Discriminative and adaptive imitation in uni-manual and bi-manual tasks
This paper addresses the problems of what to imitate and how to imitate in simple uni and bi-manual manipulatory tasks. To solve the what to imitate issue, we use a probabilistic method, based on Hidden Markov Models (HMM), to extract the relative importance of reproducing either the gesture or the specific hand path in a given task. This allows us to determine a metric of imitation performance. To solve the how to imitate issue, we compute the trajectory that optimizes the metric given the constraints of the robots body. We validate the methods using a series of experiments where a human demonstrator uses kinesthetics in order to teach a robot how to manipulate simple objects.
hmm discriminative imitation learning humanoid robots kinesthetic teaching lagrange optimization