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arXiv · 2312.03864

Geometry Matching for Multi-Embodiment Grasping

Maria Attarian·Muhammad Adil Asif·Jingzhou Liu·Ruthrash Hari·Animesh Garg·Igor Gilitschenski·Jonathan Tompson
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Abstract

Many existing learning-based grasping approaches concentrate on a single embodiment, provide limited generalization to higher DoF end-effectors and cannot capture a diverse set of grasp modes. We tackle the problem of grasping using multiple embodiments by learning rich geometric representations for both objects and end-effectors using Graph Neural Networks. 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 predictions of grasps keypoint-by-keypoint. We compare our method against baselines that support multiple embodiments. Our approach performs better across three end-effectors, while also producing diverse grasps. Examples, including real robot demos, can be found at geo-match.github.io.

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Maria Attarian, Muhammad Adil Asif, Jingzhou Liu, Ruthrash Hari, Animesh Garg, Igor Gilitschenski, Jonathan Tompson. 2023-12-06. Geometry Matching for Multi-Embodiment Grasping. https://arxiv.org/abs/2312.03864

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