11 citations · 18 across the 10 of their papers we have counts for
10 papers
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control
Andrea Ghezzi, Rudolf Reiter, Katrin Baumgärtner +2
We propose a computationally efficient rollout-then-optimize method to improve a learned control policy at deployment time. A learned policy provides a nominal trajectory, which is…
Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification
Rudolf Reiter, Jasper Hoffmann, Dirk Reinhardt +6
The fields of MPC and RL consider two successful control techniques for Markov decision processes. Both approaches are derived from similar fundamental principles, and both are wid…
AC4MPC: Actor-Critic Reinforcement Learning for Nonlinear Model Predictive Control
Rudolf Reiter, Andrea Ghezzi, Katrin Baumgärtner +3
\Ac{MPC} and \ac{RL} are two powerful control strategies with, arguably, complementary advantages. In this work, we show how actor-critic \ac{RL} techniques can be leveraged to imp…
Equivariant Deep Learning of Mixed-Integer Optimal Control Solutions for Vehicle Decision Making and Motion Planning
Rudolf Reiter, Rien Quirynen, Moritz Diehl +1
Mixed-integer quadratic programs (MIQPs) are a versatile way of formulating vehicle decision making and motion planning problems, where the prediction model is a hybrid dynamical s…
A Long-Short-Term Mixed-Integer Formulation for Highway Lane Change Planning
Rudolf Reiter, Armin Nurkanovic, Daniele Bernadini +2
This work considers the problem of optimal lane changing in a structured multi-agent road environment. A novel motion planning algorithm that can capture long-horizon dependencies…
Progressive Smoothing for Motion Planning in Real-Time NMPC
Rudolf Reiter, Katrin Baumgärtner, Rien Quirynen +1
Nonlinear model predictive control (NMPC) is a popular strategy for solving motion planning problems, including obstacle avoidance constraints, in autonomous driving applications.…
Frenet-Cartesian Model Representations for Automotive Obstacle Avoidance within Nonlinear MPC
Rudolf Reiter, Armin Nurkanović, Jonathan Frey +1
In recent years, nonlinear model predictive control (NMPC) has been extensively used for solving automotive motion control and planning tasks. In order to formulate the NMPC proble…
A Hierarchical Approach for Strategic Motion Planning in Autonomous Racing
Rudolf Reiter, Jasper Hoffmann, Joschka Boedecker +1
We present an approach for safe trajectory planning, where a strategic task related to autonomous racing is learned sample-efficient within a simulation environment. A high-level p…
Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots
Shamil Mamedov, Rudolf Reiter, Seyed Mahdi Basiri Azad +4
Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher payload-to-mass ratio. Ho…
An Inverse Optimal Control Approach for Trajectory Prediction of Autonomous Race Cars
Rudolf Reiter, Florian Messerer, Markus Schratter +2
This paper proposes an optimization-based approach to predict trajectories of autonomous race cars. We assume that the observed trajectory is the result of an optimization problem…