从正负演示和基于规则的规范中学习社交导航
Learning Social Navigation from Positive and Negative Demonstrations and Rule-Based Specifications
AI总结：
本研究提出一种结合数据驱动奖励与规则目标的社交导航框架，从正负演示学习密度奖励，并蒸馏为实时学生策略，在仿真和真实实验中提升成功率与效率。
摘要：
在动态人类环境中的移动机器人导航需要能够平衡对多样化行为的适应性与对安全约束的遵从性的策略。我们假设，将数据驱动的奖励与基于规则的目标相结合，可以使导航策略在适应性和安全性之间实现更有效的平衡。为此，我们开发了一个框架，从正负演示中学习基于密度的奖励，并辅以基于规则的避障和到达目标的目标函数。一个基于采样的前瞻控制器产生既安全又具有适应性的监督动作，随后将其蒸馏成一个紧凑的学生策略，该策略适用于具有不确定性估计的实时操作。在合成和电梯共乘模拟中的实验显示，在成功率与时间效率上相对于基线有一致的提升，并且与人类参与者的真实世界演示证实了部署的实用性。展示这项工作的视频可以在我们的项目页面 https://chanwookim971024.github.io/PioneeR/ 找到。
英文摘要：
Mobile robot navigation in dynamic human environments requires policies that balance adaptability to diverse behaviors with compliance to safety constraints. We hypothesize that integrating data-driven rewards with rule-based objectives enables navigation policies to achieve a more effective balance of adaptability and safety. To this end, we develop a framework that learns a density-based reward from positive and negative demonstrations and augments it with rule-based objectives for obstacle avoidance and goal reaching. A sampling-based lookahead controller produces supervisory actions that are both safe and adaptive, which are subsequently distilled into a compact student policy suitable for real-time operation with uncertainty estimates. Experiments in synthetic and elevator co-boarding simulations show consistent gains in success rate and time efficiency over baselines, and real-world demonstrations with human participants confirm the practicality of deployment. A video illustrating this work can be found on our project page https://chanwookim971024.github.io/PioneeR/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
