Reinforcement learning has emerged as a promising methodology for training robot
controllers. However, most results have been limited to simulation due to the need for a …

This paper proposes a learning-based model predictive control scheme. This scheme
divides the predictive model into a known nominal model and an unknown model residual …

The complex dynamics characterizing deformable terrain presents significant impediments
toward the real-world viability of locomotive robotics, particularly for legged machines. We …

As we aim to control complex systems, use of a simulator in model-based reinforcement
learning is becoming more common. However, it has been challenging to overcome the …

Differential dynamic programming (DDP) is a popular technique for solving nonlinear
optimal control problems with locally quadratic approximations. However, existing DDP …

In this work, we consider the problem of learning a reduced-order model of a high-
dimensional stochastic nonlinear system with control inputs from noisy data. In particular, we …

This paper presents a data-driven method for designing optimal controllers and robust
controllers for unknown nonlinear systems. Mathematical models for the realization of the …

The varied and complex dynamics of deformable terrain are significant impediments toward
real-world viability of locomotive robotics, particularly for legged machines. We explore …

Reactive trajectory optimization for robotics presents formidable challenges, demanding the
rapid generation of purposeful robot motion in complex and swiftly changing dynamic …

This paper proposes embedded Gaussian Process Barrier States (GP-BaS), a methodology
to safely control unmodeled dynamics of nonlinear system using Bayesian learning …