Learning Human-Like Functional Grasping for Multifinger Hands From Few Demonstrations

Abstract

This article investigates the challenge of enabling multifinger hands to perform human-like functional grasping for various intentions. However, accomplishing functional grasping in real robot hands present many challenges, including handling generalization ability for kinematically diverse robot hands, generating intention-conditioned grasps for a large variety of objects, and incomplete perception from a single-view camera. In this work, we first propose a six-step functional grasp synthesis algorithm based on fine-grained contact modeling. With the fine-grained contact-based optimization and learned dense shape correspondence, the algorithm is adaptable to various objects of the same category and a wide range of multifinger hands using few demonstrations. Second, over 10 k functional grasps are synthesized to train our neural network, named DexFG-Net, which generates intention-conditioned grasps based on reconstructed object. Extensive experiments in the simulation and physical grasps indicate that the grasp synthesis algorithm can produce human-like functional grasp with robust stability and functionality, and the DexFG-Net can generate plausible and human-like intention-conditioned grasping postures for anthropomorphic hands.