基于视觉的追捕-逃避机器人策略学习
Learning Vision-based Pursuit-Evasion Robot Policies
AI总结：
本文提出将追捕-逃避问题转化为监督学习，利用完全可观测策略指导部分可观测策略，并在四足机器人上验证，实现了在不确定性下的信息收集与意图预测。
摘要：
在现实世界约束下学习战略性机器人行为（如追捕-逃避交互中所需的行为）极具挑战性。这要求利用交互的动态特性，并同时规划物理状态和潜在意图的不确定性。在本文中，我们将这一棘手问题转化为监督学习问题，其中完全可观测的机器人策略为部分可观测的策略生成监督信号。我们发现，部分可观测的追捕者策略的监督信号质量取决于两个关键因素：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建模假设的强度。我们将策略部署在配备RGB-D相机的物理四足机器人上，在野外进行追捕-逃避交互实验。尽管面临诸多挑战，传感约束也带来了创造性：机器人在不确定时被推动去收集信息，从噪声测量中预测意图，并提前预判以进行拦截。项目网页：https://abajcsy.github.io/vision-based-pursuit/
英文摘要：
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 webpage: https://abajcsy.github.io/vision-based-pursuit/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
