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About this paper
Antonio Pariente, Ignacio Boero, Nikolai Matni, Alejandro Ribeiro
The authors introduce a structured latent world model for image-observed simulated control. Its state-dependent action map supports action recovery, while endpoint sensitivities guide Gauss–Newton planning; normalization means its dynamics are not strictly bilinear. Across four tasks, it reports 75% to 100% success and 0.7–3.1 seconds planning, versus 7–32 minutes for baselines; comparisons also change planners.
Summary by alphaXiv.
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