'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
In this work, a synergy-based reinforcement learning
algorithm has been developed to confer autonomous grasping
capabilities to anthropomorphic hands. In the presence of
high degrees of freedom, classical machine learning techniques
require a number of iterations that increases with the size of the
problem, thus convergence of the solution is not ensured. The
use of postural synergies determines dimensionality reduction
of the search space and allows recent learning techniques, such
as Policy Improvement with Path Integrals, to become easily
applicable. A key point is the adoption of a suitable reward
function representing the goal of the task and ensuring onestep
performance evaluation. Force-closure quality of the grasp
in the synergies subspace has been chosen as a cost function
for performance evaluation. The experiments conducted on the
SCHUNK 5-Finger Hand demonstrate the effectiveness of the
algorithm showing skills comparable to human capabilities in
learning new grasps and in performing a wide variety from
power to high precision grasps of very small objects