Category: Robotics

CDP4 at the HBP Summit: integrating deep models for visual saliency in the NRP

Back in the beginning of 2017, we had a great NRP Hackathon @FZI in Karlsruhe, where Alexander Kroner (SP4) presented his deep learning model for computing visual saliency.

We now presented this integration at the Human Brain Summit 2017 in Glasgow as a collaboration in CDP4 – visuo-motor integration. During this presentation we also shown how to integrate any deep learning models in the Neurorobotics Platform, as was already presented in the Young Researcher Event by Kenny Sharma.

We will continue this collaboration with SP4 by connecting the saliency model to eye movements and memory modules.



Optimising compliant robot locomotion using the HBP Neurorobotics platform

If we want robots to become a part of our everyday life, future robot platforms will have to be safe and much cheaper than most useful robots are now. Safety can be obtained by making robots compliant using passive elements (springs, soft elastic materials). Unfortunately, accurate mechanical (dynamic/kinematic) models of such robots are not available and in addition, especially when cheaper materials are used, their dynamical properties drift over time because of wear.

Therefore, cheap robots with passive compliance need adaptive control that is as robust as possible to mechanical and morphological variations. Adaptation training on each physical robot will still be necessary, but this should converge as quickly as possible.

The Tigrillo quadruped robot will be used to investigate neural closed loop motor control for locomotion to address these issues. In particular, we want to investigate how the NRP simulation framework can be used to develop such robust neural control.

As a first step, we implemented a parameterised Tigrillo simulation model generator. Using a simple script, a Gazebo simulation model with given body dimensions, mass distributions and spring constants can be generated to be simulated in the NRP. We then implemented evolutionary optimisation (CMA-ES) in the NRP’s Virtual coach to find efficient motor control patterns, which then generated with spiking population networks using a reservoir computing approach. Finally, these control patterns were transferred to the physical robot’s SpiNNaker board and the resulting gaits were compared to the simulation results.

These steps are illustrated in the video below.

Next steps are:

  • to tune the parameter ranges of  the Tigrillo generator to those that are realistic for the real robot;
  • to implement sensors on the physical robot and calibrate equivalent simulated sensors;
  • to use our setup to obtain the desired robust closed loop control and validate both qualitatively and quantitatively on the physical robot.

Many thanks to Gabriel Urbain, Alexander Vandesompele, Brecht Willems and prof. Francis wyffels for their input.


OpenSim support in the Neurorobotics platform

A key area of research of the Neurorobotics Platform (NRP) is the in-silico study of sensormotor skills and locomotion of biological systems. To simulate the physical environment and system embodiments, the NRP uses the Gazebo robotics simulator.

To perform biologically significant experiments, Gazebo has however been lacking an important feature until now: The ability to model and simulate musco-skeletal kinematics.

Therefore researchers had to rely on ad-hoc implementations calculating effective joint torques for the system at hand, wich is time consuming, error prone and cumbersome.

The physics plugin we implemented provides OpenSim as an additional physics engine alongside the physics engines already supported by Gazebo (ODE, Bullet, SimBody and DART). OpenSim is using SimBody as its underlying framework, thus featuring a stable and accurate mechanical simulation. The OpenSim plugin supports many of SimBody’s kinematic constraint types and implements collision detection support for sphere, plane and triangle mesh shapes along with corresponding contact forces (as exposed by OpenSim’s API).

However, first and foremost it treats physiological models of muscles as first class citizens alongside rigid bodies and kinematic joints. OpenSim is shipped with a number of predefined muscle-tendon actuators. Currently, users of our plugin can use OpenSim’s native XML configuration file format to specify the structure and properties of muscle-tendon systems, which are created on top of Gazebo models specified in Gazebo’s own file format (SDF).

A ROS-based messaging interface provides accessors for excitations and other biophysical parameters allowing to control musco-skeletal systems from external applications such as the Neurorobotics platform.

As demonstration of the capabilities of our physics plugin, we augmented a simple four-legged walker with a set of eight muscles (one synergist-antagonist pair per leg).

The problem we address in this demo is the reinforcement learning task of deriving a controller that excites the muscles in a pattern such that the walker is driven forward. Our setup consists of a Python application (remote-controlling Gazebo via the ROS-based messaging interface for the OpenSim plugin) performing the high-level optimization procedure and running a neural network (NN) controller.

We employ a simple genetic optimization procedure based on Python’s DEAP package to find parameters of the NN that maximize the score the walker obtains in individual trial runs.

The walker is rewarded for moving forward and penalized for unwanted motion behaviour (e. g. ground contacts of the walker’s body, moving off-center).

During a trial run, the physics simulation is stepped in small time increments, and during each iteration the NN is fed with various state variables. The NN’s output is comprised of excitation levels for the muscles. For simplicity we stuck to well-known artificial neural networks, implemented via the Tensorflow package.

We also experimented with fully dynamic grasping simulation using SimBody’s collision detection system and contact force implementations. Although the simulation setup for the grasping tests only comprised a simple two-jaw gripper and a cubic shape (consisting of a triangle mesh shape), the SimBody engine as used in our plugin was able to maintain a stable grasp using fully dynamic contact forces, tackling a problem that is notoriously difficult to solve with other physics engines.

Another application using the OpenSim plugin for Gazebo features a simplified muscle model of a mouse’s foreleg actuated by a neuronal controller modelled according the spinal cord of a real mouse. The details of this experimental setup will be covered in a separate blog post.

The OpenSim plugin does not support all of the features implemented with other engines in Gazebo. For instance, some joint types are not implemented yet. Also, some features unique to OpenSim (like inverse dynamics simulation) are not yet available in the current implementation.

To simplify the design of kinematic models with muscle systems and custom acutator models, it is planned to provide researchers and users of the NRP with a consistent, simple way to specify muscles via a graphical interface using the NRP‘s Robot Designer application.

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, …


Screenshot from 2017-09-08 14-29-33_crop

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


Simulating tendon driven robots

According to the concept of embodiment, a brain needs to be connected to a body interacting with the world for biological learning to happen, developing biomimetic robots is crucial to fully understand human intelligence. Here, a tendon driven approach can model muscle behavior in terms of flexibility, compliance and contractive force.

While this concept is clearly beneficial for research, it is very difficult to accurately model in simulation. In contrast to classical robots with motors applying torques in the joints, the simulation needs to apply forces  along wrapped ropes mimicking tendons and muscles. The artificial muscles developped in the Myorobotics [1] project include mechanical parts for flexiblity and force as well as electrical control in different operating modes as seen in Figure 1. To close the reality gap all physical properties need to be considered in modelling.


Figure 1: Myorobotics muscle unit (from [2])

We implemented a plugin for Gazebo that finally allows us to simulate the Myorobotics muscle setup. The plugin models tendon kinematics as well as mechanical and electrical properties of the technical actuator. The calculated forces derived from control commands can now be applied directly to a robot simulated in Gazebo. This brings it one step closer to being integrated into the NRP, allowing us to equip muscle units to arbitrary robot morphologies.

Ultimately, this will enable us to compare simulated biological muscles simulated by OpenSim to the technical muscle of Myorobotics modelled with this plugin. Eventually, this will help to build better biomimetic muscle units behaving just like their biological counterparts.



[2] C. Richter, S. Jentzsch, R. Hostettler, J. A. Garrido, E. Ros, A. Knoll, F. Röhrbein, P. van der Smagt, and J. Conradt, “Scalability in neural control”, IEEE ROBOTICS &AUTOMATIONMAGAZINE, vol. 1070, no. 9932/16, 2016.



Alexander Kuhn, Benedikt Feldotto (TU München)





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.