Geometry Matching for Multi-Embodiment Grasping
Maria Attarian, Muhammad Adil Asif, Jingzhou Liu, Ruthrash Hari, Animesh Garg, Igor Gilitschenski, Jonathan Tompson
Abstract
While significant progress has been made on the problem of generating grasps, many existing learning-based approaches still concentrate on a single embodiment, provide limited generalization to higher DoF end-effectors and cannot capture a diverse set of grasp modes. In this paper, we tackle the problem of grasping multi-embodiments through the viewpoint of learning rich geometric representations for both objects and end-effectors using Graph Neural Networks (GNN). Our novel method - GeoMatch - applies supervised learning on grasping data from multiple embodiments, learning end-to-end contact point likelihood maps as well as conditional autoregressive prediction of grasps keypoint-by-keypoint. We compare our method against 3 baselines that provide multi-embodiment support. Our approach performs better across 3 end-effectors, while also providing competitive diversity of grasps. Examples can be found at geomatch.github.io.
BibTeX
@inproceedings{
attarian2023geometry,
title={Geometry Matching for Multi-Embodiment Grasping},
author={Maria Attarian and Muhammad Adil Asif and Jingzhou Liu and Ruthrash Hari and Animesh Garg and Igor Gilitschenski and Jonathan Tompson},
booktitle={7th Annual Conference on Robot Learning},
year={2023},
url={https://openreview.net/forum?id=oyWkrG-LD5}
}