Learning Human-Aware Robot Navigation from Physical Interaction via Inverse Reinforcement Learning

• Published in: Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 11025 - 11031
• Main Authors: Kollmitz, Marina, Koller, Torsten, Boedecker, Joschka, Burgard, Wolfram
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.10.2020
• Subjects: Context modeling; Force control; Mobile robots; Navigation; Reinforcement learning; Robots; Trajectory
• ISSN: 2153-0866
• DOI: 10.1109/IROS45743.2020.9340865

Autonomous systems, such as delivery robots, are increasingly employed in indoor spaces to carry out activities alongside humans. This development poses the question of how robots can carry out their tasks while, at the same time, behaving in a socially compliant manner. Further, humans need to be able to communicate their preferences in a simple and intuitive way, and robots should adapt their behavior accordingly. This paper investigates force control as a natural means to interact with a mobile robot by pushing it along the desired trajectory. We employ inverse reinforcement learning (IRL) to learn from human interaction and adapt the robot behavior to its users' preferences, thereby eliminating the need to program the desired behavior manually. We evaluate our approach in a real-world experiment where test subjects interact with an autonomously navigating robot in close proximity. The results suggest that force control presents an intuitive means to interact with a mobile robot and show that our robot can quickly adapt to the test subjects' personal preferences.