Model predictive motion planning for robot-assisted observation and recording of humanactivities

用于机器人辅助观察和记录人类活动的模型预测运动规划

• 批准号: 497071854
• 负责人: Professor Dr.-Ing. Torsten Bertram
• 金额: --
• 依托单位: Lehrstuhl für Regelungssystemtechnik (RST)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/497071854?language=en
• 关键词: Model; predictive; motion; planning; robot

Authentic data of human activities are essential in many fields but at the same time difficult and costly to acquire. Data is used, for example, for documentation purposes in medicine, aerospace, and also in ethology for research on human-robot interaction. In machine learning such as deep learning or learning from observation, the quantity and quality of data determine the performance of new approaches. Robotic observationand recording of human activities is a promising way to economically access rare and exclusive data. Nowadays, model predictive control is state of the art for real-time motion planning under the aspects of collision avoidance as well as time and path optimality for robotic manipulators. The research project is therefore dedicated to model predictive motion planning for a seamless recording of human activities and the associated changes to the environment by a robot-guided eye-in-hand camera. The goal is to maximize the information gain accumulated over the entire camera motion by recording from changing perspectives in close proximity to the activity without disturbing the human. With the successful application of model predictive control in robotics, new challenges arise following this objective, both, in terms of collision avoidance under uncertainties of human motion, and systematic development of task-specific cost functions that evaluate the success of the entire spatio-temporal motion with respect to a higher-level overall goal.The methods will be evaluated, both, in detail and collectively in the context of an exemplary application in which a robotic manipulator will reproduce the observed activity using learning from observation.

人类活动的真实数据在许多领域都是必不可少的，但同时又很难获得，而且费用很高。例如，数据用于医学、航空航天以及人类行为学中的人类-机器人交互研究的文档目的。在深度学习或观察学习等机器学习中，数据的数量和质量决定了新方法的性能。机器人观察和记录人类活动是经济地获取稀有和独家数据的一种有前途的方式。目前，模型预测控制在机器人避碰、时间和路径最优等方面的实时运动规划中处于领先地位。因此，该研究项目致力于模型预测运动规划，以便通过机器人引导的手眼相机无缝记录人类活动和环境的相关变化。我们的目标是最大限度地提高信息增益积累在整个摄像机运动记录从变化的角度在接近的活动，而不打扰人。随着模型预测控制在机器人中的成功应用，在这一目标之后出现了新的挑战，无论是在人体运动的不确定性下的碰撞避免方面，还是系统地开发特定于任务的成本函数，该成本函数相对于更高级别的总体目标评估整个时空运动的成功。在示例性应用的上下文中详细地和共同地描述，在该示例性应用中，机器人操纵器将使用从观察中学习来再现观察到的活动。