Click to edit Master title style • Click to edit Master text styles – Second level • Third level – Fourth level G. Hinkel, H. Groenda, L. Vannucci, O. Denninger, N. Cauli, S. Ulbrich L’Aquila, 21-JUL-2015 A Domain-Specific Language (DSL) for Integrating Neuronal Networks in Robot Control
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G. Hinkel, H. Groenda, L. Vannucci, O. Denninger, N. Cauli, S. Ulbrich
L’Aquila, 21-JUL-2015
A Domain-Specific Language (DSL) for Integrating Neuronal Networks
in Robot Control
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Motivation Platform Python DSL Formal Model Transformation Conclusion
Bio-inspired robots
• Robots inspired by nature
• Desire to create biologically plausible robot controllers
21.07.2015 Georg Hinkel et al. - A Domain-specific Language for Integrating Neuronal Networks in Robot Control 2
[Roe14]
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Motivation Platform Python DSL Formal Model Transformation Conclusion
Connecting Robots to Neuronal Networks
21.07.2015 Georg Hinkel et al. - A Domain-specific Language for Integrating Neuronal Networks in Robot Control 3
?
[Image: extremetech.com]
[Image: FZI]
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Motivation Platform Python DSL Formal Model Transformation Conclusion
The Human Brain Project
• Unify understanding of the (human) brain – Provide ICT platforms
• Transfer this knowledge into products – Future Neuroscience
– Future Medicine
– Future Computing
• Interdisciplinary cooperation
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Motivation Platform Python DSL Formal Model Transformation Conclusion
The Neuro-Robotics Platform (NRP)
Closed Loop
Engine
Transfer Functions
World Simulator (Gazebo)
Transfer Functions
Neuronal Simulator
(Nest)
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Camera images, Sensors
Control messages Membrane potentials
Network stimuli
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Motivation Platform Python DSL Formal Model Transformation Conclusion
The NRP in action
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Motivation Platform Python DSL Formal Model Transformation Conclusion
A basic example experiment
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[Brai86] [Image: Clearpath robotics]
EyeSensorTransmit
WheelSensorTransmit
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Motivation Platform Python DSL Formal Model Transformation Conclusion
Components of a running simulation
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Motivation Platform Python DSL Formal Model Transformation Conclusion
Designing a DSL
• General-purpose language inappropriate [Fow10]
• Developers do not voluntarily change their primary language [MR13]
• Python popular among neuroscientists & roboticists – Goal to make DSL familiar to Python developers
• No limit for expressiveness in the Transfer Functions – Internal Python DSL
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Motivation Platform Python DSL Formal Model Transformation Conclusion
Motivation Platform Python DSL Formal Model Transformation Conclusion
Conclusion
• Internal DSL in Python for Transfer Functions – Raises abstraction level of the platform
– Familiar for Python users
• Formal model for Integrating neuronal networks with robot control – Advantages in model validation
– Enabling artifact for graphical editor
• Support of both abstraction levels – Transformation approach
– Allow expert users to use Python DSL
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References
[Roe14] A. Roennau, G. Heppner, M. Nowicki, and R. Dillmann, „LAURON V: a versatile six-legged walking robot with advanced maneuverability,” in Advanced Intelligent Mechatronics (AIM), 2014 IEEE/ASME International Conference on, IEEE, 2014, pp. 82-87.
[Völ06] M. Völter, T. Stahl, J. Bettin,A. Haase, S. Helsen, and K. Czarnecki, Model-Driven Software Development: Technology. 2006
[Brai86] V. Braitenberg, Vehicles: Experiments in synthetic psychology. MIT press, 1986
[MR13] L. A. Meyerovich and A. S. Rabkin, “Empirical analysis of programming language adoption” in Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages & Applications, ACM, 2013, pp. 1-18
[Fow10] M. Fowler, Domain-specic languages. Pearson Education, 2010.