Prof. Claudius Gros |
Robots are normally hard controlled with the aim
to perform predefined movements. The same holds
for genetically encoded locomotion. Alternatively
one may consider motion to be generated by self-organizing
principles within the sensory-motor loop. In this case
the only sensory information available is the state of
the actuator, such as the angle of a limb or a wheel.
We use simulated and small real-world robots to study how locomotion
can be generated through the `donkey-and-carrot' principle, which
state that the target state for the actuator is updated
as soon as the state of the actuator changes. One finds
that complex and reactive motion patterns may emerge,
with the actual motion pattern realized being dependent
on the initial conditions.
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Computational Neurosciences, AI and Machine Learning
We develop theories and working principles for neural
models and synaptic learning. Our interest involve
both autonomous systems with self-sustained neural
activity, self-organized locomotion and the impact of
Cognitive systems theory is located at the crossroad between
computational neurosciences and machine learning, as it
regards both biological and artificial intelligences (AI).
One of the long-term goals is to formulate generating principles
allowing to construct modular information processing cognitive
systems which are, as far as possible, self-organizing.