Active and Transfer Learning of Grasps by Sampling from Demonstration

TL;DR

This paper introduces a novel active and transfer learning approach for grasping objects, inspired by human infant learning, using biased random search methods grounded in kernel adaptive MCMC, demonstrating promising experimental results.

Contribution

It presents a new active and transfer learning framework for grasping that leverages kernel adaptive mode-hopping MCMC, a novel approach in robotic grasp learning.

Findings

Effective grasp learning for known objects.

Successful transfer of grasp knowledge to novel objects.

Promising experimental results demonstrating applicability.

Abstract

We guess humans start acquiring grasping skills as early as at the infant stage by virtue of two key processes. First, infants attempt to learn grasps for known objects by imitating humans. Secondly, knowledge acquired during this process is reused in learning to grasp novel objects. We argue that these processes of active and transfer learning boil down to a random search of grasps on an object, suitably biased by prior experience. In this paper we introduce active learning of grasps for known objects as well as transfer learning of grasps for novel objects grounded on kernel adaptive, mode-hopping Markov Chain Monte Carlo. Our experiments show promising applicability of our proposed learning methods.