Computational elements of robot learning by imitation

Robot learning by imitation makes an increasing body of robotics research. Imitation learning complements motor learning techniques by restricting the search space to a computationally tractable subset. Imitation learning search for spatial and temporal invariants across several demonstrations. These invariants are task- dependent. This talk will present an algorithm that determines the key features of an imitation task through a comparative analysis of the data in joint space, carthesian space and visual space. These features are used to control the reproduction of the observed motion by a 30 degrees of freedom humanoid robot