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

Autor:	Rozo, Leonel, Guo, Meng, Kupcsik, Andras G., Todescato, Marco, Schillinger, Philipp, Giftthaler, Markus, Ochs, Matthias, Spies, Markus, Waniek, Nicolai, Kesper, Patrick, Büerger, Mathias
Rok vydání:	2020
Předmět:	Computer Science - Robotics Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: First three authors equally contributed. Pre-print accepted for publication in IROS'2020. Video: https://youtu.be/dRGLadt32o4
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.10471 Zobrazit plný text záznamu View this record from Arxiv