Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control
专题命中 机器人学习 :robot policy(abstract);分类 cs.RO、cs.LG
Comments Preprint
视觉与机器人
机器人、具身智能、机器人学习、操作、导航和具身世界模型。
专题命中 机器人学习 :robot policy(abstract);分类 cs.RO、cs.LG
Comments Preprint
专题命中 机器人学习 :robot learning(abstract);分类 cs.RO、cs.AI
专题命中 机器人学习 :manipulation(abstract);分类 cs.RO、cs.AI
专题命中 机器人学习 :navigation(abstract);分类 cs.RO、cs.AI
Comments Accepted to ICRA 2020
专题命中 机器人学习 :robotic(abstract);分类 cs.RO、cs.LG
Comments Published at a RSS20 workshop
专题命中 机器人学习 :robot learning(abstract);分类 cs.RO、cs.AI
Comments 7 pages, 4 figures, ICRA 2020
专题命中 机器人学习 :robotics(abstract);分类 cs.RO、cs.LG
Comments Final paper for IEEE CASE, Hong Kong, August, 2020. First three authors contributed equally
专题命中 机器人学习 :robotics(abstract);分类 cs.RO、cs.AI
专题命中 机器人学习 :robotic(abstract);分类 cs.RO、cs.AI
专题命中 机器人学习 :robotics(abstract);分类 cs.RO、cs.LG
Comments Computer Aided Verification, 2020
专题命中 机器人学习 :robotic(abstract);分类 cs.CV、cs.LG
Comments Accepted ICCV 2019 Workshop
专题命中 机器人学习 :robot learning(abstract);分类 cs.RO、cs.CV
专题命中 机器人学习 :embodied agent(abstract);分类 cs.AI、cs.CV
专题命中 机器人学习 :manipulation(abstract);分类 cs.RO、cs.LG
Comments arXiv admin note: substantial text overlap with arXiv:1810.10191
专题命中 机器人学习 :embodied agent(abstract);分类 cs.AI、cs.CV
专题命中 机器人学习 :robotic(abstract);分类 cs.RO、cs.LG
Comments Copyright 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
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
专题命中 机器人学习 :robotics(abstract);分类 cs.AI、cs.LG
专题命中 机器人学习 :robotics(abstract);分类 cs.AI、cs.LG
Comments NIPS 2018
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
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)
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
专题命中 机器人学习 :manipulation(abstract);分类 cs.AI、cs.LG
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)
与大学生共同创造可构建且开放的社会机器人学习伙伴
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)
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
通过T-LQG实现接近最优的信念空间规划
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"
通过归纳逻辑编程从演示中学习组合符号任务规则
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)
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
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."
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
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