This work proposes a novel approach to social robot navigation by learning to
generate robot controls from a social motion latent space. By leveraging this
social motion latent space, the proposed method achieves significant
improvements in social navigation metrics such as success rate, navigation
time, and trajectory length while producing smoother (less jerk and angular
deviations) and more anticipatory trajectories. The superiority of the proposed
method is demonstrated through comparison with baseline models in various
scenarios. Additionally, the concept of humans' awareness towards the robot is
introduced into the social robot navigation framework, showing that
incorporating human awareness leads to shorter and smoother trajectories owing
to humans' ability to positively interact with the robot.

本研究提出了一种新方法，通过从社交动作潜空间中学习生成机器人控制来实现社交机器人导航。该方法在社交导航度量指标（如成功率、导航时间和轨迹长度）方面取得了显著改进，同时产生更平滑（较小加速度和角度偏差）和更具预测性的轨迹。通过与基准模型在不同场景下的比较，验证了该方法的优越性。此外，引入了人类对机器人的认知概念到社交机器人导航框架中，表明融入人类的意识可以导致更短、更平滑的轨迹，因为人类能够与机器人积极互动。