Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …

High-precision trajectory tracking is fundamental in robotic manipulation. While industrial
robots address this through stiffness and high-performance hardware, compliant and cost …

The learning-based model predictive control (LB-MPC) is an effective and critical method to
solve the path tracking problem in mobile platforms under uncertain disturbances. It is well …

Bayesian optimization (BO) is proposed for automatic learning of optimal controller
parameters from experimental data. A probabilistic description (a Gaussian process) is used …

State-space models (SSMs) are a highly expressive model class for learning patterns in time
series data and for system identification. Deterministic versions of SSMs (eg, LSTMs) proved …

Despite the availability of ever more data enabled through modern sensor and computer
technology, it still remains an open problem to learn dynamical systems in a sample-efficient …

A robust model predictive control (RMPC) approach for linear systems with bounded state-
dependent uncertainties is proposed. Such uncertainties can arise from unmodeled non …

We study the optimal control of multiple-input and multiple-output dynamical systems via the
design of neural network-based controllers with stability and output tracking guarantees …

Accurately predicting the dynamics of robotic systems is crucial for model-based control and
reinforcement learning. The most common way to estimate dynamics is by fitting a one-step …

We consider the problem of sequential estimation of the unknowns of state-space and deep
state-space models that include estimation of functions and latent processes of the models …