Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics

TL;DR

This paper presents a hybrid framework combining Bayesian inference and deep reinforcement learning to improve object search efficiency and reliability for mobile robots in indoor environments.

Contribution

It introduces a novel integration of Bayesian belief maps with deep RL policies for object navigation, enhancing performance under uncertainty.

Findings

Increased success rate in object search tasks.

Reduced search effort compared to baseline strategies.

Effective handling of partial observability in indoor environments.

Abstract

Autonomous object search is challenging for mobile robots operating in indoor environments due to partial observability, perceptual uncertainty, and the need to trade off exploration and navigation efficiency. Classical probabilistic approaches explicitly represent uncertainty but typically rely on handcrafted action-selection heuristics, while deep reinforcement learning enables adaptive policies but often suffers from slow convergence and limited interpretability. This paper proposes a hybrid object-search framework that integrates Bayesian inference with deep reinforcement learning. The method maintains a spatial belief map over target locations, updated online through Bayesian inference from calibrated object detections, and trains a reinforcement learning policy to select navigation actions directly from this probabilistic representation. The approach is evaluated in realistic indoor simulation using Habitat 3.0 and compared against developed baseline strategies. Across two indoor environments, the proposed method improves success rate while reducing search effort. Overall, the results support the value of combining Bayesian belief estimation with learned action selection to achieve more efficient and reliable objectsearch behavior under partial observability.