This letter presents a framework for recognition and prediction of ongoing human motions. The predictions generated by this framework could be used in a controller for a robotic device, enabling the emergence of intuitive and predictable interactions between humans and a robotic collaborator. The framework includes motion onset detection, phase speed estimation, intent estimation and conditioning. For recognition and prediction of a motion, the framework makes use of a motion model database. This database contains several motion models learned using the probabilistic Principal Component Analysis (PPCA) method. The proposed framework is evaluated with joint angle trajectories of eight subjects performing squatting, stooping and lifting tasks. The motion onset and phase speed estimation modules are first evaluated separately. Next, an evaluation of the full framework provides more insight in the current challenges regarding motion prediction. A brief comparison between PPCA and the Probabilistic Movement Primitives (ProMP) method for learning motion models is made based on the influence of both methodologies on the performance of the framework. Both PPCA and ProMP motion models are able to predict motions over a short time horizon but struggle to predict motions over a longer horizon.

中文翻译：

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使用概率运动模型识别和预测人机协作中人体运动的框架

这封信提出了识别和预测正在进行的人体运动的框架。该框架生成的预测可用于机器人设备的控制器，从而实现人类与机器人合作者之间直观且可预测的交互。该框架包括运动开始检测、相位速度估计、意图估计和调节。为了识别和预测运动，该框架使用运动模型数据库。该数据库包含使用概率主成分分析 (PPCA) 方法学习的多个运动模型。所提出的框架是用八名执行蹲下、弯腰和举重任务的受试者的关节角度轨迹进行评估的。首先分别评估运动开始和相位速度估计模块。下一个，对完整框架的评估提供了对当前关于运动预测的挑战的更多见解。基于两种方法对框架性能的影响，对用于学习运动模型的 PPCA 和 Probabilistic Movement Primitives (ProMP) 方法进行了简要比较。PPCA 和 ProMP 运动模型都能够预测短时间范围内的运动，但难以预测较长时间范围内的运动。