Title:

Learning Motion Trajectories from Phase Space Analysis of the Demonstration

A major goal of learning from demonstration is task generalization via observation of a teacher. We propose a novel framework for learning motion from a single demonstration. Our approach reconstructs the demonstrated trajectory’s phase space curve via a linear piece wise regression method. We approximate dynamics of trajectory segments with linear time invariant equations, each yielding closed form solutions. We show convergence to desired phase space states via an energy-based analysis. The robustness of the model is evaluated on a robot for a sequential trajectory task. Additionally, we show the advantages that the phase space model has over the dynamic motion primitive for a kinematic based task.

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