Homeostasis Learning
In general organisms will try to maintain homeostastis responding to a disturbance with a compensatory reaction (often a reflex). The pain on touching a hot surface is usually followed by a withdrawal reflex. Due to the fact that all more advanced creatures possess far-sensors, which react to distance signals (like heat-radiation), animals can learn the correlation between an earlier occurring far-sensor signal and the corresponding – later occurring – near-sensor event. In our example one would like to learn the conjunction that heat-radiation predicts pain, which is a causal coupled temporal sequence of sensor events. Hence such a mechanism serves to learn predicting the environment of the animal/agent in a better way (predictive learning) and as a result the animal/agent will have learned anticipatory disturbance compensation.
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Currently we are using these algorithms
for two sub-projects
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1) To learn load compensation at a multi-joint robot arm, where the movement of one joint will lead to a predictable (hence learnable) torque change at the other joints. Here it is of importance to learn the “right degree” of compensation, which corresponds to a learned forward model of the system. To this end we are employing recruitment mechanisms similar to those used at real muscles. |
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2) Furthermore we are using the same learning algorithms to improve the dynamic stability of our fast biped walking robot “RunBot”, which is currently the fastest biped walker existing, attaining a relative speed of 3.5 leg-length per second (human record walkers arrive at about 4-4.5 leg-length per second) |
Main cooperation partners:
- Bernd Porr, Glasgow (learning)
- Tao Geng, Stirling (RunBot)
- Andre Seyfarth, Zully Ritter, Jena (walking robots and muscle models)
Belongs to Group(s):
Computational Neuroscience
Members working within this Project:
Kolodziejski, Christoph
Selected Publication(s):