8 papers · 1 filter
TRACE: Coverage Path Planning for Unknown Environments Using Hierarchical Coverage Tree
Zongyuan Shen, Haodong Liu, Gao Wang +6
This paper presents a novel online coverage path planning (CPP) algorithm, called TRACE, for real-time coverage of unknown environments. TRACE is built upon a hierarchical coverage…
RoboTok: A Scalable Data Engine for Internet Demonstration Video Retrieval and Dexterous Manipulation Learning
Howard Qian, Yiting Chen, Yunfei Xie +6
Robot learning increasingly depends on broad and diverse demonstrations, yet collecting robot data remains expensive and difficult to scale across the wide range of real-world task…
Learning-Based Motion Planning for Dynamic Environments: From Foundational Algorithms to Emerging Paradigms
Zongyuan Shen, Shalabh Gupta, Shancheng Zhao +7
Motion planning in dynamic environments is a fundamental problem in robotics, aiming to generate safe and efficient paths, trajectories, or control actions in the presence of movin…
Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions
Zongyuan Shen, Shalabh Gupta, Shancheng Zhao +6
Coverage path planning (CPP) is a fundamental problem in robot motion planning, whose aim is to produce robot trajectories that provide complete coverage of target workspaces while…
EgoInfinity: A Web-Scale 4D Hand-Object Interaction Data Engine for Any-View Robot Retargeting and Video-to-Action Robot Learning
Gaotian Wang, Kejia Ren, Andrew Morgan +4
Internet videos constitute the largest reservoir of embodied human manipulation knowledge, yet converting arbitrary RGB footage into actionable robot training data remains a major…
Motion Planning in Dynamic Environments: A Survey from Classical to Modern Methods
Zongyuan Shen, Yaming Ou, Shalabh Gupta +6
Motion planning in dynamic environments requires robots to continuously adapt their paths in response to environmental changes for safe and uninterrupted navigation. While many sur…
Zero-Shot Sim-to-Real Robot Learning: A Dexterous Manipulation Study on Reactive Catching
Kejia Ren, Gaotian Wang, Andrew S. Morgan +1
Dexterous manipulation is physics-intensive and highly sensitive to modeling errors and perception noise, making sim-to-real transfer prohibitively challenging. Domain randomizatio…
ManiDreams: An Open-Source Library for Robust Object Manipulation via Uncertainty-aware Task-specific Intuitive Physics
Gaotian Wang, Kejia Ren, Andrew S. Morgan +1
Dynamics models, whether simulators or learned world models, have long been central to robotic manipulation, but most focus on minimizing prediction error rather than confronting a…