JEPA World ModelsExpert
AVL-JEPA: Preventing Causal Dynamics Information Collapse In Joint Embedding Predictive Architecture World Models
This arXiv preprint introduces AVL-JEPA, a training method that stops joint-embedding world models from discarding action-conditioned physical dynamics while preserving visual representations. Across four simulated robot control tasks, it reaches 81.5% success on PushT and 88.7% on TwoRoom under Gaussian input noise, versus 15.3% and 65.7% for the baseline, while maintaining competitive clean-environment performance. The work matters for robot planners that must stay reliable when camera or state observations are noisy.