A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations

Anirban Santara

Abstract: Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object. We propose a modular framework for object-nav that is able to efficiently search indoor environments for not just static objects but also movable objects (e.g. fruits, glasses, phones, etc.) that frequently change their positions due to human interaction. Our contextual-bandit agent efficiently explores the environment by showing optimism in the face of uncertainty and learns a model of the likelihood of spotting different objects from each navigable location. The likelihoods are used as rewards in a weighted minimum latency solver to deduce a trajectory for the robot.