机器人 / 具身智能 - arXivDaily 专题

2210.06650 2023-11-14 cs.LG cs.AI cs.NE cs.RO stat.ML 67%

Interpreting Neural Policies with Disentangled Tree Representations

Tsun-Hsuan Wang, Wei Xiao, Tim Seyde, Ramin Hasani, Daniela Rus

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

2308.09733 2023-08-22 cs.LG cs.AI cs.RO 67%

Intrinsically Motivated Hierarchical Policy Learning in Multi-objective Markov Decision Processes

Sherif Abdelfattah, Kathryn Merrick, Jiankun Hu

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

2206.14802 2022-06-30 cs.RO cs.AI cs.LG 67%

Visual Foresight With a Local Dynamics Model

Colin Kohler, Robert Platt

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

2203.08715 2022-03-17 cs.RO cs.AI cs.LG cs.SY eess.SY math.DS 67%

Multiscale Sensor Fusion and Continuous Control with Neural CDEs

Sumeet Singh, Francis McCann Ramirez, Jacob Varley, Andy Zeng, Vikas Sindhwani

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

Comments Submitted to IEEE IROS 2022

2108.01034 2021-11-17 cs.RO cs.AI cs.LG 67%

An Efficient Image-to-Image Translation HourGlass-based Architecture for Object Pushing Policy Learning

Marco Ewerton, Angel Martínez-González, Jean-Marc Odobez

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

Comments 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2007.07793 2020-07-16 cs.RO cs.AI cs.LG cs.SY eess.SY 67%

Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors

Aditya M. Deshpande, Rumit Kumar, Ali A. Minai, Manish Kumar

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

Comments 10 pages, 8 figures, Accepted in Dynamic Systems and Control Conference (https://event.asme.org/DSCC)

2005.11810 2020-05-26 cs.RO cs.AI cs.LG 67%

Learning visual servo policies via planner cloning

Ulrich Viereck, Kate Saenko, Robert Platt

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

1909.06933 2019-09-17 cs.RO cs.CV cs.LG 67%

Self-Supervised Correspondence in Visuomotor Policy Learning

Peter Florence, Lucas Manuelli, Russ Tedrake

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

Comments Video at: https://sites.google.com/view/visuomotor-correspondence

1908.10398 2019-08-29 cs.AI cs.LG cs.RO 67%

A Data-Efficient Deep Learning Approach for Deployable Multimodal Social Robots

Heriberto Cuayáhuitl

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

1612.01746 2019-05-29 cs.LG cs.AI cs.RO stat.ML 67%

Factored Contextual Policy Search with Bayesian Optimization

Peter Karkus, Andras Kupcsik, David Hsu, Wee Sun Lee

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

Comments BayesOpt 2016, NeurIPS Workshop. A full paper extension is available at arXiv:1904.11761

1810.10191 2019-03-11 cs.RO cs.AI cs.LG 67%

Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

Michelle A. Lee, Yuke Zhu, Krishnan Srinivasan, Parth Shah, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg

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

Comments ICRA 2019

1806.07123 2018-06-20 cs.MA 67%

Cooperative Queuing Policies for Effective Human-Multi-Robot Interaction

Masoume M. Raeissi, Alessandro Farinelli

专题命中机器人学习：robot learning(abstract)；robotic(abstract)

1703.02899 2026-06-04 cs.LG cs.RO cs.SY eess.SY stat.ML 66%

Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers

基于模型的策略搜索用于多变量PID控制器的自动调优

Andreas Doerr, Duy Nguyen-Tuong, Alonso Marco, Stefan Schaal, Sebastian Trimpe

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

AI总结本文提出基于模型的策略搜索框架，用于自动调优多变量PID控制器，通过数据驱动的方法解决复杂系统的控制器调优问题。

Comments Accepted final version to appear in 2017 IEEE International Conference on Robotics and Automation (ICRA)

2209.11908 2025-05-29 cs.LG cs.RO 66%

Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations

Letian Chen, Sravan Jayanthi, Rohan Paleja, Daniel Martin, Viacheslav Zakharov, Matthew Gombolay

机构 * Georgia Institute of Technology（佐治亚理工学院）

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

Journal ref Proceedings of Conference on Robot Learning (CoRL) 2022

2412.15166 2024-12-20 cs.RO cs.AI 66%

Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration

Junjia Liu, Zhuo Li, Minghao Yu, Zhipeng Dong, Sylvain Calinon, Darwin Caldwell, Fei Chen

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

Comments 9 pages, 8 figures. Accepted by IEEE Robotics and Automation Magazine

2402.08424 2024-10-28 cs.RO cs.LG 66%

Conditional Neural Expert Processes for Learning Movement Primitives from Demonstration

Yigit Yildirim, Emre Ugur

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

Comments This work has been submitted to the IEEE RA-L for possible publication. Submitted to Robotics and Automation Letters on July 5, 2024

2302.00171 2023-11-02 cs.RO cs.LG cs.SY eess.SY math.OC 66%

Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach

Haimin Hu, David Isele, Sangjae Bae, Jaime F. Fisac

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

Comments The International Journal of Robotics Research. arXiv admin note: text overlap with arXiv:2202.07720

2307.06135 2023-09-29 cs.RO cs.AI 66%

SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning

Krishan Rana, Jesse Haviland, Sourav Garg, Jad Abou-Chakra, Ian Reid, Niko Suenderhauf

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

Comments Accepted for oral presentation at the Conference on Robot Learning (CoRL), 2023. Project page can be found here: https://sayplan.github.io

