Learning strategic robot behavior -- like that required in pursuit-evasion
interactions -- under real-world constraints is extremely challenging. It
requires exploiting the dynamics of the interaction, and planning through both
physical state and latent intent uncertainty. In this paper, we transform this
intractable problem into a supervised learning problem, where a
fully-observable robot policy generates supervision for a partially-observable
one. We find that the quality of the supervision signal for the
partially-observable pursuer policy depends on two key factors: the balance of
diversity and optimality of the evader's behavior and the strength of the
modeling assumptions in the fully-observable policy. We deploy our policy on a
physical quadruped robot with an RGB-D camera on pursuit-evasion interactions
in the wild. Despite all the challenges, the sensing constraints bring about
creativity: the robot is pushed to gather information when uncertain, predict
intent from noisy measurements, and anticipate in order to intercept. Project
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将从追逐 - 逃避互动中学习战略性机器人行为这一实际约束下的问题转化为一个监督学习问题，其中完全可观测的机器人策略为部分可观测的机器人策略生成监督信号，并发现这个监督信号的质量依赖于两个关键因素：逃避者行为的多样性与最优性的平衡以及完全可观测策略中的建模假设的强度。在野外的追逐 - 逃避互动中，我们部署了这个策略在一台带有 RGB-D 相机的四足机器人上，尽管面临各种挑战，感知限制激发了创造力：机器人在不确定时努力收集信息，从嘈杂的测量中预测意图，并为了拦截而提前预判。