Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration

Matthias Mayr, Faseeh Ahmad, Konstantinos Chatzilygeroudis, Luigi Nardi, Volker Krueger

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

Abstract

In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems often fall short. We introduce an approach that provides a combination of task-level planning with targeted learning of scenario-specific parameters for skill-based systems. We propose the following pipeline: the user provides a task goal in the planning language PDDL, then a plan (i.e., a sequence of skills) is generated and the learnable parameters of the skills are automatically identified, and, finally, an operator chooses reward functions and parameters for the learning process. Two aspects of our methodology are critical: (a) learning is tightly integrated with a knowledge framework to support symbolic planning and to provide priors for learning, (b) using multi-objective optimization. This can help to balance key performance indicators (KPIs) such as safety and task performance since they can often affect each other. We adopt a multi-objective Bayesian optimization approach and learn entirely in simulation. We demonstrate the efficacy and versatility of our approach by learning skill parameters for two different contact-rich tasks. We show their successful execution on a real 7-DOF KUKA-iiwa manipulator and outperform the manual parameterization by human robot operators.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Pages1995-2002
Number of pages8
ISBN (Electronic)9781665481090
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022 - Jinghong, China
Duration: 2022 Dec 52022 Dec 9

Conference

Conference2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022
Country/TerritoryChina
CityJinghong
Period2022/12/052022/12/09

Subject classification (UKÄ)

  • Robotics

Free keywords

  • Reinforcement Learning
  • Multi-objective
  • Knowledge Representation

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