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 https://github.com/JinnnK/TGRF

翻译：机器人导航已从优先考虑避障转变为采用适应人类存在的社交感知导航策略。因此，在动态以人为中心的环境中识别社交感知导航在机器人领域日益受到重视。尽管强化学习技术推动了社交感知导航的发展，但定义合适的奖励函数（尤其在拥挤环境中）仍是一项重大挑战。这些奖励函数在引导机器人行为中至关重要，但由于其复杂性和无法自动设置的局限性，需要复杂的人工设计。大量手动设计的奖励函数会导致超参数冗余、不平衡以及无法充分表征独特物体特征等问题。为解决这些挑战，我们提出了一种可变换高斯奖励函数（TGRF）。TGRF显著降低了超参数调优负担，展现出跨多种奖励函数的适应性，并加速了学习速率，尤其在使用深度强化学习（DRL）的拥挤环境中表现优异。我们通过概念背景、特性、实验及实际应用等章节对TGRF进行介绍与验证，为机器人领域提供更高效、更具适应性的方法。完整源代码已开源至 https://github.com/JinnnK/TGRF