Temporal Force Synergies in Human Grasping
Julia Starke, Marco Keller, Amim Asfour
Abstract
Humans can intuitively grasp objects of different shape and weight. Throughout the grasp execution they control and coordinate the grasp forces at all contact points between the hand and the object to achieve a stable grasp. Dexterous grasping with humanoid hands relies on the perfect coordination between grasp posture and force balance at the contact points in a high dimensional space and remains a challenge. In this paper, we present temporal force synergies describing the change in human grasp forces during the grasp execution in a low-dimensional space based on two new grasp synergy models: 1) static force synergies that are derived by a Principal Component Analysis and represent temporal grasp forces as a sequence of time-independent synergy configurations and 2) dynamic force synergies that are learned by a recurrent neural network and encode the temporal change of grasp forces throughout grasp execution in a latent synergy space clustered by grasp types. We show that both synergy spaces encode human grasp forces with an error of less than 2% and allow the generation of human-like grasp force patterns. Grasp forces for stable grasps described by the dynamic force synergies achieve a grasp quality comparable to demonstrated human grasps in simulation.
BibTeX
@inproceedings{iros2021_temporalforcesyn,
title = {Temporal Force Synergies in Human Grasping},
author = {Julia Starke and Marco Keller and Amim Asfour},
booktitle = {IROS 2021},
year = {2021}
}