Segmentation and generalisation for writing skills transfer from humans to robots

• Main Authors: Chunxu Li, Chenguang Yang, Cinzia Giannetti
• Format: Article
• Language: English
• Published: Wiley 2019-01-01
• Series: Cognitive Computation and Systems
• Subjects: motion control; humanoid robots; gaussian processes; regression analysis; human-robot interaction; trajectory control; mixture models; nonlinear control systems; difference equations; matlab; control engineering computing; robot programming; kuka iiwa robot; difference method; trajectories data; nonlinear system; dynamic movement primitive; dmp; gaussian mixture model; gaussian mixture regression algorithm; teaching by demonstration technique; trajectories modelling; writing skills transfer
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/ccs.2018.0005

In this study, the authors present an enhanced generalised teaching by demonstration technique for a KUKA iiwa robot. Movements are recorded from a human operator, and then the recorded data are sent to be segmented via MATLAB by using the difference method (DV). The outputted trajectories data are used to model a non-linear system named dynamic movement primitive (DMP). For the purpose of learning from multiple demonstrations correctly and accurately, the Gaussian mixture model is employed for the evaluation of the DMP in order to modelling multiple trajectories by the teaching of demonstrator. Furthermore, a synthesised trajectory with smaller position errors in 3D space has been successfully generated by the usage of the Gaussian mixture regression algorithm. The proposed approach has been tested and demonstrated by performing a Chinese characters writing task with a KUKA iiwa robot.