[Submitted on 28 Sep 2026]
Title:Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control
View a PDF of the paper titled Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control, by Antonio Pariente and 2 other authors
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Abstract:World models jointly learn latent representations and dynamics that predict how high-dimensional observations evolve under actions. In this work, we propose a JEPA-style world model in which, rather than learning arbitrary latent dynamics, we restrict them to follow a bilinear parameterization. This structure enables efficient planning and control while shifting the modeling burden onto the encoder, encouraging richer representations that expose the controllable geometry of the system. In particular, this structured parameterization allows us to structurally enforce action recoverability, thereby preventing representation collapse by construction. Although prescribing a bilinear parametrization may appear restrictive, we show that a broad class of nonlinear dynamical systems admits a transformation under which the dynamics become bilinear. Empirically, we show across standard 2D and 3D control tasks that representations with bilinear-parameterized dynamics can be learned directly from high-dimensional observations, reducing planning time by nearly three orders of magnitude while retaining or even improving control accuracy. We also propose more demanding regimes of longer-horizon planning and real-time control, and demonstrate that our method succeeds in both, moving JEPA-style world models beyond short-horizon offline planning.
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2609.36305 [cs.RO]
(or arXiv:2609.36305v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.36305
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ignacio Boero [view email] [v1] Mon, 28 Sep 2026 21:39:06 UTC (6,939 KB)
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