Explore, Approach, and Terminate: Evaluating Subtasks in Active Visual Object Search Based on Deep Reinforcement Learning

Abstract

Searching for objects and distinguishing task-relevant objects from others is a key requirement for service robots. We propose a reinforcement learning solution to the active visual object search problem. Our method successfully learns to explore the environment, to approach the target object, and to decide when to terminate the search as the target object has been found. We demonstrate the efficiency of our solution on a dataset of real-world images collected by a robot. Our approach outperforms state-space planning or other baseline search strategies, reaching a higher success rate in a shorter time. We also study individual subtasks of active visual object search. Although strong baselines exist for the subtasks, our RL solution outperforms them in the overall search task.