Article

RoKON: Efficient and Adaptive Data-Enabled Predictive Control for Robotic Manipulation

The paper proposes RoKON, a data-driven predictive control method for robotic manipulation that does not require learning an explicit dynamics model. The method uses a Gaussian-kernel nonlinear predictor to represent the input-output data relationship, proposes a damped Gauss-Newton method that exploits the least-squares structure of the control problem to achieve real-time control, and introduces a hybrid buffer management strategy that incorporates informative online data while retaining relevant offline data, allowing adaptation to changes in dynamics.

Part of the event: RoKON: Adaptive Predictive Control Without Learning Explicit Models

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