Vincent Berenz

Autonomous Motion

IT Tübingen

Office:
Max-Planck-Ring 4
Tübingen
Germany

07071 601 1734

http://vincentberenz.is.tuebinge...

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Overview
Publications

Vincent Berenz is scientific software engineer in the Autonomous Motion Department at the Max Planck Institute for Intelligent Systems (Tübingen, Germany) since January 2015. He is in charge of overseeing the development of software related to robotic architecture and research projects.

Vincent received a Chemical Engineering degree from Ecole Superieure de Chimie Organique et Minerale (2001, France) and a Master of Business Engineering Bio-informatics from Ecole de Biologie Industrielle (2002, France). He worked as a software engineer at CEREP (USA and France) and Pharmadesign (Japan) from 2002 to 2006 and from 2006 to 2007, respectively. In 2012, he received his Ph.D. in Intelligent Interactions Technologies from the University of Tsukuba (Japan) under the supervision of Kenji Suzuki. In 2013 and 2014 he was a research scientist at RIKEN Brain Science Institute/Toyota Collaborative Center (Japan) and he remains a guest researcher of the RIKEN Intelligent Behavior Control Unit.

25 results (View BibTeX file of all listed publications)

2020

Learning Sensory-Motor Associations from Demonstration

Berenz, V., Bjelic, A., Herath, L., Mainprice, J.

29th IEEE International Conference on Robot and Human Interactive Communication (Ro-Man 2020), August 2020 (conference) Accepted

Abstract

We propose a method which generates reactive robot behavior learned from human demonstration. In order to do so, we use the Playful programming language which is based on the reactive programming paradigm. This allows us to represent the learned behavior as a set of associations between sensor and motor primitives in a human readable script. Distinguishing between sensor and motor primitives introduces a supplementary level of granularity and more importantly enforces feedback, increasing adaptability and robustness. As the experimental section shows, useful behaviors may be learned from a single demonstration covering a very limited portion of the task space.

[BibTex]

2020

am Berenz, V., Bjelic, A., Herath, L., Mainprice, J. Learning Sensory-Motor Associations from Demonstration 29th IEEE International Conference on Robot and Human Interactive Communication (Ro-Man 2020), August 2020 (conference) Accepted

[BibTex]

An Open Force-Controlled Modular Robot Architecture for Legged Locomotion Research

Felix Grimminger, , Avadesh Meduri, , Majid Khadiv, , Julian Viereck, , Manuel Wüthrich, , Maximilien Naveau, , Vincent Berenz, , Steve Heim, , Felix Widmaier, , Thomas Flayols, , Jonathan Fiene, , Alexander Badri-Spröwitz, , Righetti, L.

The IEEE Robotics and Automation Letters, 5(2):3650 - 3657, IEEE, April 2020 (article)

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Youtube Open Dynamic Robot Initiative link (url) DOI [BibTex]

am dlg ei mg sf sg Felix Grimminger, , Avadesh Meduri, , Majid Khadiv, , Julian Viereck, , Manuel Wüthrich, , Maximilien Naveau, , Vincent Berenz, , Steve Heim, , Felix Widmaier, , Thomas Flayols, , Jonathan Fiene, , Alexander Badri-Spröwitz, , Righetti, L. An Open Force-Controlled Modular Robot Architecture for Legged Locomotion Research The IEEE Robotics and Automation Letters, 5(2):3650 - 3657, IEEE, April 2020 (article)

Youtube Open Dynamic Robot Initiative link (url) DOI [BibTex]

Learning to Play Table Tennis From Scratch using Muscular Robots

Büchler, D., Guist, S., Calandra, R., Berenz, V., Schölkopf, B., Peters, J.

2020 (article) Submitted

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arXiv Project Page Video [BibTex]

ei sg am Büchler, D., Guist, S., Calandra, R., Berenz, V., Schölkopf, B., Peters, J. Learning to Play Table Tennis From Scratch using Muscular Robots 2020 (article) Submitted

arXiv Project Page Video [BibTex]

Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Nubert, J., Koehler, J., Berenz, V., Allgower, F., Trimpe, S.

IEEE Robotics and Automation Letters, 2020 (article) Accepted

Abstract

Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.

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arXiv PDF DOI [BibTex]

am ics Nubert, J., Koehler, J., Berenz, V., Allgower, F., Trimpe, S. Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control IEEE Robotics and Automation Letters, 2020 (article) Accepted

arXiv PDF DOI [BibTex]

2018

A Value-Driven Eldercare Robot: Virtual and Physical Instantiations of a Case-Supported Principle-Based Behavior Paradigm

Anderson, M., Anderson, S., Berenz, V.

Proceedings of the IEEE, pages: 1,15, October 2018 (article)

Abstract

In this paper, a case-supported principle-based behavior paradigm is proposed to help ensure ethical behavior of autonomous machines. We argue that ethically significant behavior of autonomous systems should be guided by explicit ethical principles determined through a consensus of ethicists. Such a consensus is likely to emerge in many areas in which autonomous systems are apt to be deployed and for the actions they are liable to undertake. We believe that this is the case since we are more likely to agree on how machines ought to treat us than on how human beings ought to treat one another. Given such a consensus, particular cases of ethical dilemmas where ethicists agree on the ethically relevant features and the right course of action can be used to help discover principles that balance these features when they are in conflict. Such principles not only help ensure ethical behavior of complex and dynamic systems but also can serve as a basis for justification of this behavior. The requirements, methods, implementation, and evaluation components of the paradigm are detailed as well as its instantiation in both a simulated and real robot functioning in the domain of eldercare.

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