Programming by demonstration in the application of rehabilitation robots

The rising number of elderly and disabled persons leads to the increasing demand for reha-bilitation robots for the support in daily life actions. The major factor limiting the application of robots in this domain is the programming of the robot since a detailed knowledge of the daily life activities as well as an effective mechanism to deal with the diverse environment is not available. The most promising way to alleviate this problem is to take advantage of the human experience and skills in performing daily activities and to transfer them to the robot. For this, the concept of Programming by Demonstration (PbD) is believed to be an intuitive and preferable solution. However, classic PbD methods will fail in the situations where the environment in which the task is to be performed differs from that in the demonstration, since important parts of the human skill are lost in that programming process. This work proposes a comprehensive solution for this problem, which is implemented and tested for the exemplary daily life action 'pouring a beverage from a bottle to a glass' (pour-in in short). This solution consists of three advanced methods: The parameter-based trajectory generation, the inclusion of the closed control loop into the PbD process and the implementa-tion of an anti-dripping movement into a pour-in. In the parameter-based trajectory genera-tion, human demonstration is used to obtain the characteristics of a pour-in trajectory repre-senting the human skills. This information is used to autonomously generate a general pour-in trajectory that is optimized to the current task conditions (e.g. the size of bottle and glass). This general pour-in trajectory is then processed by a closed control loop that manages other task restrictions like the target filling liquid. Based on the concept of cascade control loops two control systems reflecting different control strategies of human beings in a pour-in are designed in this work: The first is the three-point based cascade weight/flow rate control (TCC), which applies the human strategy to control the flow rate within minimum and maxi-mum limits. These limits depend on the filling ratio of the liquid. The second is the monitored cascade weight/flow rate control (MCC) based on the imitation of a particularity of the human demonstration, which is the unidirectivity of the movement. Experiments with the rehabilita-tion robot system FRIEND reveal that the lager delay time of the system influences the con-trol accuracy considerable due to the corresponding delays in the system response. Therefore, a number of strategies are developed and implemented into the control loops to dampen the impact of the time delay. Finally, the pour-in process is accomplished and optimized by the inclusion of an anti-dripping movement, whose implementation is based on human demon-strations. By the application of particular trajectory transformations it is possible to apply only one anti-dripping trajectory to any pour-in process. Experiments prove that a robust task execution under various task conditions is achieved to-gether with a high accuracy in reaching the target filling weight. This shows that the applica-tion of these advanced PbD methods for the implementation of pour-in processes into a reha-bilitation robot is successful and that this comprehensive solution is able to confer the abilities of human autonomous environment adaptation. The promising results qualify the developed methods to serve as a starting point for future implementations of additional daily life tasks