Human-aware navigation is a complex task for mobile robots, requiring an autonomous navigation system capable of achieving efficient path planning together with socially compliant behaviors. Social planners usually add costs or constraints to the objective function, leading to intricate tuning processes or tailoring the solution to the specific social scenario. Machine Learning can enhance planners' versatility and help them learn complex social behaviors from data. This work proposes an adaptive social planner, using a Deep Reinforcement Learning agent to dynamically adjust the weighting parameters of the cost function used to evaluate trajectories. The resulting planner combines the robustness of the classic Dynamic Window Approach, integrated with a social cost based on the Social Force Model, and the flexibility of learning methods to boost the overall performance on social navigation tasks. Our extensive experimentation on different environments demonstrates the general advantage of the proposed method over static cost planners.

翻译：人类感知导航对移动机器人而言是一项复杂任务，需要自主导航系统既能实现高效路径规划，又能体现符合社会规范的行为。传统社会规划器通常会在目标函数中添加代价或约束条件，导致参数调优过程繁琐，或需针对特定社会场景定制解决方案。机器学习可增强规划器的适应性，帮助其从数据中学习复杂社会行为。本研究提出一种自适应社会规划器，利用深度强化学习智能体动态调整用于评估轨迹的代价函数加权参数。该规划器融合了经典动态窗口法的鲁棒性、基于社会力模型的社会代价，以及学习方法在提升社会导航任务整体性能上的灵活性。我们在不同环境中的广泛实验表明，所提方法相比静态代价规划器具有显著优势。