Legible and Proactive Robot Planning for Prosocial Human-Robot Interactions
<|reference_start|>Legible and Proactive Robot Planning for Prosocial Human-Robot Interactions: 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. Project page can be found at https://uw-ctrl.github.io/phri/.<|reference_end|>
@article{geldenbott2024legible,
    title={Legible and Proactive Robot Planning for Prosocial Human-Robot
  Interactions},
    author={Jasper Geldenbott and Karen Leung},
    journal={arXiv preprint arXiv:2404.03734},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03734},
    primaryClass={cs.RO cs.SY eess.SY}
}