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Learning Hybrid Dynamics Models With Simulator-Informed Latent States
In this paper, we propose a new approach to hybrid modeling, where we inform the latent states of a learned model via a black-box simulator.
Exact Inference for Continuous-Time Gaussian Process Dynamics
This is crucial to sample consistent predictions from the dynamics model.
Combining Slow and Fast: Complementary Filtering for Dynamics Learning
This filtering technique combines two signals by applying a high-pass filter to one signal, and low-pass filtering the other.
Structure-preserving Gaussian Process Dynamics
On the other hand, classical numerical integrators are specifically designed to preserve these crucial properties through time.
Wasserstein Adversarial Imitation Learning
Imitation Learning describes the problem of recovering an expert policy from demonstrations.
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