Manuel Kudruss — accepted AI-conference papers

2018

Model-Plant Mismatch Compensation Using Reinforcement Learning

Ivan Koryakovskiy, Manuel Kudruss, Heike Vallery, Robert Babuska, Wouter Caarls

RA-L 2018

Learning-based approaches are suitable for the control of systems with unknown dynamics. However, learning from scratch involves many trials with exploratory actions until a good control policy is discovered. Real robots usually cannot withstand the exploratory actions and suffer damage. This proble

Cited by 41 Source ↗Scholar ↗

2017

A Reactive Walking Pattern Generator Based on Nonlinear Model Predictive Control

Maximilien Naveau, Manuel Kudruss, Olivier Stasse, Christian Kirches, Katja D. Mombaur, Philippe Souères

RA-L 2017

The contribution of this work is to show that real-time nonlinear model predictive control (NMPC) can be implemented on position controlled humanoid robots. Following the idea of “walking without thinking,” we propose a walking pattern generator that takes into account simultaneously the position an

Cited by 106 Source ↗Scholar ↗