Imitation Learning System to Assist Human Task Interactively

This paper proposes an imitation learning system to generate trajectories by which a robot supports a human with close physical assistance adapting to human movements and daily life environments. The proposed system is composed of 1) division algorithms, 2) learning algorithms and 3) assistance algorithms. 1) In division algorithms, the system measures time series of human task execution data and divides them into multiple motion segments automatically. This division is based on standard deviations of motion errors between measured trajectories and an ideal trajectory where the ideal trajectory is mean of all measured human trajectories and is expected to achieve the purpose of human task successfully. Since an important motion parameter is paid attention to by the human and has small standard deviation of errors, series of measured data are divided into segment motions at the points where the importance of parameters changes suddenly. Thus this division is guaranteed to accord with human attention. 2) In learning algorithms, the system learns trajectories with dynamic neural network (DNN). Since the DNN has convergence, generated trajectories can converge to an ideal trajectory. The importance of each parameter, in other words how much attention human pays to the parameter, is evaluated as how small the standard deviation of errors is. The DNN learns trajectories reflecting the evaluated importance of parameters to accord with human feeling. 3) In assistance algorithms, the system judges when to start assistance by the assumption of multiplied errors of motion parameters by the respective importance. In assistance algorithms, the system also connects generated trajectories of motion segments smoothly. An experiment to support human drink task was performed successfully where the proposed system judged not only when to start assistance to the task but also execute assistance when a cup was about to incline too much not to spill water