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

2025-12-16

AI Summary

Real-world robots must adapt in real time to gradual drift, transient disturbances, and abrupt structural changes in dynamically evolving environments. To address this, we propose an online Bayesian adaptive control framework tailored for nonlinear dynamics. Our method introduces a novel implicit changepoint detection mechanism grounded in data likelihood, decoupling offline representation learning from online closed-form Bayesian updating—enabling millisecond-scale relearning upon abrupt changes and continuous refinement under gradual drift, while preserving uncertainty calibration. By integrating latent-variable inference, adaptive regret analysis, and online probabilistic inference, the framework significantly enhances model robustness and responsiveness. Evaluated on inverted-pendulum simulations and real-world quadrotor experiments—including scenarios with swinging payloads and mid-air payload release—the approach achieves a 32% improvement in prediction accuracy, reduces disturbance recovery time by 47%, and lowers closed-loop trajectory tracking error by 58% relative to baseline methods.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Online Learning & Bandits
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Adapting robot control to evolving dynamics in real time
• Decoupling representation learning from online Bayesian adaptation
• Detecting changepoints to manage gradual, transient, or abrupt shifts

Innovation

• Online closed-form Bayesian updates for real-time adaptation
• Changepoint-aware mechanism to detect and respond to shifts
• Decoupled representation learning from online adaptation