Learning Task Agnostic Sufficiently Accurate Models
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For complex real-world systems, designing controllers are a difficult task. With the advent of neural networks as a proxy for complex function approximators, it has become popular to learn the controller directly. However, these controllers are specific to a given task and need to be relearned for a new task. Alternatively, one can learn just the model of the dynamical system and compose it with external controllers. Such a model is task (and controller) agnostic and must generalize well across the state space. This paper proposes learning a "sufficiently accurate" model of the dynamics that explicitly enforces small residual error on pre-defined parts of the state-space. We formulate task agnostic controller design for this learned model as an optimization problem with state and control constraints that is solved in an online fashion. We validate this approach in simulation using a challenging contact-based Ball-Paddle system.
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