3 citations · 5 across the 6 of their papers we have counts for
6 papers
Resolution Complete In-Place Object Retrieval given Known Object Models
Daniel Nakhimovich, Yinglong Miao, Kostas E. Bekris
This work proposes a robot task planning framework for retrieving a target object in a confined workspace among multiple stacked objects that obstruct the target. The robot can use…
Safe, Occlusion-Aware Manipulation for Online Object Reconstruction in Confined Spaces
Yinglong Miao, Rui Wang, Kostas Bekris
Recent work in robotic manipulation focuses on object retrieval in cluttered spaces under occlusion. Nevertheless, the majority of efforts lack an analysis of conditions for the co…
Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces
Rui Wang, Yinglong Miao, Kostas E. Bekris
Prehensile object rearrangement in cluttered and confined spaces has broad applications but is also challenging. For instance, rearranging products in a grocery shelf means that th…
Online Object Model Reconstruction and Reuse for Lifelong Improvement of Robot Manipulation
Shiyang Lu, Rui Wang, Yinglong Miao +2
This work proposes a robotic pipeline for picking and constrained placement of objects without geometric shape priors. Compared to recent efforts developed for similar tasks, where…
MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning under Kinodynamic Constraints
Linjun Li, Yinglong Miao, Ahmed H. Qureshi +1
Kinodynamic Motion Planning (KMP) is to find a robot motion subject to concurrent kinematics and dynamics constraints. To date, quite a few methods solve KMP problems and those tha…
Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
Ahmed H. Qureshi, Yinglong Miao, Anthony Simeonov +1
This paper describes Motion Planning Networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems. MPNet uses neural networks t…