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

人类活动的真实数据在许多领域是必不可少的，但同时也很难获得，成本也很高。例如，数据被用于医学、航空航天的记录目的，也被用于研究人与机器人相互作用的行为学。在深度学习或从观察中学习等机器学习中，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有数据的一种有前途的方式。目前，模型预测控制是机器人在避碰、时间和路径优化等方面进行实时运动规划的最新技术。因此，该研究项目致力于建模预测运动规划，以通过机器人引导的手眼相机无缝记录人类活动和相关的环境变化。目标是在不干扰人类的情况下，通过在活动附近记录变化的视角来最大化在整个相机运动中积累的信息增益。随着模型预测控制在机器人学中的成功应用，在人类运动不确定情况下的碰撞避免方面以及相对于更高级别的总体目标评估整个时空运动的成功的特定任务成本函数的系统开发方面，这一目标带来了新的挑战。将在机器人机械手使用从观察中学习的示例性应用的背景下对这些方法进行详细和集体的评估。

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.