Composable Part-Based ManipulationDownload PDF

Published: 30 Aug 2023, Last Modified: 03 Jul 2024CoRL 2023 PosterReaders: Everyone
Keywords: Manipulation, Part Decomposition, Diffusion Model
TL;DR: We compose diffusion models based on different part-part correspondences to improve learning and generalization of robotic manipulation skills.
Abstract: In this paper, we propose composable part-based manipulation (CPM), a novel approach that leverages object-part decomposition and part-part correspondences to improve learning and generalization of robotic manipulation skills. By considering the functional correspondences between object parts, we conceptualize functional actions, such as pouring and constrained placing, as combinations of different correspondence constraints. CPM comprises a collection of composable diffusion models, where each model captures a different inter-object correspondence. These diffusion models can generate parameters for manipulation skills based on the specific object parts. Leveraging part-based correspondences coupled with the task decomposition into distinct constraints enables strong generalization to novel objects and object categories. We validate our approach in both simulated and real-world scenarios, demonstrating its effectiveness in achieving robust and generalized manipulation capabilities.
Student First Author: no
Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)
Publication Agreement: pdf
Poster Spotlight Video: mp4
Website: https://cpmcorl2023.github.io/
Video: https://cpmcorl2023.github.io/media/overview.mp4
Code: https://cpmcorl2023.github.io/
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/composable-part-based-manipulation/code)
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