Variable duration movement encoding with minimal intervention control

Programming by Demonstration (PbD) offers a user-friendly way to transfer skills from human to robot. Typically, demonstration data do not contain the control inputs required to reproduce the demonstrated skill. These can be obtained from a low-level controller that tracks the modeled movement. We present a PbD approach for minimal intervention control - a control strategy that only corrects perturbations that interfere with task performance. The novelty of our approach is the probabilistic encoding of the movement duration, providing a performance measure that enables minimal intervention control in a temporal sense. This is achieved by combining a probabilistic movement encoding based on Hidden Semi-Markov Model (HSMM) with Model Predictive Control (MPC). The probabilistic model is used to construct an objective function, hereby assuming that variance is a measure for task performance. The proposed method is demonstrated in a robot experiment and compared with our earlier work.

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