探索社交运动潜在空间与人类意识以实现拥挤环境中的有效机器人导航
Exploring Social Motion Latent Space and Human Awareness for Effective Robot Navigation in Crowded Environments
AI总结：
本文提出利用社交运动潜在空间生成控制指令，并引入人类意识概念，以提升拥挤环境中机器人导航的成功率、平滑性和预见性。
摘要：
本文提出了一种新颖的社交机器人导航方法，通过学习从社交运动潜在空间生成机器人控制指令。通过利用该社交运动潜在空间，所提方法在社交导航指标（如成功率、导航时间和轨迹长度）上取得了显著改进，同时生成更平滑（更少的抖动和角度偏差）且更具预见性的轨迹。通过与基线模型在各种场景下的比较，证明了所提方法的优越性。此外，将人类对机器人的意识概念引入社交机器人导航框架，表明纳入人类意识能够产生更短且更平滑的轨迹，这得益于人类与机器人积极互动的能力。
英文摘要：
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.
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。
