Search Results for author: Beomjoon Kim

Found 9 papers, 2 papers with code

Local object crop collision network for efficient simulation of non-convex objects in GPU-based simulators

no code implementations19 Apr 2023 Dongwon Son, Beomjoon Kim

Their main issue lies in contact detection (CD): existing CD algorithms, such as Gilbert-Johnson-Keerthi (GJK), must trade off their computational speed with accuracy which becomes expensive as the number of collisions among non-convex objects increases.

Representation, learning, and planning algorithms for geometric task and motion planning

no code implementations9 Mar 2022 Beomjoon Kim, Luke Shimanuki, Leslie Pack Kaelbling, Tomás Lozano-Pérez

The first is an algorithm for learning a rank function that guides the discrete task level search, and the second is an algorithm for learning a sampler that guides the continuous motionlevel search.

Motion Planning Representation Learning

A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects

no code implementations16 Nov 2020 Anthony Simeonov, Yilun Du, Beomjoon Kim, Francois R. Hogan, Joshua Tenenbaum, Pulkit Agrawal, Alberto Rodriguez

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 object models.

Graph Attention Motion Planning

Integrated Task and Motion Planning

no code implementations2 Oct 2020 Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, Tomás Lozano-Pérez

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 state of the objects, is known as task and motion planning (TAMP).

Motion Planning

CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs

1 code implementation26 Jul 2020 Rohan Chitnis, Tom Silver, Beomjoon Kim, Leslie Pack Kaelbling, Tomas Lozano-Perez

A general meta-planning strategy is to learn to impose constraints on the states considered and actions taken by the agent.

Motion Planning

Learning to guide task and motion planning using score-space representation

no code implementations26 Jul 2018 Beomjoon Kim, Zi Wang, Leslie Pack Kaelbling, Tomas Lozano-Perez

In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems.

Motion Planning

Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples

no code implementations4 Nov 2017 Beomjoon Kim, Leslie Pack Kaelbling, Tomas Lozano-Perez

For such complex planning problems, unguided uniform sampling of actions until a path to a goal is found is hopelessly inefficient, and gradient-based approaches often fall short when the optimization manifold of a given problem is not smooth.

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