Article

Online Learning of Heterogeneous Locomotion Dynamics for Precise Mobile Manipulation

The study proposes an online method for learning closed-loop dynamics under a frozen locomotion policy, using a contaminated Gaussian model to distinguish predictable from unpredictable responses and combining a Gaussian process with random Fourier features to build an online learner that reduces bias from heterogeneous observations. Combined with model predictive control (MPC), the method predicts floating-base motion more accurately; in simulation and hardware experiments, end-effector tracking error was 0.01 m, and a finite-sample error bound for the online learning is provided.

Part of the event: Online Learning of Closed-Loop Dynamics Improves Mobile Manipulation Accuracy

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