Publications (20)
Fast Generation of Feasible Trajectories in Direct Optimal Control
David Kiessling, Katrin Baumgärtner, Jonathan Frey +3
This paper examines the question of finding feasible points to discrete-time optimal control problems. The optimization problem of finding a feasible trajectory is transcribed to a…
Gauss-Newton Runge-Kutta Integration for Efficient Discretization of Optimal Control Problems with Long Horizons and Least-Squares Costs
Jonathan Frey, Katrin Baumgärtner, Moritz Diehl
This work proposes an efficient treatment of continuous-time optimal control problem (OCP) with long horizons and nonlinear least-squares costs. The Gauss-Newton Runge-Kutta (GNRK)…
Differentiable Nonlinear Model Predictive Control
Jonathan Frey, Katrin Baumgärtner, Gianluca Frison +5
The efficient computation of parametric solution sensitivities is a key challenge in the integration of learning-enhanced methods with nonlinear model predictive control (MPC), as…
Imitation Learning from Nonlinear MPC via the Exact Q-Loss and its Gauss-Newton Approximation
Andrea Ghezzi, Jasper Hoffman, Jonathan Frey +2
This work presents a novel loss function for learning nonlinear Model Predictive Control policies via Imitation Learning. Standard approaches to Imitation Learning neglect informat…
MPC4RL -- A Software Package for Reinforcement Learning based on Model Predictive Control
Dirk Reinhardt, Katrin Baumgärnter, Jonathan Frey +2
In this paper, we present an early software integrating Reinforcement Learning (RL) with Model Predictive Control (MPC). Our aim is to make recent theoretical contributions from th…
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control
Alessandro Saviolo, Jonathan Frey, Abhishek Rathod +2
Model-based control requires an accurate model of the system dynamics for precisely and safely controlling the robot in complex and dynamic environments. Moreover, in the presence…