93 citations · 407 across the 82 of their papers we have counts for
9 papers · 1 filter
Fast High-Quality Tabletop Rearrangement in Bounded Workspace
Kai Gao, Darren Lau, Baichuan Huang +2
In this paper, we examine the problem of rearranging many objects on a tabletop in a cluttered setting using overhand grasps. Efficient solutions for the problem, which capture a c…
Improving Kinodynamic Planners for Vehicular Navigation with Learned Goal-Reaching Controllers
Aravind Sivaramakrishnan, Edgar Granados, Seth Karten +2
This paper aims to improve the path quality and computational efficiency of sampling-based kinodynamic planners for vehicular navigation. It proposes a learning framework for ident…
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…
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
Bowen Wen, Wenzhao Lian, Kostas Bekris +1
Task-relevant grasping is critical for industrial assembly, where downstream manipulation tasks constrain the set of valid grasps. Learning how to perform this task, however, is ch…
BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models
Bowen Wen, Kostas Bekris
Tracking the 6D pose of objects in video sequences is important for robot manipulation. Most prior efforts, however, often assume that the target object's CAD model, at least at a…