2306.09211 2023-06-16 cs.LG cs.RO 66%

A Framework for Learning from Demonstration with Minimal Human Effort

Marc Rigter, Bruno Lacerda, Nick Hawes

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

Comments Preprint version of IEEE Robotics and Automation Letters paper

2201.12813 2023-01-30 cs.CV cs.AI 66%

Contrastive Learning from Demonstrations

André Correia, Luís A. Alexandre

专题命中机器人学习：robotic(abstract,journal_ref)；分类 cs.AI、cs.CV

Journal ref IEEE Robotic Computing, Naples, Italy, December 5-7, 2022

2212.02587 2022-12-07 cs.RO cs.AI cs.SY eess.SY 66%

Learning Sampling Distributions for Model Predictive Control

Jacob Sacks, Byron Boots

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

Comments Accepted at the Conference on Robot Learning (CoRL), 2022. Main paper is 9 pages with 4 figures. Appendix is 12 pages with 11 figures and 1 table

2210.07015 2022-10-14 cs.RO cs.AI 66%

Augmentation for Learning From Demonstration with Environmental Constraints

Xing Li, Manuel Baum, Oliver Brock

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

Comments Submitted to 2023 IEEE International Conference on Robotics and Automation (ICRA)

2210.03512 2022-10-10 cs.LG cs.RO 66%

Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes

Joe Watson, Jan Peters

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

Comments 43 pages, 37 figures. Conference on Robot Learning 2022

2011.15014 2022-08-08 cs.RO cs.LG cs.SY eess.SY 66%

Learning from Human Directional Corrections

Wanxin Jin, Todd D. Murphey, Zehui Lu, Shaoshuai Mou

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

Comments This is a preprint. The published version can be accessed at IEEE Transactions on Robotics

2206.14998 2022-07-04 cs.RO cs.AI 66%

Understanding Physical Effects for Effective Tool-use

Zeyu Zhang, Ziyuan Jiao, Weiqi Wang, Yixin Zhu, Song-Chun Zhu, Hangxin Liu

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

Journal ref IEEE Robotics and Automation Letters, 2022

2004.00116 2020-07-17 cs.RO cs.LG 66%

APPLD: Adaptive Planner Parameter Learning from Demonstration

Xuesu Xiao, Bo Liu, Garrett Warnell, Jonathan Fink, Peter Stone

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

Comments Accepted by Robotics and Automation Letters (RAL) and International Conference on Intelligent Robots and Systems (IROS) 2020

1912.12270 2020-06-30 cs.CV cs.LG eess.IV 66%

Combining Deep Learning and Verification for Precise Object Instance Detection

Siddharth Ancha, Junyu Nan, David Held

专题命中机器人学习：robotic(abstract)；分类 cs.CV、cs.LG；robot learning(journal_ref)

Comments 9 pages main paper, 2 pages references, 10 pages supplementary material

Journal ref Conference on Robot Learning (CoRL), 2019

1910.03003 2020-04-23 cs.LG cs.RO cs.SY eess.SY stat.ML 66%

Stochastic Optimal Control as Approximate Input Inference

Joe Watson, Hany Abdulsamad, Jan Peters

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

Comments Conference on Robot Learning (CoRL 2019)

1909.04121 2019-12-17 cs.LG cs.AI stat.ML 66%

AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers

Andrey Kurenkov, Ajay Mandlekar, Roberto Martin-Martin, Silvio Savarese, Animesh Garg

专题命中机器人学习：robotic(abstract)；分类 cs.AI、cs.LG；robot learning(comments)

Comments Conference on Robot Learning (CoRL) 2019

1907.13627 2019-10-08 cs.RO cs.LG 66%

Disentangled Relational Representations for Explaining and Learning from Demonstration

Yordan Hristov, Daniel Angelov, Michael Burke, Alex Lascarides, Subramanian Ramamoorthy

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

Comments 15 pages, 12 figures, accepted at the Conference on Robot Learning (CoRL) 2019, Osaka, Japan

1. 机器人学习 858 篇

Interpreting Neural Policies with Disentangled Tree Representations

Intrinsically Motivated Hierarchical Policy Learning in Multi-objective Markov Decision Processes

Visual Foresight With a Local Dynamics Model

Multiscale Sensor Fusion and Continuous Control with Neural CDEs

An Efficient Image-to-Image Translation HourGlass-based Architecture for Object Pushing Policy Learning

Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors

Learning visual servo policies via planner cloning

Self-Supervised Correspondence in Visuomotor Policy Learning

A Data-Efficient Deep Learning Approach for Deployable Multimodal Social Robots

Factored Contextual Policy Search with Bayesian Optimization

Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

Cooperative Queuing Policies for Effective Human-Multi-Robot Interaction

Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers

Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations

Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration

Conditional Neural Expert Processes for Learning Movement Primitives from Demonstration

Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach

SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning

A Framework for Learning from Demonstration with Minimal Human Effort

Contrastive Learning from Demonstrations

Learning Sampling Distributions for Model Predictive Control

Augmentation for Learning From Demonstration with Environmental Constraints

Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes

Learning from Human Directional Corrections

Understanding Physical Effects for Effective Tool-use

APPLD: Adaptive Planner Parameter Learning from Demonstration

Combining Deep Learning and Verification for Precise Object Instance Detection

Stochastic Optimal Control as Approximate Input Inference

AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers

Disentangled Relational Representations for Explaining and Learning from Demonstration