ARCADE: Adaptive Robot Control with Online Changepoint-Aware Bayesian Dynamics Learning

TL;DR

This paper introduces ARCADE, a real-time adaptive control framework for robots that detects and responds to dynamic changes in the environment using Bayesian methods, improving robustness and accuracy.

Contribution

It presents a novel changepoint-aware Bayesian dynamics learning approach that decouples offline representation learning from online adaptation for robotic systems.

Findings

Enhanced predictive accuracy in robotic dynamics.

Faster recovery from abrupt changes.

Maintained calibrated uncertainty during adaptation.

Abstract

Real-world robots must operate under evolving dynamics caused by changing operating conditions, external disturbances, and unmodeled effects. These may appear as gradual drifts, transient fluctuations, or abrupt shifts, demanding real-time adaptation that is robust to short-term variation yet responsive to lasting change. We propose a framework for modeling the nonlinear dynamics of robotic systems that can be updated in real time from streaming data. The method decouples representation learning from online adaptation, using latent representations learned offline to support online closed-form Bayesian updates. To handle evolving conditions, we introduce a changepoint-aware mechanism with a latent variable inferred from data likelihoods that indicates continuity or shift. When continuity is likely, evidence accumulates to refine predictions; when a shift is detected, past information is tempered to enable rapid re-learning. This maintains calibrated uncertainty and supports probabilistic reasoning about transient, gradual, or structural change. We prove that the adaptive regret of the framework grows only logarithmically in time and linearly with the number of shifts, competitive with an oracle that knows timings of shift. We validate on cartpole simulations and real quadrotor flights with swinging payloads and mid-flight drops, showing improved predictive accuracy, faster recovery, and more accurate closed-loop tracking than relevant baselines.