# Belief Aided Navigation using Bayesian Reinforcement Learning for Avoiding Humans in Blind Spots

arXiv: https://arxiv.org/abs/2403.10105 · HTML: https://mlsanity.com/paper/2403.10105/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Relational Graph Learning for Crowd Navigation](https://mlsanity.com/paper/1909.13165/) (2019)
- [Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2011.04820/) (2020)
- [DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data](https://mlsanity.com/paper/2004.14079/) (2020)
- [Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments](https://mlsanity.com/paper/1904.11483/) (2019)
- [Multi-Robot Cooperative Socially-Aware Navigation Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2309.15234/) (2023)
- [Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions](https://mlsanity.com/paper/1502.06030/) (2015)
- [Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction](https://mlsanity.com/paper/2107.07056/) (2021)
- [Occlusion-Aware Crowd Navigation Using People as Sensors](https://mlsanity.com/paper/2210.00552/) (2022)
- [Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation](https://mlsanity.com/paper/2201.00469/) (2022)
- [Behavior Planning of Autonomous Cars with Social Perception](https://mlsanity.com/paper/1905.00988/) (2019)
- [On-Robot Bayesian Reinforcement Learning for POMDPs](https://mlsanity.com/paper/2307.11954/) (2023)
- [Transformable Gaussian Reward Function for Socially-Aware Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2402.14569/) (2024)

## 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.