Belief Aided Navigation using Bayesian Reinforcement Learning for Avoiding Humans in Blind Spots
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
Recent research on mobile robot navigation has focused on socially aware navigation in crowded environments. However, existing methods do not adequately account for human robot interactions and demand accurate location information from omnidirectional sensors, rendering them unsuitable for practical applications. In response to this need, this study introduces a novel algorithm, BNBRL+, predicated on the partially observable Markov decision process framework to assess risks in unobservable areas and formulate movement strategies under uncertainty. BNBRL+ consolidates belief algorithms with Bayesian neural networks to probabilistically infer beliefs based on the positional data of humans. It further integrates the dynamics between the robot, humans, and inferred beliefs to determine the navigation paths and embeds social norms within the reward function, thereby facilitating socially aware navigation. Through experiments in various risk laden scenarios, this study validates the effectiveness of BNBRL+ in navigating crowded environments with blind spots. The model's ability to navigate effectively in spaces with limited visibility and avoid obstacles dynamically can significantly improve the safety and reliability of autonomous vehicles.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 15 of its 28 references that are in the map. First posted on arXiv 15 Mar 2024.
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- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
- 2022Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graphcited by 53
- 2019Relational Graph Learning for Crowd Navigationcited by 48
- 2020Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learningcited by 40
- 2020DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Datacited by 19
- 2019Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environmentscited by 18
- 2023Multi-Robot Cooperative Socially-Aware Navigation Using Multi-Agent Reinforcement Learningcited by 17
- 2015Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actionscited by 11
- 2021Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Predictioncited by 10
- 2022Occlusion-Aware Crowd Navigation Using People as Sensorscited by 9
- 2022Feedback-efficient Active Preference Learning for Socially Aware Robot Navigationcited by 7
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- 2019Behavior Planning of Autonomous Cars with Social Perceptioncited by 6
- 2023On-Robot Bayesian Reinforcement Learning for POMDPscited by 3
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- 2025Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches
- 2025Perspective-Shifted Neuro-Symbolic World Models: A Framework for Socially-Aware Robot Navigation
- 2025NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points
- 2025Towards Generalizable Safety in Crowd Navigation via Conformal Uncertainty Handling
- 2024Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot Navigation
- 2026Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics
- 2017Uncertainty-Aware Reinforcement Learning for Collision Avoidance