predictive-control

This paper presents a goal-agnostic control framework for partial differential equations using a joint-embedding predictive architecture (JEPA) with a lightweight 2D ViT encoder and action-conditioned latent dynamics, showing that using a learned physical observable probe outperforms raw latent distance for control tasks.

This paper introduces a predictive formulation for deep reinforcement learning that augments the state space with future reference horizons to enable anticipatory control for trajectory tracking. Simulation results show significant error reduction, though zero-shot transfer to physical hardware reveals a sim-to-real gap.