Here we propose a neuromorphic control system for a
medium size humanoid robot under development in the
Robotics and Mechatronics Department at Nazarbayev University
 and in cooperation with Politecnico di Milano

Aubakir, Bauyrzhan

Folgheraiter, Michele

Gini, Giuseppina

Keldibek, Amina

Mauro Franchi, Alessio

Salakchinov, Shyngys

English

Nazarbayev University Repository

A Neuromorphic Motion Controller for a Biped RobotMichele Folgheraiter, Amina Keldibek, Bauyrzhan Aubakir, Shyngys Salakchinov 1,Giuseppina Gini, Alessio Mauro Franchi 2Humans beings, during their life, are exposed to dynamicenvironments that force them to quickly react to externalstimulus, adapt to new situations and learn new sensory-motor control strategies. In an analogous way a humanoidrobot which is supposed to operate in an unstructured envi-ronment is also required to learn and adapt while operatingand performing different tasks.Continuous Time Recurrent Neural Networks (CTRNN)represent a powerful paradigm to control mobile roboticssystems [1]. They can be used to predict sensory inputs, forimage processing, speech recognition and to generate motiontrajectories [2]. Different learning methods can be appliedto tune the CTRNN’s synapses such as back-propagationthrough time [3], real-time recurrent learning [4], Kalman fil-ters, etc. In particular, the reward-modulated Hebbian learn-ing rule [5] represents a more biological plausible methodwhere the adaptation depends on a binary signal encodingan instantaneous reward based on the average performanceof the neural controller.Here we propose a neuromorphic control system for amedium size humanoid robot under development in theRobotics and Mechatronics Department at Nazarbayev Uni-versity (Fig.1) and in cooperation with Politecnico di Mi-lano. The high level layer decides which motion primitivesto generate according to the task. The middle level layercoordinates the low level modules setting the gait frequency,the step size and height. Finally the low level modules, thatmimic the structure of microcircuits located in the primatescortex [6], generate the joints trajectories. More specifically,each module is represented by a CTRNN of 200 neuronsrandomly and sparsely connected with fixed synapses andcapable to learn and reproduce tunable periodic trajectories.Each neuron in the circuit is modeled by a first orderdifferential equation with a sigmoidal activation function.The adaptation takes place in the synapses of readout unitsthat realize a linear combination of the neurons output inorder to reproduce the target signals.1School of Science and Technology, Robotics andMechatronics Department,Nazarbayev University, Kaza-khstan. michele.folgheraiter@nu.edu.kz,akeldibek@nu.edu.kz, b.aubakir@nu.edu.kz,shyngys.salakchinov@nu.edu.kz2 DEIB, Politecnico di Milano, piazza L. da Vinci32, Milano, Italy giuseppina.gini@polimi.it,alessiomauro.franchi@polimi.it*This work is supported by the Ministry of Education and Science ofthe Republic of Kazakhstan under the grant and target funding schemeagreement #220/073-2015.150 155 160 165 170⌃1.5⌃1⌃0.500.511.5Time [s]RNNOutput1150 155 160 165 170⌃1.5⌃1⌃0.500.511.5Time [s]RNNOutput2150 155 160 165 170⌃1.5⌃1⌃0.500.511.5Time [s]RNNOutput3150 155 160 165 170⌃1.5⌃1⌃0.500.511.5Time [s]RNNOutput4150 155 160 165 170⌃1.5⌃1⌃0.500.511.5Time [s]RNNOutput5AdaptableExcitatoryInhibitory345A)B)C)Fig. 1. A) A low level CTRNN module, B) Biped prototype underdevelopment at Nazarbayev University: 10DOFs, 4.5Kg, 0.6m height C)Learned trajectories for the five joints of the biped right leg obtained froma step simulation, the vertical blue dashed line indicates the learning time.REFERENCES[1] R. D. Beer, H. J. Chiel, R. D. Quinn, K. S. Espenschied, and P. Larsson,“A distributed neural network architecture for hexapod robot locomo-tion,” Neural Computation, vol. 4, no. 3, pp. 356–365, May 1992.[2] P. Joshi and W. Maass, “Movement generation with circuits of spikingneurons,” Neural Computation, vol. 17, no. 8, pp. 1715–1738, 2005.[3] P. Werbos, “Backpropagation through time: what it does and how todo it,” Proceedings of the IEEE, vol. 78, no. 10, pp. 1550–1560, Oct1990.[4] R. J. Williams and D. Zipser, “Experimental Analysis of the Real-timeRecurrent Learning Algorithm,” Connection Science, vol. 1, no. 1, pp.87–111, 1989.[5] G. M. Hoerzer, R. Legenstein, and W. Maass, “Emergence of complexcomputational structures from chaotic neural networks through reward-modulated hebbian learning,” Cerebral Cortex, vol. 24, no. 3, pp. 677–690, 2012.[6] J. Wessberg, C. R. Stambaugh, J. D. Kralik, P. D. Beck, M. Laubach,J. K. Chapin, J. Kim, S. J. Biggs, M. A. Srinivasan, and M. A. L.Nicolelis, “Real-time prediction of hand trajectory by ensembles ofcortical neurons in primates,” Nature, vol. 408, no. 6810, pp. 361–365,11 2000.

A Neuromorphic Motion Controller for a Biped Robot

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A Neuromorphic Motion Controller for a Biped Robot

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