title: Learning Vision-based Pursuit-Evasion Robot Policies
summary: 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/
paper_authors._id: 64f0127c8602335a045a84fb
paper_authors.name: Andrea Bajcsy
paper_authors._id: 64f0127c8602335a045a84fc
paper_authors.name: Antonio Loquercio
paper_authors._id: 64f0127c8602335a045a84fd
paper_authors.name: Ashish Kumar
paper_authors._id: 64f0127c8602335a045a84fe
paper_authors.name: Jitendra Malik
paper_title: Learning Vision-based Pursuit-Evasion Robot Policies
paper_summary: 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/
paper_ai_summary: A fully-observable robot policy guides a partially-observable one in pursuit-evasion tasks by generating supervision, focusing on evader behavior diversity and modeling assumptions.
paper_ai_keywords: fully-observable partially-observable pursuit-evasion supervision signal evader behavior diversity modeling assumptions quadruped robot RGB-D camera intent prediction anticipation