LAIF: Learning by demonstration through Active Incremental data Fusion of task observations

As William Arthur Ward once said: “Curiosity is the wick in the candle of learning”. In this work, inspired by the curiosity that drives human beings, we propose an active perception framework aiming towards the incremental learning through visual observations of multiple demonstrations, performed by a human. The proposed framework, considers an active observer, i.e. a sensor with the ability to move in Cartesian space, which acts in order to maximize the information gathered towards the modelling of the observed motion. For the the encoding of the human action, a Dynamic Movement Primitives (DMP) model is utilized. In the core of the method, a Kalman-filter-inspired data fusion mechanism is employed that exploits the knowledge of the trained DMP model, and accounts for the uncertainty of the current knowledge and the measurement uncertainty, in an iterative manner. The proposed method is tested using a UR5e robotic manipulator with an eye-in-hand ZED 2 camera in two different scenarios, involving the motion of the human hand along a curve, considering the existence of occlusions within the field of view of the camera, as well as anocclusion-free case. The proposed method is theoretically proven to minimize the uncertainty in each repetition and its performance is demonstrated trough the results of the experimental evaluation.