比较惯性、占用、语义和意图信息在日常任务中人体运动预测中的效用
Comparing Utility of Inertial, Occupancy, Semantic, and Intent Information in Human Motion Prediction During Daily Tasks
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究使用人体运动扩散模型，通过收集九名受试者在室内日常任务中的运动数据，比较惯性、占用、语义和意图信息对运动预测的效用，发现意图信息最有效，整体预测误差比恒定速度基线降低42%。
AI中文摘要：
准确的人体运动预测对于在人类周围运行的机器人系统至关重要，尤其是在复杂的室内空间中。在本研究中，我们使用人体运动扩散模型评估了室内运动预测中不同信息来源的相对重要性。我们收集了一个数据集，包含九名天真的人类受试者在佩戴一副Meta Aria眼镜时进行模拟室内日常活动的数据。该数据集涵盖了大学校园内的十栋建筑，包含238分钟的日常任务间导航。总体而言，我们展示了比恒定速度基线提高42%的性能。包含身体运动、场景表示和眼睛注视固定数据显著减少了预测误差。发现语义信息对室内运动预测有用，但其程度低于室外导航。提供明确的意图信息比其他任何添加都更能减少误差，这表明纳入明确的意图估计或用户输入对于更精细的室内运动预测至关重要。眼睛注视固定作为少数意图指标之一，被发现特别有助于预测减速，并在没有占用地图的情况下为模型提供了基本的空间信息。这些结果是朝着在高度模糊的室内场景中预测人体运动迈出的第一步。代码将在接受后公开。项目页面：此HTTPS URL
英文摘要：
Accurate human motion prediction is crucial for robotic systems operating around people, particularly in complex indoor spaces. In this study, we assess the relative importance of different sources of information in indoor motion prediction with a human motion diffusion model. We collected a dataset of nine naive human subjects conducting simulated indoor daily activities while wearing a pair of Meta Aria glasses. This dataset includes ten buildings from a university campus, and encompasses 238 minutes of navigation between daily tasks. Overall, we demonstrate a 42% improvement beyond a constant velocity baseline. Including body motion, scene representation, and eye gaze fixation data significantly reduced prediction error. Semantic information was found to be useful for indoor motion prediction, but to a lesser degree than in outdoor navigation. Providing explicit intent information reduced error beyond any other addition, suggesting that incorporating explicit intent estimation or user input are fundamental for finer prediction of indoor motion. One of the few indicators of intent, eye gaze fixation, was found to be especially useful in predicting deceleration, and provided basic spatial information to the model in the absence of an occupancy map. These results are a first step towards predicting human motion in highly ambiguous indoor scenarios. Code will be made public upon acceptance. Project page: https://human-motion-diffusion.github.io/
