Vincent Berenz

07071 601 1734

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

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.

22 results (View BibTeX file of all listed publications)

2019

Automated Generation of Reactive Programs from Human Demonstration for Orchestration of Robot Behaviors

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

ArXiv, 2019 (article)

Abstract

Social robots or collaborative robots that have to interact with people in a reactive way are difficult to program. This difficulty stems from the different skills required by the programmer: to provide an engaging user experience the behavior must include a sense of aesthetics while robustly operating in a continuously changing environment. The Playful framework allows composing such dynamic behaviors using a basic set of action and perception primitives. Within this framework, a behavior is encoded as a list of declarative statements corresponding to high-level sensory-motor couplings. To facilitate non-expert users to program such behaviors, we propose a Learning from Demonstration (LfD) technique that maps motion capture of humans directly to a Playful script. The approach proceeds by identifying the sensory-motor couplings that are active at each step using the Viterbi path in a Hidden Markov Model (HMM). Given these activation patterns, binary classifiers called evaluations are trained to associate activations to sensory data. Modularity is increased by clustering the sensory-motor couplings, leading to a hierarchical tree structure. The novelty of the proposed approach is that the learned behavior is encoded not in terms of trajectories in a task space, but as couplings between sensory information and high-level motor actions. This provides advantages in terms of behavioral generalization and reactivity displayed by the robot.

Support Video link (url) [BibTex]

2019

am Berenz, V., Bjelic, A., Mainprice, J. Automated Generation of Reactive Programs from Human Demonstration for Orchestration of Robot Behaviors ArXiv, 2019 (article)

Support Video link (url) [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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