and Cybernetics（捷克信息学、机器人学与自动控制研究所） 纠错

专题命中 机器人学习 ：robotic(abstract)；分类 cs.RO；robotics(comments)

AI总结 提出一种基于归纳逻辑编程的分解学习方法，从演示中学习可解释、可重用且支持强泛化的符号任务规则。

Comments In: ICRA 2026 Workshop on Semantics for Reliable Robot Autonomy: From Environment Understanding and Reasoning to Safe Interaction, Vienna, 2026 In: ICRA 2026, International Joint Workshop on Ontologies, Semantic Maps and Autonomous Robotics Standardization (J-WOSMARS 2026), Vienna, 2026

The Temporal Trap: Entanglement in Pre-Trained Visual Representations for Visuomotor Policy Learning

Nikolaos Tsagkas, Andreas Sochopoulos, Duolikun Danier, Chris Xiaoxuan Lu, Oisin Mac Aodha

专题命中 机器人学习 ：分类 cs.RO、cs.AI、cs.CV；robot learning(comments)

Comments This submission replaces our earlier work "When Pre-trained Visual Representations Fall Short: Limitations in Visuo-Motor Robot Learning." The original paper was split into two studies; this version focuses on temporal entanglement in pre-trained visual representations. The companion paper is "Attentive Feature Aggregation."

A Talent-infused Policy-gradient Approach to Efficient Co-Design of Morphology and Task Allocation Behavior of Multi-Robot Systems

Prajit KrisshnaKumar, Steve Paul, Souma Chowdhury

专题命中 机器人学习 ：robot policy(abstract)；分类 cs.RO；robotic(comments)

Comments Presented in proceedings of the International Symposium on Distributed Autonomous Robotic Systems (DARS) 2024

Aligning Human Intent from Imperfect Demonstrations with Confidence-based Inverse soft-Q Learning

Xizhou Bu, Wenjuan Li, Zhengxiong Liu, Zhiqiang Ma, Panfeng Huang

专题命中 机器人学习 ：manipulation(abstract)；分类 cs.RO；robotics(journal_ref)

Comments Our code see https://github.com/XizoB/CIQL

Journal ref IEEE Robotics and Automation Letters, vol. 9, no. 8, pp. 7150 - 7157, Aug. 2024

LEMURS: Learning Distributed Multi-Robot Interactions

Eduardo Sebastian, Thai Duong, Nikolay Atanasov, Eduardo Montijano, Carlos Sagues

专题命中 机器人学习 ：navigation(abstract)；分类 cs.RO；robotics(comments)

Comments Accepted for publication at IEEE International Conference on Robotics and Automation 2023

Guided Learning from Demonstration for Robust Transferability

Fouad Sukkar, Victor Hernandez Moreno, Teresa Vidal-Calleja, Jochen Deuse

专题命中 机器人学习 ：robotic(abstract)；分类 cs.RO；robotics(comments)

Comments 7 Pages, 7 Figures, accepted to the 2023 IEEE International Conference on Robotics and Automation (ICRA)

Ex-DoF: Expansion of Action Degree-of-Freedom with Virtual Camera Rotation for Omnidirectional Image

Kosuke Tahara, Noriaki Hirose

专题命中 机器人学习 ：robotic(abstract)；分类 cs.RO；robotics(comments)

Comments 8 pages, 9 figures, 2 tables, IEEE International Conference on Robotics and Automation (ICRA2022)

Lyapunov-stable neural-network control

Hongkai Dai, Benoit Landry, Lujie Yang, Marco Pavone, Russ Tedrake

专题命中 机器人学习 ：robotics(abstract,comments)；分类 cs.RO

Comments Published at Robotics: Science and Systems (RSS) in July, 2021

Ergodic imitation: Learning from what to do and what not to do

Aleksandra Kalinowska, Ahalya Prabhakar, Kathleen Fitzsimons, Todd Murphey

专题命中 机器人学习 ：robotics(abstract,journal_ref)；分类 cs.RO

Comments Kalinowska and Prabhakar contributed equally to this work

Journal ref International Conference on Robotics and Automation, 2021

Learning Certified Control using Contraction Metric

Dawei Sun, Susmit Jha, Chuchu Fan

专题命中 机器人学习 ：robotic(abstract)；分类 cs.RO；robot learning(comments)

Comments Accepted to Conference on Robot Learning (CoRL) 2020

Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances

Vitor Guizilini, Jie Li, Rares Ambrus, Sudeep Pillai, Adrien Gaidon

专题命中 机器人学习 ：robotic(abstract)；分类 cs.CV；robot learning(comments)

Comments Conference on Robot Learning (CoRL 2019)

Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performance

M. Asif Rana, Daphne Chen, S. Reza Ahmadzadeh, Jacob Williams, Vivian Chu, Sonia Chernova

专题命中 机器人学习 ：manipulation(abstract)；分类 cs.RO；robotics(comments)

Comments 8 pages, 8 figures, submitted to IEEE Robotics and Automation Letters, videos and website can be found at https://sites.google.com/view/rail-lfd

Multi-Objective Policy Generation for Multi-Robot Systems Using Riemannian Motion Policies

Anqi Li, Mustafa Mukadam, Magnus Egerstedt, Byron Boots

专题命中 机器人学习 ：robotic(abstract)；分类 cs.RO；robotics(comments)

Comments The International Symposium on Robotics Research (ISRR), 2019

Learning to serve: an experimental study for a new learning from demonstrations framework

Okan Koc, Jan Peters

专题命中 机器人学习 ：robotics(abstract,journal_ref)；分类 cs.RO

Journal ref IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 1784-1791, April 2019. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8630019&isnumber=8581687

Prioritized Optimal Control

Andrea Del Prete, Francesco Romano, Lorenzo Natale, Giorgio Metta, Giulio Sandini, Francesco Nori

专题命中 机器人学习 ：robotics(abstract,comments)；分类 cs.RO

Comments Pre-print of the paper presented at Robotics and Automation (ICRA), IEEE International Conference on, Hong Kong, China, 2014

Interactive incremental learning of generalizable skills with local trajectory modulation

Markus Knauer, Alin Albu-Schäffer, Freek Stulp, João Silvério

专题命中 机器人学习 ：robotics(comments,journal_ref)；分类 cs.RO、cs.AI、cs.LG

Comments Accepted at IEEE Robotics and Automation Letters (RA-L), 16 pages, 19 figures, 6 tables. See https://github.com/DLR-RM/interactive-incremental-learning for further information and video

Journal ref IEEE Robotics and Automation Letters (RA-L). Volume: 10, Issue: 4, April 2025, Pages 3398 - 3405

GlanceWAM: Sparse Test-Time Imagination for World-Action Models

GlanceWAM：面向世界-动作模型的稀疏测试时想象方法

Linhan Wang, Zijian An, Mingyuan Zhang, Chen Dai, Yi Xu, Can Cui, Zichong Yang, Yinlin Chen, Lifeng Zhou, Chang-Tien Lu

机构 * Virginia Tech（弗吉尼亚理工大学） ； Drexel University（卓克索大学） ； Northeastern University（东北大学） ； Purdue University（普渡大学） 纠错

专题命中 机器人学习 ：robot learning(abstract)；分类 cs.CV

AI总结 该研究提出GlanceWAM，通过异步解耦视频DiT的想象与控制，打破世界-动作模型的速度-成功率困境，在RoboCasa、LIBERO基准测试中表现优异，推理速度达48ms/块。

On the Capability Separation Between World-Model Policy Learning and Imitated World-Action Models

世界模型策略学习与模仿世界-动作模型之间的能力分离

Yang Yu

机构 * Nanjing University（南京大学） 纠错

专题命中 机器人学习 ：world model(abstract)；分类 cs.LG

AI总结 该研究对比了直接行为克隆策略等三类策略，明确世界-动作模型学习与直接行为克隆的能力差异，指出观测演示无法识别动作效果，干预可实现零遗憾值。

A Latency-Aware Framework for Visuomotor Policy Learning on Industrial Robots

面向工业机器人视觉-运动策略学习的延迟感知框架

Daniel Ruan, Salma Mozaffari, Sigrid Adriaenssens, Arash Adel

机构 * Princeton University（普林斯顿大学） 纠错

专题命中 机器人学习 ：robotic(abstract)；分类 cs.RO

AI总结 本文提出了一种面向工业机器人视觉-运动策略学习的延迟感知框架，通过优化执行策略以应对延迟问题，提升策略在现实环境中的可靠性和稳定性。

Hand-Object Interaction in the Age of Large Foundation Models:Reconstruction, Generation, and Embodied Transfer

大基础模型时代的手物交互：重建、生成与具身迁移

Weiquan Lin, Yu Deng, Shiyang Liu, Luping Xiao, Xu Tang, Junzhi Yu, Jiaolong Yang, Lei Zhang, Xingyu Chen

专题命中 机器人学习 ：robot learning(abstract)；分类 cs.CV

AI总结 本综述系统梳理大基础模型在手物交互（HOI）领域的应用，建立基础模型子先验分类，分析其在HOI任务与机器人学习中的作用，总结数据集与评估协议并展望未来方向。