在虚拟现实中从动态演示学习基于深度视觉的个性化机器人导航
Learning Depth Vision-Based Personalized Robot Navigation From Dynamic Demonstrations in Virtual Reality
AI总结：
提出基于深度视觉的个性化机器人导航学习框架，利用虚拟现实动态演示和变分自编码器感知流水线，实现仅依赖深度图像的首个个性化导航控制器。
摘要：
为了获得最佳的人机交互体验，机器人的导航策略应考虑用户的个人偏好。本文提出一个学习框架，并辅以感知流水线，从用户演示中训练基于深度视觉的个性化导航控制器。我们的虚拟现实界面支持在用户运动下演示机器人导航轨迹，适用于动态交互场景。该新颖的感知流水线结合了变分自编码器和运动预测器。它将感知到的深度图像压缩为潜在状态表示，使学习智能体能够对机器人的动态环境进行高效推理。在详细分析和消融研究中，我们评估了感知流水线的不同配置。为进一步量化导航控制器的个性化质量，我们开发并应用了一种基于Fréchet Distance的新颖指标来衡量偏好反映。我们讨论了机器人在各种虚拟场景中的导航性能，并展示了首个仅依赖深度图像的个性化机器人导航控制器。补充视频在线提供。
英文摘要：
For the best human-robot interaction experience, the robot's navigation policy should take into account personal preferences of the user. In this paper, we present a learning framework complemented by a perception pipeline to train a depth vision-based, personalized navigation controller from user demonstrations. Our virtual reality interface enables the demonstration of robot navigation trajectories under motion of the user for dynamic interaction scenarios. The novel perception pipeline enrolls a variational autoencoder in combination with a motion predictor. It compresses the perceived depth images to a latent state representation to enable efficient reasoning of the learning agent about the robot's dynamic environment. In a detailed analysis and ablation study, we evaluate different configurations of the perception pipeline. To further quantify the navigation controller's quality of personalization, we develop and apply a novel metric to measure preference reflection based on the Fréchet Distance. We discuss the robot's navigation performance in various virtual scenes and demonstrate the first personalized robot navigation controller that solely relies on depth images. A supplemental video highlighting our approach is available online.
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。
