Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape AugmentationDownload PDF

Published: 10 Sept 2022, Last Modified: 05 May 2023CoRL 2022 PosterReaders: Everyone
Keywords: Dexterous Manipulation, Learning from Human Demonstration, Data Augmentation
TL;DR: We propose Implicit Shape Augmentation, a method that is able to robustly interact with daily unseen objects by leveraging simulation as well as a small number of human demonstrations.
Abstract: Dexterous robotic hands have the capability to interact with a wide variety of household objects. However, learning robust real world grasping policies for arbitrary objects has proven challenging due to the difficulty of generating high quality training data. In this work, we propose a learning system (\emph{ISAGrasp}) for leveraging a small number of human demonstrations to bootstrap the generation of a much larger dataset containing successful grasps on a variety of novel objects. Our key insight is to use a correspondence-aware implicit generative model to deform object meshes and demonstrated human grasps in order to create a diverse dataset for supervised learning, while maintaining semantic realism. We use this dataset to train a robust grasping policy in simulation which can be deployed in the real world. We demonstrate grasping performance with a four-fingered Allegro hand in both simulation and the real world, and show this method can handle entirely new semantic classes and achieve a 79% success rate on grasping unseen objects in the real world.
Student First Author: yes
Supplementary Material: zip
Website: https://sites.google.com/view/implicitaugmentation/home
13 Replies