World Models as Reference Trajectories for Rapid Motor Adaptation
Track: full paper
Keywords: Motor Adaptation, Model-based RL, Rapid Adaptation, Model Reference Control, Data Efficiency, Naturalistic Motor Control
TL;DR: Dual control framework that uses world model predictions as implicit reference trajectories for rapid adaptation
Abstract: Deploying learned control policies in real-world environments poses a fundamental challenge: when system dynamics change unexpectedly, performance degrades until models are retrained on new data. We introduce a dual control framework that uses world model predictions as implicit reference trajectories for rapid adaptation, while preserving the policy's optimal behavior. Our method separates the control problem into long-term reward maximization through reinforcement learning and robust motor execution through rapid latent control. In continuous control tasks under varying dynamics, this achieves significantly faster adaptation compared to model-based RL baselines while maintaining near-optimal performance. This dual architecture combines the benefits of flexible policy learning through reinforcement learning with the robust adaptation capabilities of classical control, providing a principled approach to maintaining performance in high-dimensional locomotion tasks under varying dynamics.
Presenter: ~Carlos_Stein_Brito1
Format: Maybe: the presenting author will attend in person, contingent on other factors that still need to be determined (e.g., visa, funding).
Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding availability would significantly influence their ability to attend the workshop in person.
Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.
Submission Number: 42
Loading