Feb 5, 2020 · We explore three different approaches for learning tubes that contain the possible trajectories of the system, and demonstrate how to use each of them in a ...

Jul 12, 2020 · Abstract—Learning-based control aims to construct models of a system to use for planning or trajectory optimization, e.g. in.

We explore three different approaches for learning tubes that contain the possible trajectories of the system, and demonstrate how to use each of them in a ...

A deep quantile regression framework for control is introduced that enforces probabilistic quantile bounds and quantifies epistemic uncertainty.

Jun 4, 2020 · In this work we use deep learning to obtain expressive and flexible models of how distributions of trajectories behave, which we then use for ...

MPC. Tubes. Three Problems. Deep Learning. Summary. Deep Learning Tubes for Tube MPC. Johan Gronqvist. 2020-11-30. Page 2. Deep Learning. Tubes for Tube.

Deep Learning Tubes for Tube MPC. Contribute to ddfan/deep_learning_tubes development by creating an account on GitHub.

Learning-based control aims to construct models of a system to use for planning or trajectory optimization, e.g. in model-based reinforcement learning.

Tube MPC can be used in the presence of bounded external disturbances to keep the actual state within an invariant tube around the nominal trajectory [19, 44,48] ...

Feb 5, 2020 · First, we introduce a deep quantile regression framework for control which enforces probabilistic quantile bounds and quantifies epistemic ...