Data-driven model predictive control (MPC) has demonstrated significant
potential for improving robot control performance in the presence of model
uncertainties. However, existing approaches often require extensive offline
data collection and computationally intensive training, limiting their ability
to adapt online. To address these challenges, this paper presents a fast online
adaptive MPC framework that leverages neural networks integrated with
Model-Agnostic Meta-Learning (MAML). Our approach focuses on few-shot
adaptation of residual dynamics – capturing the discrepancy between nominal and
true system behavior – using minimal online data and gradient steps. By
embedding these meta-learned residual models into a computationally efficient
L4CasADi-based MPC pipeline, the proposed method enables rapid model
correction, enhances predictive accuracy, and improves real-time control
performance. We validate the framework through simulation studies on a Van der
Pol oscillator, a Cart-Pole system, and a 2D quadrotor. Results show
significant gains in adaptation speed and prediction accuracy over both nominal
MPC and nominal MPC augmented with a freshly initialized neural network,
underscoring the effectiveness of our approach for real-time adaptive robot
control.

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