Search Results for author: Jinhwi Lee

Found 5 papers, 2 papers with code

Learning to Assemble Geometric Shapes

1 code implementation24 May 2022 Jinhwi Lee, Jungtaek Kim, Hyunsoo Chung, Jaesik Park, Minsu Cho

Assembling parts into an object is a combinatorial problem that arises in a variety of contexts in the real world and involves numerous applications in science and engineering.

Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning

no code implementations NeurIPS 2021 Hyunsoo Chung, Jungtaek Kim, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, Minsu Cho

Discovering a solution in a combinatorial space is prevalent in many real-world problems but it is also challenging due to diverse complex constraints and the vast number of possible combinations.

Object reinforcement-learning +1

Fragment Relation Networks for Geometric Shape Assembly

no code implementations NeurIPS Workshop LMCA 2020 Jinhwi Lee, Jungtaek Kim, Hyunsoo Chung, Jaesik Park, Minsu Cho

Our model processes the candidate fragments in a permutation-equivariant manner and can generalize to cases with an arbitrary number of fragments and even with a different target object.

Object Relation

Combinatorial 3D Shape Generation via Sequential Assembly

3 code implementations16 Apr 2020 Jungtaek Kim, Hyunsoo Chung, Jinhwi Lee, Minsu Cho, Jaesik Park

To alleviate this consequence induced by a huge number of feasible combinations, we propose a combinatorial 3D shape generation framework.

3D Shape Generation Bayesian Optimization

Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulation

no code implementations24 Mar 2020 Sang Hun Cheong, Brian Y. Cho, Jinhwi Lee, ChangHwan Kim, Changjoo Nam

We present an algorithm determining where to relocate objects inside a cluttered and confined space while rearranging objects to retrieve a target object.

Robotics

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