Partners

COUNTRYPARTICIPANT ORGANISATION NAMEPRINCIPAL ROLESHORT NAMEMORE INFO
Eindhoven University of Technology

Alessandro Saccon (Coordinator)

TU/e
Read more
Ecolé Polytechnique Fédérale de Lausanne

Aude Billard (PI)

EPFL
Read more
Technische Universität München
Sami Haddadin (PI)
TUM
Read more
Centre National de la Recherche Scientifique
Abderrahmane Kheddar (PI)
CNRS
Read more
Algoryx Simulation
Claude Lacoursière
AGX
Read more
Franka Emika
José Medina
FE
Read more
Smart Robotics
Heico Sandee
SR
Read more
Vanderlande Industries
Bas Coenen
VDLANDE
Read more

Eindhoven University of Technology
Coordinator

The Eindhoven University of Technology, via the Dynamics and Control section of the Department of Mechanical Engineering, brings in to the consortium its expertise in nonsmooth dynamics and nonlinear control. Alessandro Saccon has a longstanding experience in control theory, multibody dynamics, and numerical optimization.

Alessandro Saccon

affiliation: TU/e
Coordinator

Nathan van de Wouw

affiliation: TU/e
Nonsmooth Mechanics and Nonlinear Control

Jos den Ouden

affiliation: TU/e
Project Manager

Henk Nijmeijer

affiliation:TU/e
Nonlinear Dynamics and Control

Jari van Steen

affiliation: TU/e
researcher (PhD student)

Maarten Jongeneel

affiliation: TU/e
researcher (PhD student)

Ecolé Polytechnique Fédérale de Lausanne 

The Ecole Polytechnique Fédérale de Lausanne (EPFL) brings a long-standing expertise at developing robust and adaptive control architectures to realize skilful robot motions. The Learning Algorithms and Systems Laboratory (LASA) of EPFL is world premier laboratory on research on programming by demonstration. Research at LASA combines engineering, computer science and computational neuroscience methods for the development of learning control system to enable flexible human-robot interactions.

Aude Billard

affiliation: EPFL
PI

Michael Bombile

affiliation: EPFL
Researcher

Technische Universität München

 

The Technical University of Munich, via the Chair of Robotics and System Intelligence (RSI), part of Munich School of Robotics and Machine Intelligence (MSRM), provides the consortium with expertise and experience in automatic control, robotics, machine learning and specifically collision handling and safe physical human-robot interaction. The goal of RSI is to significantly advance the scientific foundations for intelligent machines capable of acting autonomously in our world and in close interaction with their human creators.

Sami Haddadin

affiliation: TUM
PI

Saeed Abdolshah

affiliation: TUM
Senior Researcher

Alexander Kurdas

affiliation: TUM
Researcher

Centre National
de la Recherche Scientifique

Founded in 1939, the Centre National de la Recherche Scientifique (CNRS) is a government-funded research organization. CNRS research units are spread throughout France, and employ a large body of permanent researchers, engineers, technicians, and administrative staff. As the largest fundamental research organization in Europe, CNRS is involved in all scientific fields. The Montpellier Laboratory of Informatics, Robotics, and Micro-electronics (LIRMM), Interactive Digital Human group research activities focus on multi-sensory and multi-objectives task-space control of complex robotic systems such as humanoid with a focus on physical interaction.

Abderrahmane Kheddar

affiliation: CNRS
PI

Niels Dehio

affiliation: CNRS
PostDoc

Yuquan Wang

affiliation: CNRS
PostDoc

Pierre Gergondet

affiliation: CNRS, in leave
Research Engineer (external collaborator)

Algoryx Simulation

Algoryx Simulation is a leading provider of software and services for visual and interactive physics based simulation. Algoryx brings in to the consortium its expertise and technology to simulate a large variety of complex systems and machines that includes nonsmooth phenomena such as dry friction, mechanical play, and hard impacts. Interfaces to popular libraries make it easy to develop autonomous systems quickly and reliably and Algoryx technology AGX Dynamics support several integrated platforms and tools, for example Unity, Unreal Engine, web/cloud environments and many of the market leading AI-tools, such as Tensorflow, pyTorch, ML Agents, MATLAB and more.

Claude Lacoursière

affiliation: AGX
Co-founder and Chief Scientist

Franka Emika GmbH 

Franka Emika (FE), a deep‐tech company from Munich, Germany, redefined robotics with the world’s most advanced robotic system Panda Powertool. In pursuit of high‐performance and accessibility, FE combined human‐centered design with trustworthy German engineering, bringing exceptional soft‐robot performance to everyone. Through I.AM., FE will explore the limits of its robots during impact situations, in order to bring highly dynamic manipulation to real applications.

José Medina

affiliation: FE
PI

Andreas Spenninger

affilitation:FE
Engineer

Smart Robotics BV

Smart Robotics develops and markets robot systems that are flexibly deployable and easy to configure. Through I.AM they plan to extend their software platform with dynamic pick and place algorithms, combined with its 3D-vision feedback. Smart Robotics will integrate the projects results in a number of demonstrators, to test the increase in performance of its palletizing and item picking applications.

Heico Sandee

affiliation: SR
Managing Director

Janno Lunenburg

affiliation: SR
R&D Lead

Simon Jansen

affiliation: SR
Robotics Engineer

Vanderlande Industries B.V.

Vanderlande is the global market leader for value-added logistic process automation at airports, and in the parcel market. The company is also a leading supplier of process automation solutions for warehouses. Vanderlande brings in the consortium the in-depth domain knownledge of these markets and is actively involved in the dissemination and exploitation of I.AM. results, specifically to the industry, business and end-users.

Bas Coenen

affiliation: VDLANDE
PI

Jalte Norder

affiliation: VDLANDE
Product Manager Robotics

I.AM. has received funding from the European Union's Horizon 2020 Research and Innovation Programme (call: H2020-ICT-09-2019-2020, RIA) under Grant Agreement No. 871899