Neuronal Feedback Guiding Motor Command Execution
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1
Georg-August-Universität Göttingen, Third Institute of Physics - Biophysics, Germany
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2
Bernstein Focus Neurotechnology, Germany
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3
Bernstein Center for Computational Neuroscience, Germany
Feedback loops allow controllers to work in an effective manner, especially in dynamic environments, where the systems under control may be influenced in one way or the other. Usually the behavioral control unit changes the motor command according to the available sensory information. Here, a neuronal closed loop system is presented which acts completely on the local motor control level and can inject sensory feedback, thereby generating sensitive actuators which are characterized by a reduction of unnecessary motor stress.
The proposed system is based on homeokinetic control [Der, 2001], a paradigm for self-organized control of autonomous robots. Due to the homeokinetic principle motor command generation is based on proprioceptive sensory feedback. The system's input, called reference signal here, is the desired motor primitive generated by the behavioral control unit. Its output, the motor command, directly drives the actuator. Hence the system is placed between controller and actuator. If the actuator can follow the reference signal, e.g. a motor can achieve the desired angle, the system will not intervene. However, if the actuator cannot follow the reference signal, e.g. a motor is blocked and can not achieve the desired angle, the system will downsize the motor command. As a consequence, in abnormal conditions motor commands are decreased if they cannot be properly realized. This way the closed loop circuit guides motor command execution.
The proposed system is applied to a physically realistic simulated legged robot where we use an oscillation generated by a central pattern generator (CPG) as a reference signal. In normal conditions the desired walking behavior can be realized following the original CPG signal. When colliding with an obstacle and motors get blocked the corresponding motor command is drastically reduced due to the proposed neuronal closed loop system and thereby unnecessary motor stress is decreased. Note, the reference signal generated by the behavioral control unit does not change. If the robot passed the obstacle and the motors can move freely again, the reference signal is executed as before the collision.
This result exemplifies, how a small neuronal circuit can inject sensory feedback into local motor control and thereby generate sensitive actuators. Furthermore the presented approach for blockade detection in legged robot locomotion relies only on one sensor quality, while usually more information, e.g. torque, joint angle velocity, foot contact and/or distance sensor information, is required [von Twickel et al., 2011, Cruse et al., 2007].
Acknowledgements
This research was supported by the BMBF-funded BFNT Göttingen with grant numbers 01GQ0811 (project 3B) and BCCN Göttingen with grant number 01GQ1005A (project D1) and the Emmy Noether Program (DFG, MA4464/3-1).
References
Der, R. 2001. Self-organized acquisition of situated behaviors. Theory in Biosciences 120, no. 3-4: 179-187.
von Twickel A, Büschges A, Pasemann F. 2011. Deriving neural network controllers from neuro-biological data: implementation of a single-leg stick insect controller. Biol Cybern. 104(1-2): 95-119.
Cruse, H., Dürr, V., & Schmitz, J. 2007. Insect walking is based on a decentralized architecture revealing a simple and robust controller. Phil trans., Mathematical, physical, and engineering sciences, 365(1850), 221-50. doi: 10.1098/rsta.2006.1913.
Keywords:
adaptive systems,
feedback control,
motor control,
neurorobotics,
self-organization
Conference:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.
Presentation Type:
Poster
Topic:
motor control (please use "motor control" as keyword)
Citation:
Hesse
F,
Manoonpong
P and
Wörgötter
F
(2011). Neuronal Feedback Guiding Motor Command Execution
.
Front. Comput. Neurosci.
Conference Abstract:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011.
doi: 10.3389/conf.fncom.2011.53.00102
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Received:
23 Aug 2011;
Published Online:
04 Oct 2011.
*
Correspondence:
Dr. Frank Hesse, Georg-August-Universität Göttingen, Third Institute of Physics - Biophysics, Göttingen, Germany, fhesse@physik3.gwdg.de