Unsupervised optimization approach to in situ calibration of collaborative human-robot interaction tools

In this work we are proposing an intuitive tool based on motion capture system for programming by demonstration tasks in robot manipulation. For a robot manipulator set in a working environment equipped with any external measurement sys-tem, we propose an online calibration method based on unsupervised learning and simplex optimization. Without loos of generality the Nelder-Mead simplex method is used to calibrate the rigid transforms of the robot tools and environment based on motion capture system recordings. Fast optimization procedure is enabled through dataset subsampling using iterative clustering and outlier detection procedure. The online calibration enables customization and execution of programming by demonstration tasks in real time.