arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~

期刊&会议

International Conference on Robotics and Automation · 会议 · Robotics

2026-03-19 至 2026-03-19 共收录 4
2603.17802 2026-03-19 eess.SY cs.SY

An HMDP-MPC Decision-making Framework with Adaptive Safety Margins and Hysteresis for Autonomous Driving

具有自适应安全边际和滞回机制的HMDP-MPC决策框架用于自动驾驶

Siyuan Li, Chengyuan Liu, Wen-Hua Chen

AI总结 本文提出一种整合混合马尔可夫决策过程与模型预测控制的决策框架,结合速度依赖的安全边际和预测感知的滞回机制,通过双层恢复方案提升决策连续性与鲁棒性,经多场景测试验证其在异构交通中的适应性与安全性。

Comments 8 pages, 6 figures, to be published in ICRA 2026 proceedings

详情

展开后加载摘要…

URL PDF HTML 收藏
2512.11903 2026-03-19 cs.RO cs.CV

Aion: Towards Hierarchical 4D Scene Graphs with Temporal Flow Dynamics

Aion:面向具有时间流动态的分层4D场景图

Iacopo Catalano, Eduardo Montijano, Javier Civera, Julio A. Placed, Jorge Pena-Queralta

机构 * University of Turku(图尔库大学) Centre for Artificial Intelligence, Zürich University of Applied Sciences(应用科学大学人工智能中心) Instituto Tecnológico de Aragón (ITA) and the University of Zaragoza(阿兰理工大学和萨拉戈萨大学) University of Zaragoza(萨拉戈萨大学)

AI总结 Aion通过在分层3D场景图中嵌入时间流动态,提升动态环境中导航规划和交互的可解释性和可扩展性。

Comments Accepted at ICRA 2026, 8 pages

详情

展开后加载摘要…

URL PDF HTML 收藏
2603.17632 2026-03-19 eess.SY cs.RO cs.SY math.OC

Real-Time Online Learning for Model Predictive Control using a Spatio-Temporal Gaussian Process Approximation

基于时空高斯过程近似的实时在线学习用于模型预测控制

Lars Bartels, Amon Lahr, Andrea Carron, Melanie N. Zeilinger

机构 * Institute for Dynamic Systems and Control, ETH Zürich(动态系统与控制研究所,苏黎世联邦理工学院)

AI总结 本文提出一种高效的时空高斯过程近似方法,用于实时在线学习,提升模型预测控制的性能,尤其适用于时变系统。

Comments to be published at 2026 IEEE International Conference on Robotics & Automation (ICRA)

详情

展开后加载摘要…

URL PDF HTML 收藏
2505.22882 2026-03-19 cs.RO

TwinTrack: Bridging Vision and Contact Physics for Real-Time Tracking of Unknown Objects in Contact-Rich Scenes

TwinTrack:通过视觉与接触物理弥合差距,实现实时跟踪未知物体

Wen Yang, Zhixian Xie, Yiting Wang, Abhijit Tadepalli, Heni Ben Amor, Shan Lin, Wanxin Jin

机构 * School for Engineering of Matter, Transport, and Energy(物质、运输与能量工程学院) School of Computing and Augmented Intelligence(计算与增强智能学院) School of Electrical, Computer and Energy Engineering(电气、计算机与能源工程学院) Arizona State University(亚利桑那州立大学) Department of Robotics(机器人学系) Mohamed bin Zayed University of Artificial Intelligence(Mohamed bin Zayed人工智能大学)

AI总结 TwinTrack通过结合视觉与接触物理,实现实时跟踪复杂场景中未知动态物体,利用接触物理线索提升鲁棒性和精度,实验表明其在20Hz以上的跟踪速度优于基线方法。

Comments Accepted by IEEE International Conference on Robotics & Automation (ICRA) 2026

详情

展开后加载摘要…

URL PDF HTML 收藏