Full Body Motion Anticipation using Kinematic, Dynamic, and Symbolic Priors and Controls (P4)

该项目的目标是预测人体的全身运动。将纯数据驱动和基于物理的建模方法相结合，将实现基于非常稀疏的不同性质的传感器信号(例如惯性测量单元、肌电传感器或地面接触传感器)的全身运动预测。通过基于模型的预测组件(包括来自平衡控制、物理约束的信息)扩展基于物理学的层上的信息是另一个重要目标，以便能够从类似于现有知识库中记录的运动范围到新的运动范围，特别是与残疾有关的运动范围进行稳健的外推。预期的全身运动可用于确定协作任务的最优机器人位置以及对正在进行的运动行为的直接夹带和调节。从研究单位的其他项目获得的启示的象征性标签和轨迹将被纳入，作为关于运动的额外先验知识，这将减少计算时间，将稳定短期预测，甚至为该方法打开通往长期预期的大门。

The goal of the project is the anticipation of human full body motions. Combining purely data-driven with physics-based modeling approaches, anticipation of full body motions on the basis of very sparse sensor signals of different nature, e.g., inertial measurement units, EMG sensors, or ground-contact sensors, will be realized. Extending the information on the physics-based layer by model-based anticipation components (including information from balance control, physical constraints) is another important objective to allow robust extrapolations from the range of motions similar to ones recorded in an existing knowledge base to new motion ranges, especially those relatedto disabilities. The anticipated whole-body motions can be used to determine the optimal robot placement for collaborative tasks and for direct entrainment and modulation of ongoing motor behavior. Symbolic labels and trajectories from affordances obtained from other projects of the research unit will be incorporated as additional a priori knowledge on motions, which will reduce computations times, will stabilize short term predictions and even open the door for the method to long-term anticipations.