Category: Robotics

Successful NRP User Workshop

Date: 24.07.2017
Venue: FZI, Karlsruhe, Germany

Thanks to all of the 17 participants for making this workshop a great time.

Last week, we held a successful Neurorobotics Platform (NRP) User Workshop in FZI, Karlsruhe.  We welcomed 17 attendants over three days, coming from various sub-projects (such as Martin Pearson, SP3) and HBP outsiders (Carmen Peláez-Moreno and  Francisco José Valverde Albacete). We focused on hands-on sessions so that users got comfortable using the NRP themselves.


Thanks to our live boot image with the NRP pre-installed, even users who did not follow the local installation steps beforehand could run the platform locally in no time. During the first day, we provided a tutorial experiment, exclusively developed for the event, which walked the users through the many features of the NRP. This tutorial experiment is inspired from the baby playing ping pong video, which is here simulated with an iCub robot. This tutorial experiment will soon get released with the official build of the platform.


On the second and third days, more freedom was given to the users so that they could implement their own experiments. We had short hands-on sessions on the Robot Designer as well as Virtual Coach, for offline optimization and analysis. Many new experiments were successfully integrated into the platform: the Miro robot from Consequential Robotics,  a snake-like robot moving with Central Patterns Generators (CPG), revival of the Lauron experiment, …


We received great feedback from the users. We are looking forward for the organization of the next NRP User Workshop!


Morphological Properties of Mass-Spring-Damper Networks for Optimal Locomotion Learning

Morphological Properties of Mass-Spring-Damper Networks for Optimal Locomotion Learning

Robotic Embodiment

The combination of brain inspired AI and robotics is in the core of our work in the Human Brain Project. AI is a vague concept that originated from computer sciences many decades ago and encompasses all algorithms that mimic some cognitive functions of the human species. They are increaslingly based on methods that learn automatically from big datasets.

However, applying those methods to control robots is not as straightforward as it could seem. Unlike computer software, robots generally evolve in noisy and continuously changing environments but on the other hand, their mechanical complexity can be seen as an asset to simplify the control. This is studied through the fields of embodiment and morphological computation. Extreme examples have shown that mechanical structures could provide very natural behavior with no controller at all.

The Passive Walker experiment from T. McGeer is a powerful demonstration emphazing the importance of the body design versus the controller complexity to obtain robust and natural locomotion gaits.

Towards a Formalization of the Concept

Some recent investigations have tried to formalize the relation between the dynamical complexity of a mechanical system and its capability to require simple control. To this goal, a simple yet efficient tool consists in simulating structures composed of masses connected with actuated damper-spring links.

To extend this research, we developed a basic simulator of mass-spring-damper (MSD) networks and optimized a naive locomotion controller to provide them with efficient gaits in term of traveled distance and dissipated power. Three experiments have been done in open-loop to determinate the influence of the size of a structure (estimated though the number of nodes), the compliance (inverse of the spring stiffness) and the saturation at high powers.

This video presents several simulation renditions. The different locomotion processes displayed are learned through optimization in open-loop control.

In the second part of this work, the capacity of realizing closed-loop control in a very simple way requiring very few numerical computations has then been demonstrated.

clThe principal components in the closed-loop learning pipeline consist in a readout layer which is trained at each time step and a signal mixer that gradually integrates the feedback in the actuation signal.

Our Contribution

A full discussion about the results is accessible directly in this article under Creative Common license.

This work has been realized at Ghent Uuniversity together with Jonas Degrave, Francis wyffels, Joni Dambre and Benonie Carette. It is mainly mainly academic and provides a methodology to optimize a controller for locomotion and indications on what we can expect from its complexity to be able to realize this experiment. In the future, this knowledge will be used to conduct similar experiments on quadruped robots both in the real world and in simulation using the Neuro-Robotic Platform (NRP) developed in HBP.