In cooperative environments, such as in factories or assistive scenarios, it is important for a robot to communicate its intentions to observers, who could be either other humans or robots. A legible trajectory allows an observer to quickly and accurately predict an agent's intention. In adversarial environments, such as in military operations or games, it is important for a robot to not communicate its intentions to observers. An illegible trajectory leads an observer to incorrectly predict the agent's intention or delays when an observer is able to make a correct prediction about the agent's intention. However, in some environments there are multiple observers, each of whom may be able to see only part of the environment, and each of whom may have different motives. In this work, we introduce the Mixed-Motive Limited-Observability Legible Motion Planning (MMLO-LMP) problem, which requires a motion planner to generate a trajectory that is legible to observers with positive motives and illegible to observers with negative motives while also considering the visibility limitations of each observer. We highlight multiple strategies an agent can take while still achieving the problem objective. We also present DUBIOUS, a trajectory optimizer that solves MMLO-LMP. Our results show that DUBIOUS can generate trajectories that balance legibility with the motives and limited visibility regions of the observers. Future work includes many variations of MMLO-LMP, including moving observers and observer teaming.

翻译：在协作环境中，例如工厂或辅助场景中，机器人向观察者（可能是其他人类或机器人）传达其意图至关重要。一条可解读的轨迹使观察者能够快速准确地预测智能体的意图。而在对抗环境中，例如军事行动或游戏中，机器人不向观察者传达其意图则至关重要。一条不可解读的轨迹会导致观察者对智能体意图做出错误预测，或延迟其做出正确预测的时间。然而，在某些环境中存在多个观察者，每个观察者可能只能看到环境的一部分，并且各自可能具有不同的动机。在本工作中，我们提出了混合动机有限可观测性可解读运动规划问题，该问题要求运动规划器生成一条轨迹，该轨迹对具有积极动机的观察者而言是可解读的，对具有消极动机的观察者而言是不可解读的，同时还需考虑每个观察者的视野限制。我们阐述了智能体在实现该问题目标时可采取的多种策略。我们还提出了DUBIOUS，一种用于求解MMLO-LMP的轨迹优化器。我们的结果表明，DUBIOUS能够生成在可解读性与观察者动机及有限视野区域之间取得平衡的轨迹。未来的工作包括MMLO-LMP的多种变体，例如移动观察者及观察者协同等情况。