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/.

翻译：人类在拥挤导航任务中，尽管人类行为存在复杂性和不确定性，仍展现出流畅进行联合避障的卓越能力。这些交互背后的基础是共同理解：(i) 个体具有亲社会性，即平等承担避障责任；(ii) 个体应保持行为可读性，即通过动作清晰传达意图，减少避让方式上的歧义。为构建能与人类安全无缝交互的机器人，我们提出了一种通用的机器人轨迹规划框架，用于合成可读且具有前瞻性的行为，并证明该规划器自然能够产生亲社会交互。具体而言，我们引入标记因子概念以激励可读性与前瞻性行为，并设置不便预算约束以确保平等避障责任。我们通过与成熟的多智能体规划算法对比评估，证明该方法能产生安全、流畅且亲社会的交互。通过人在环仿真验证了该方法的实时可行性。项目页面详见https://uw-ctrl.github.io/phri/。