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.

本研究介绍了一种新的算法 BNBRL+，基于部分可观测的马尔科夫决策过程框架评估不可见区域的风险，并在不确定性下制定移动策略，通过将信念算法与贝叶斯神经网络相结合，根据人类的定位数据以概率推断信念，进一步整合机器人、人类和推断信念之间的动力学，确定导航路径并将社交规范嵌入奖励函数中，从而促进了社会感知导航。通过在各种风险环境中进行实验，验证了 BNBRL + 在导航拥挤环境中具有盲区时的有效性。模型在能见度受限的空间中有效导航并动态避开障碍物的能力可以显著提高自动驾驶车辆的安全性和可靠性。

利用贝叶斯强化学习辅助导航以避免盲点中的人群

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

Robot navigation has transitioned from prioritizing obstacle avoidance to
adopting socially aware navigation strategies that accommodate human presence.
As a result, the recognition of socially aware navigation within dynamic
human-centric environments has gained prominence in the field of robotics.
Although reinforcement learning technique has fostered the advancement of
socially aware navigation, defining appropriate reward functions, especially in
congested environments, has posed a significant challenge. These rewards,
crucial in guiding robot actions, demand intricate human-crafted design due to
their complex nature and inability to be automatically set. The multitude of
manually designed rewards poses issues with hyperparameter redundancy,
imbalance, and inadequate representation of unique object characteristics. To
address these challenges, we introduce a transformable gaussian reward function
(TGRF). The TGRF significantly reduces the burden of hyperparameter tuning,
displays adaptability across various reward functions, and demonstrates
accelerated learning rates, particularly excelling in crowded environments
utilizing deep reinforcement learning (DRL). We introduce and validate TGRF
through sections highlighting its conceptual background, characteristics,
experiments, and real-world application, paving the way for a more effective
and adaptable approach in robotics.The complete source code is available on
this https URL

机器人导航已从优先考虑避障转向采用能够适应人类存在的社交化导航策略，这导致社交化导航在机器人领域内具有显著地位。我们介绍并验证了可变高斯奖励函数（TGRF），该函数显著减轻了超参数调整的负担，在各种奖励函数中具备适应性，并在深度强化学习中展示加速学习速度，尤其在拥挤环境中表现出色，从而为机器人学中的更有效和适应性的方法铺平了道路。