27 citations · 45 across the 8 of their papers we have counts for
9 papers
Learning Whole-body Manipulation for Quadrupedal Robot
Seunghun Jeon, Moonkyu Jung, Suyoung Choi +2
We propose a learning-based system for enabling quadrupedal robots to manipulate large, heavy objects using their whole body. Our system is based on a hierarchical control strategy…
Local object crop collision network for efficient simulation of non-convex objects in GPU-based simulators
Dongwon Son, Beomjoon Kim
Our goal is to develop an efficient contact detection algorithm for large-scale GPU-based simulation of non-convex objects. Current GPU-based simulators such as IsaacGym and Brax m…
Representation, learning, and planning algorithms for geometric task and motion planning
Beomjoon Kim, Luke Shimanuki, Leslie Pack Kaelbling +1
We present a framework for learning to guide geometric task and motion planning (GTAMP). GTAMP is a subclass of task and motion planning in which the goal is to move multiple objec…
A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects
Anthony Simeonov, Yilun Du, Beomjoon Kim +4
We present a framework for solving long-horizon planning problems involving manipulation of rigid objects that operates directly from a point-cloud observation, i.e. without prior…
Integrated Task and Motion Planning
Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay +4
The problem of planning for a robot that operates in environments containing a large number of objects, taking actions to move itself through the world as well as to change the sta…
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
Rohan Chitnis, Tom Silver, Beomjoon Kim +2
Meta-planning, or learning to guide planning from experience, is a promising approach to improving the computational cost of planning. A general meta-planning strategy is to learn…