Legible and Proactive Robot Planning for Prosocial Human-Robot Interactions

Jasper Geldenbott, Karen Leung
University of Washington, William E. Boeing Department of Aeronautics and Astronautics
IEEE International Conference on Robotics and Automation (ICRA) 2024
Paper Code arXiv

Real-time human-in-the-loop simulation of the our method in ROS Gazebo on the Clearpath Husky A200.

Abstract

Humans have a remarkable ability to fluently engage in joint collision avoidance in crowded navigation tasks despite the complexities and uncertainties inherent in human behavior. Underlying these interactions is a mutual understanding that (i) individuals are prosocial, that is, there is equitable responsibility in avoiding collisions, and (ii) individuals should behave legibly, that is, move in a way that clearly conveys their intent to reduce ambiguity in how they intend to avoid others. Toward building robots that can safely and seamlessly interact with humans, we propose a general robot trajectory planning framework for synthesizing legible and proactive behaviors and demonstrate that our robot planner naturally leads to prosocial interactions. Specifically, we introduce the notion of a markup factor to incentivize legible and proactive behaviors and an inconvenience budget constraint to ensure equitable collision avoidance responsibility. We evaluate our approach against well-established multi-agent planning algorithms and show that using our approach produces safe, fluent, and prosocial interactions. We demonstrate the real-time feasibility of our approach with human-in-the-loop simulations.

Poster

BibTeX

@inproceedings{GeldenbottLeung2024,
  author = {Geldenbott, J. and Leung, K.},
  booktitle = {{Proc.\ IEEE Conf.\ on Robotics and Automation}},
  title = {Legible and Proactive Robot Planning for Prosocial Human-Robot Interactions},
  year = {2024}
}