The Neurorobotics Platform (NRP) is a software and hardware infrastructure through which scientists and technology developers can connect validated brain models to simulations of robot bodies and environments.

We released the first version of our Neurorobotics Platform (NRP) to the scientific community in April this year. It includes a Robot Designer, an Environment Builder and a Closed-Loop Engine, as well as the NRP host facility. It provides internet accessible tools to design and customise models of robots, experiments and environments. It also enables researchers to collaboratively design and run virtual experiments in cognitive neuroscience using brain models developed within and outside the HBP.

The Platform targets researchers of multiple fields such as neuroscientists wanting to validate brain models in the context of closed action-perception loops as well as robotics researchers who want to develop new neuro-inspired controllers. The neurorobotics workflow and Platform has been validated in four different pilot experiments: 1) A basic closed-loop integration of a Husky wheeled robot and the LAURON hexapod as simple Braitenberg vehicles; 2) a virtual mouse model with soft body simulation 3) the humanoid robot iCub including a retina model; 4) and the neuromorphic control of a physical biomimetic robotic arm.

For more information please visit the Neurorobotics webpage, or take a look at SP10's YouTube channel.

 

SP Leader: Alois KNOLL

Deputy SP Leader: Marc-Oliver GEWALTIG

Work Package Leaders:

 

Publication highlights:

Hinkel, G., Groenda, H., Vannucci, L., Denninger, O., Cauli, N., Ulbrich, S., Falotico, E., Roennau, A., Gewaltig, M.-O., Knoll, A., Dillmann, R., Laschi, C., & Reussner, R. (2016). A Framework for Coupled Simulations of Robots and Spiking Neuronal Networks. Journal of Intelligent & Robotic Systems: doi: 10.1007/s10846-016-0412-6.

Manassi, M., Hermens, F., Francis, G., & Herzog, M.H. (2015). Release of crowding by pattern completion. Journal of Vision, 15:1-15.

Moraud, E.M., Capogrosso, M., Formento, E., Wenger, N., DiGiovanna, J., Courtine, G., & Micera, S. (2016). Mechanisms Underlying the Neuromodulation of Spinal Circuits for Correcting Gait and Balance Deficits after Spinal Cord Injury. Neuron, 89:814-828.

Richter, C., Jentzsch, S., Hostettler, R., Garrido, J.A., Ros, E. Knoll, A., Röhrbein, F., van der Smagt, P., & Conradt, J. (2016). Musculoskeletal Robots: Scalability in Neural Control. IEEE Robotics and Automation Magazine, 99: doi:10.1109/MRA.2016.2535081.

Vannucci, L., Ambrosano, A., Cauli, N., Albanese, U., Falotico, E., Ulbrich, S., Pfotzer, L., Hinkel, G., Denninger, O., Peppicelli, D., Guyot, L., von Arnim, A., Deser, S., Maier, P., Dillmann, R., Klinker, G., Levi, P., Knoll, A., Gewaltig, M.-O., & Laschi, C. (2015). A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulation. IEEE-RAS 15th International Conference on Humanoid Robots, 2015:1179-1184.

Walter, F., Röhrbein, F., & Knoll, A. (2015). Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks. Neural Networks, 72:152-167.

Walter, F. Röhrbein, F., & Knoll, A. (2016). Computation by time. Neural Processing Letters, 44:103-124.