Learning of Parameters in Behavior Trees for Movement Skills

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

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review


Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error. Despite numerous successes in many applications, RL algorithms still require thousands of trials to converge to high-performing policies, can produce dangerous behaviors while learning, and the optimized policies (usually modeled as neural networks) give almost zero explanation when they fail to perform the task. For these reasons, the adoption of RL in industrial settings is not common. Behavior Trees (BTs), on the other hand, can provide a policy representation that a) supports modular and composable skills, b) allows for easy interpretation of the robot actions, and c) provides an advantageous low-dimensional parameter space. In this paper, we present a novel algorithm that can learn the parameters of a BT policy in simulation and then generalize to the physical robot without any additional training. We leverage a physical simulator with a digital twin of our workstation, and optimize the relevant parameters with a black-box optimizer. We showcase the efficacy of our method with a 7-DOF KUKAiiwa manipulator in a task that includes obstacle avoidance and a contact-rich insertion (peg-in-hole), in which our method outperforms the baselines.
Titel på värdpublikation2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
FörlagIEEE - Institute of Electrical and Electronics Engineers Inc.
Antal sidor7
ISBN (elektroniskt)978-1-6654-1715-0
ISBN (tryckt)978-1-6654-1714-3
StatusPublished - 2021 dec. 16
EvenemangIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021 - Prague, Tjeckien
Varaktighet: 2021 sep. 272021 okt. 1


KonferensIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021

Ämnesklassifikation (UKÄ)

  • Datavetenskap (datalogi)
  • Robotteknik och automation


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