Active Object Search

In this work, we investigate an Active Object Search (AOS) task that is not explicitly addressed in the literature. It aims to actively perform as few action steps as possible to search and locate the target object in a 3D indoor scene. Different from classic object detection that passively receives visual information, this task encourages an intelligent agent to perform active search via reasonable action planning; thus it can better recall the target objects, especially for the challenging situations that the target is far from the agent, blocked by an obstacle and out of view. To handle this AOS task, we formulate a reinforcement learning framework that consists of a 3D object detector, a state controller and a cross-modal action planner to work cooperatively to find out the target object with minimal action steps. During training, we design a novel cost-sensitive active search reward that penalizes inaccurate object search and redundant action steps. To evaluate this novel task, we construct an Active Object Search (AOS) benchmark that contains 5,845 samples from 30 diverse indoor scenes. We conduct extensive qualitative and quantitative evaluations on this benchmark to demonstrate the effectiveness of the proposed approach and analyze the key factors that contribute more to address this task.