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[Ultrastable Neuroendocrine Robot Controller]

Neural networks Java AI A-Life Date: Aug 2011

[Masters dissertation]

Attributes of ultrastability as an adaption mechanism in a hormone-modulated neural robot controller were investigated in a simulation. Action-selection based on hormone-driven utilities of memory items was used to alternate between mineral gathering and recharging.

Results showed that the implemented ultrastability mechanism could be applied to both fast-operating neurons and slowly operating glands, although its main limitation was that the adaptation was unstable when a task required an unexpectedly varied satisfaction time.

Artificial neural network, ultrastability, homeostasis, robot controller

Pitonakova, L. (2013). Ultrastable neuroendocrine robot controller. Adaptive Behaviour, 21(1), 47-63.

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Ultrastable Neuroendocrine Robot is born

I have finally put a beginning of an artificial brain to my Ultrastable Neuroendocrine Robot - UNER... my lego robot can now measure inputs from its 3 front proximity sensors and individual cells in its brain get activated when it sees something

Adaptive Robot Controller

A robot neural controller where obstacle-avoidance and random-walk neurons observe level of their irritation and randomly reconfigure their connections to motors when the irritation reaches a specified threshold is presented and tested.


The main purpose of pyCreeper is to wrap tens of lines of python code, required to produce graphs that look good for a publication, into functions. It takes away your need to understand various quirks of matplotlib and gives you back ready-to-use and well-documented code.

Designing Robot Swarms

This project looks at the challenges involved in modeling, understanding and designing of multi-robot systems.

Robustness in Foraging E-puck Swarms Through Recruitment

Swarms of five e-puck robots are used in a semi-virtual environment, facilitated by the VICON positioning system. Recruitment can make swarms more robust to noise in robot global positioning data.

Information Flow Regulation in Preferentially Foraging Swarms

Swarms are tasked with foraging from multiple sources in dynamic environments where source locations change periodically. Preferential foraging leads to a better swarm performance, provided that information flow among robots, that results from recruitment, is regulated