Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics

ArXi:2604.13366v1 Announce Type: new Accurate modeling of robot dynamics is essential for model-based control, yet remains challenging under distributional shifts and real-time constraints. In this work, we formulate system identification as an in-context meta-learning problem and compare deterministic and generative sequence models for forward dynamics prediction. We take a Transformer-based meta-model, as a strong deterministic baseline, and