[2603.25366] Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics

Computer Science > Robotics arXiv:2603.25366 (cs) [Submitted on 26 Mar 2026] Title:Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics Authors:João Castelo-Branco, José Santos-Victor, Alexandre Bernardino View a PDF of the paper titled Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics, by Jo\~ao Castelo-Branco and 2 other authors View PDF 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, t...