论文标题
独奏T-Dirl：基于轨迹排名深的逆增强学习的社会意识的动态本地规划师
SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning
论文作者
论文摘要
这项工作通过建立最近提出的轨迹排名最大的最大熵深度逆增强学习（T-Medirl），为拥挤的环境中具有社会意识的本地规划师的新框架提出了一个新的框架。为了解决社会导航问题，我们的多模式学习计划者明确考虑了社会互动因素以及社会意识因素，以从人类示威中学习奖励功能。此外，我们建议使用机器人周围行人的突然速度变化来解决人类示范中的亚次临时性，从而提出一种新颖的轨迹排名评分。我们的评估表明，这种方法可以成功地使机器人在拥挤的社交环境中导航，并在成功率，导航时间和入侵率方面胜过最先进的社会导航方法。
This work proposes a new framework for a socially-aware dynamic local planner in crowded environments by building on the recently proposed Trajectory-ranked Maximum Entropy Deep Inverse Reinforcement Learning (T-MEDIRL). To address the social navigation problem, our multi-modal learning planner explicitly considers social interaction factors, as well as social-awareness factors into T-MEDIRL pipeline to learn a reward function from human demonstrations. Moreover, we propose a novel trajectory ranking score using the sudden velocity change of pedestrians around the robot to address the sub-optimality in human demonstrations. Our evaluation shows that this method can successfully make a robot navigate in a crowded social environment and outperforms the state-of-art social navigation methods in terms of the success rate, navigation time, and invasion rate.
