Learning Fine Motion by Markov Mixtures of Experts

Compliant control is a standard method for performing fine manip(cid:173) ulation tasks, like grasping and assembly, but it requires estimation of the state of contact (s.o.c.) between the robot arm and the ob(cid:173) jects involved. Here we present a method to learn a model of the movement from measured data. The method requires little or no prior knowledge and the resulting model explicitly estimates the s.o.c. The current s.o.c. is viewed as the hidden state variable of a discrete HMM. The control dependent transition probabilities between states are modeled as parametrized functions of the mea(cid:173) surement.