Physically Interpretable World Models via Weakly Supervised Representation Learning
arXiv:2412.12870v5 Announce Type: replace Abstract: Learning predictive models from high-dimensional sensory observations is fundamental for cyber-physical systems, yet the latent representations learned by standard world models lack physical interpretability. This limits their reliability, generalizability, and applicability to safety-critical tasks. We…
