Modeling and planning high-level in-hand manipulation actions from human knowledge and active learning from demonstration

We propose a method to plan in-hand manipulation actions with a robotic anthropomorphic hand. We consider in-hand manipulation actions as sequences between canonical grasp types identified in the humans. Our work concerns the generation of this sequence, which should be autonomous and fast enough to be performed on-line. We use a Markov Decision Process (MDP) governing the transitions between grasp types, depending on the object and on the goal grasp. The policy is learnt directly from human behavior, after an initialization using an empirical estimation of the state action probabilities of the MDP. Then, the policy is finely learnt from samples of human in-hand manipulation records. These samples are chosen using active learning, in order to maximize the useful information of every record, and speed up the learning process. For planning, the policy gives the sequence with highest probability of success. We show a serie of realistic human-like grasp transition sequences derived from the proposed method.