Active Object Perceiver: Recognition-guided Policy Learning for Object Searching on Mobile Robots

TL;DR

This paper introduces a novel framework combining object recognition and reinforcement learning to enable mobile robots to actively search for objects in indoor environments, demonstrating superior performance in simulation and real-world tests.

Contribution

It presents a new integrated approach for object search that combines recognition-guided policy learning with a decaying reward function, advancing robotic navigation capabilities.

Findings

Outperforms existing methods in success rate

Reduces average trajectory length

Effective in both simulation and real-world environments

Abstract

We study the problem of learning a navigation policy for a robot to actively search for an object of interest in an indoor environment solely from its visual inputs. While scene-driven visual navigation has been widely studied, prior efforts on learning navigation policies for robots to find objects are limited. The problem is often more challenging than target scene finding as the target objects can be very small in the view and can be in an arbitrary pose. We approach the problem from an active perceiver perspective, and propose a novel framework that integrates a deep neural network based object recognition module and a deep reinforcement learning based action prediction mechanism. To validate our method, we conduct experiments on both a simulation dataset (AI2-THOR) and a real-world environment with a physical robot. We further propose a new decaying reward function to learn the control policy specific to the object searching task. Experimental results validate the efficacy of our method, which outperforms competing methods in both average trajectory length and success rate.