Incremental Motion Learning with Gaussian Process Modulated Dynamical Systems
نویسندگان
چکیده
Dynamical Systems (DS) for robot motion modeling are well-suited for efficient robot learning and control. Our focus in this extended abstract is on autonomous dynamical systems, which represent a motion plan completely without dependency on time. We develop a method that allows to locally reshape an existing, stable autonomous DS without risking introduction of additional equilibrium points or unstable behavior. This is achieved by locally applying rotations and scalings to the original dynamics. Gaussian Processes are then used to incrementally learn reshaped dynamical systems. We briefly report on preliminary results from applying the proposed methodology for learning 2d hand-writing motions.
منابع مشابه
Incremental motion learning with locally modulated dynamical systems
Dynamical Systems (DS) for robot motion modeling are a promising approach for efficient robot learning and control. Our focus in this paper is on autonomous dynamical systems, which represent a motion plan without dependency on time. We develop a method that allows to locally reshape an existing, stable nonlinear autonmous DS while preserving important stability properties of the original syste...
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