Composable Part-Based Manipulation

Conference on Robot Learning (CoRL) 2023

1Stanford, 2MIT, 3NVIDIA, 4University of Utah, 5Georgia Tech


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.



    title     = {Composable Part-Based Manipulation},
    author    = {Liu, Weiyu and Mao, Jiayuan and Hsu, Joy and Hermans, Tucker and Garg, Animesh and Wu, Jiajun},
    year      = {2023},
    booktitle = {CoRL 2023}