In-hand manipulation planning using human motion dictionary

TL;DR

This paper introduces a method using human motion dictionaries to learn and reproduce dexterous in-hand manipulation skills in robots, focusing on fingertip motions and optimizing primitive combinations.

Contribution

It presents a novel approach leveraging motion primitives dictionaries to model and generate in-hand manipulation skills based on human demonstrations.

Findings

Generated manipulation motions are coherent with human movements.

Manipulation constraints are inherited without explicit formalization.

The method effectively reproduces complex fingertip motions.

Abstract

Dexterous in-hand manipulation is a peculiar and useful human skill. This ability requires the coordination of many senses and hand motion to adhere to many constraints. These constraints vary and can be influenced by the object characteristics or the specific application. One of the key elements for a robotic platform to implement reliable inhand manipulation skills is to be able to integrate those constraints in their motion generations. These constraints can be implicitly modelled, learned through experience or human demonstrations. We propose a method based on motion primitives dictionaries to learn and reproduce in-hand manipulation skills. In particular, we focused on fingertip motions during the manipulation, and we defined an optimization process to combine motion primitives to reach specific fingertip configurations. The results of this work show that the proposed approach can generate manipulation motion coherent with the human one and that manipulation constraints are inherited even without an explicit formalization.