Search Results for author: Beomjoon Kim

Found 10 papers, 3 papers with code

Transformable Gaussian Reward Function for Socially-Aware Navigation with Deep Reinforcement Learning

1 code implementation22 Feb 2024 Jinyeob Kim, Sumin Kang, Sungwoo Yang, Beomjoon Kim, Jargalbaatar Yura, Donghan Kim

Although reinforcement learning technique has fostered the advancement of socially aware navigation, defining appropriate reward functions, especially in congested environments, has posed a significant challenge.

reinforcement-learning Robot Navigation

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.

Generative Adversarial Network

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 Task and Motion Planning

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 +2

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 +1

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.

Contact Detection

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