Generalizing to New Physical Systems via Context-Informed Dynamics Model
This addresses a key limitation in modeling physical systems for applications like robotics or physics simulation, though it appears incremental as it builds on existing adaptation methods.
The paper tackles the problem of data-driven models failing to generalize to unseen physical systems with shared dynamics but different contexts, proposing the CoDA framework that achieves state-of-the-art generalization results on nonlinear dynamics across various domains.
Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different physical contexts. We propose a new framework for this key problem, context-informed dynamics adaptation (CoDA), which takes into account the distributional shift across systems for fast and efficient adaptation to new dynamics. CoDA leverages multiple environments, each associated to a different dynamic, and learns to condition the dynamics model on contextual parameters, specific to each environment. The conditioning is performed via a hypernetwork, learned jointly with a context vector from observed data. The proposed formulation constrains the search hypothesis space to foster fast adaptation and better generalization across environments. We theoretically motivate our approach and show state-of-the-art generalization results on a set of nonlinear dynamics, representative of a variety of application domains. We also show, on these systems, that new system parameters can be inferred from context vectors with minimal supervision. Code is available at https://github.com/yuan-yin/CoDA .