$\Sigma$-LeWM: Learning World Models via S-Information for Robot Control
Overview
This paper introduces $\Sigma$-LeWM, an extension of the LeWorldModel architecture that incorporates backward dynamics and inverse dynamics action predictors alongside a latent forward dynamics predictor. The authors argue that these additional losses enforce further structure on the latent embedding, improving performance and planning efficiency for real-time model-based control. Results are demonstrated on standard world model benchmarks and two simulated robot manipulation tasks.
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- arXiv Robotics — research abstractsSignal$\Sigma$-LeWM: Learning World Models via S-Information for Robot Control
This paper introduces $\Sigma$-LeWM, an extension of the LeWorldModel architecture that incorporates backward dynamics and inverse dynamics action predictors alongside a latent forward dynamics predictor. The authors argue that these additional losses enforce further structure on the latent embedding, improving performance and planning efficiency for real-time model-based control. Results are demonstrated on standard world model benchmarks and two simulated robot manipulation tasks.
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