双线性世界模型：学习具有结构化动态的表示以实现高效控制
Bilinear World Models: Learning Representations with Structured Dynamics for Efficient Control
- University of Pennsylvania（宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种JEPA风格的双线性世界模型，通过结构化动态参数化实现高效规划与控制，显著减少规划时间并保持精度，适用于长视界和实时控制任务。
AI中文摘要：
世界模型联合学习潜在表示和动态，以预测高维观测在动作下的演变。在这项工作中，我们提出了一种JEPA风格的世界模型，其中我们不学习任意的潜在动态，而是将其限制为遵循双线性参数化。这种结构使得高效规划和控制成为可能，同时将建模负担转移到编码器上，鼓励更丰富的表示，从而暴露系统的可控几何结构。特别是，这种结构化参数化使我们能够从结构上强制动作可恢复性，从而通过构造防止表示崩溃。尽管规定双线性参数化可能看起来具有限制性，但我们表明，一大类非线性动态系统允许一种变换，在该变换下动态变为双线性。实证上，我们在标准的2D和3D控制任务中表明，具有双线性参数化动态的表示可以直接从高维观测中学习，将规划时间减少近三个数量级，同时保持甚至提高控制精度。我们还提出了更长视界规划和实时控制的更具挑战性的机制，并证明我们的方法在两者中都成功，将JEPA风格的世界模型扩展到短视界离线规划之外。
英文摘要：
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.
