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 e...
Antonio Pariente, Ignacio Boero, Nikolai Matni et al.· 0 citations
This work proposes a cascaded physics-informed neural network (PINN) framework to approximately solve partial differential equations (PDEs) and demonstrates that this approach can computationally discover effective feedback-linearizing representations of nonlinear systems for control tasks.
Pavlos Kallinikidis, Feng-Jun Yang, David Snyder et al.· 0 citations
Domain Randomization (DR) has been widely used to overcome the sim-to-real gap by training a controller on a distribution of simulated environments via reinforcement learning. While DR can achieve robust performance simply using controllers synthesized via policy gradient (PG) methods, the optimization landscape is not...
Tesshu Fujinami, Bruce D. Lee, Anastasios Tsiamis et al.· 0 citations
We study optimal input design over a finite horizon for linear dynamical systems. The goal is to minimize a weighted inverse-covariance (information) criterion subject to an energy budget. The set of covariances achievable by causal policies is convex but lacks a tractable explicit description, ruling out projection-ba...
Fethi Bencherki, Bruce D. Lee, Nikolai Matni et al.· 0 citations
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