Motion generation by reference-point-dependent trajectory HMMs

This paper presents an imitation learning method for object manipulation such as rotating an object or placing one object on another. In the proposed method, motions are learned using reference-point-dependent probabilistic models. Trajectory hidden Markov models (HMMs) are used as the probabilistic models so that smooth trajectories can be generated from the HMMs. The method was evaluated in physical experiments in terms of motion generation. In the experiments, a robot learned motions from observation, and it generated motions under different object placement. Experimental results showed that appropriate motions were generated even when the object placement was changed.