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

使用运动学、动态和符号先验和控制进行全身运动预测 (P4)

• 批准号: 332969467
• 负责人: Professor Dr. Reinhard Klein, since 4/2020
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/332969467?language=en
• 关键词: Full; Body; Motion; Anticipation; using

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.

该项目的目标是预测人体全身运动。将纯粹的数据驱动与基于物理的建模方法相结合，将实现基于不同性质的非常稀疏的传感器信号（例如惯性测量单元、肌电图传感器或地面接触传感器）的全身运动预测。通过基于模型的预期组件（包括来自平衡控制、物理约束的信息）扩展基于物理的层上的信息是另一个重要目标，以允许从与现有知识库中记录的运动范围类似的运动范围到新的运动范围（尤其是与残疾相关的运动范围）进行稳健的推断。预期的全身运动可用于确定协作任务以及直接夹带和调节正在进行的运动行为的最佳机器人位置。从研究单位其他项目获得的符号标签和轨迹将被纳入运动的额外先验知识，这将减少计算时间，稳定短期预测，甚至为长期预测的方法打开大门。