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Learning Implicit Priors for Motion Optimization
In this paper, we focus on the problem of integrating Energy-based Models (EBM) as guiding priors for motion optimization.
Stein Variational Model Predictive Control
By casting MPC as a Bayesian inference problem, we employ variational methods for posterior computation, naturally encoding the complexity and multi-modality of the decision making problem.
Deep Forward and Inverse Perceptual Models for Tracking and Prediction
We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics.
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