Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks

TL;DR

This paper introduces a rapid, flexible robot skill-sequencing framework using object-centric hidden semi-Markov models, enabling industrial robots to adapt and sequence manipulation skills efficiently for complex tasks.

Contribution

The work presents a novel skill-sequencing algorithm that encodes manipulation skills with object-centric models, reducing manual effort and increasing flexibility and reusability.

Findings

Effective sequencing of manipulation skills demonstrated on a 7 DoF robot arm.

High flexibility and reusability of learned skills in industrial tasks.

Smooth transition computation between skills using Riemannian optimal control.

Abstract

Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e.g., be able adapt to the current workspace configuration. Furthermore, to accomplish complex manipulation tasks, robots should be able to sequence several skills and adapt them to changing situations. In this work, we propose a rapid robot skill-sequencing algorithm, where the skills are encoded by object-centric hidden semi-Markov models. The learned skill models can encode multimodal (temporal and spatial) trajectory distributions. This approach significantly reduces manual modeling efforts, while ensuring a high degree of flexibility and re-usability of learned skills. Given a task goal and a set of generic skills, our framework computes smooth transitions between skill instances. To compute the corresponding optimal end-effector trajectory in task space we rely on Riemannian optimal controller. We demonstrate this approach on a 7 DoF robot arm for industrial assembly tasks.