**1.2. Research methods**

has found that in ultimate navigating lifesaving situations, the decisions in the fly's brain, with about 250,000 neurons, are taken by a reduced set of neurons that consume energy and originate an inner noisy output that in turn fires a massive body response (change in flying direction, for instance). So, at this scale, by using its own onboard neural processor, a selfmotivated behavior initiator situation occurs inside the fly, causing noticeable changes in the

As a significant consequence, this internal capacity converts the fly into a free-running autonomous living creature. One of our objectives in the chapter is thus to develop design methods

At the bigger scale of human brains with about eighty billion of neurons, the function of autonomous behaviors initiator is a much more elaborated matter, well documented by Raichel and its research team by using modern functional magnetic resonance imaging (fMRI) [3, 4]. From these studies, one noticeable finding is that the human brain never really rests but stays always in constant activity, burning a substantial amount of energy that seems to go nowhere. Raichel called this phenomenon "the brain dark energy," and his discovery changes every previous concept about brain functioning. This energy-burning attitude seems to be the common way of living brains, and signs of constant burning energy have been reported in

The use of artificial neural nets to control robots represents a promising activity, and recent research has been published. In [7], the authors develop an autonomous robot with the application of neural network and apply it for monitoring and rescue activities in case of natural or man-made disaster. In [8], the use of an artificial neural network to improve the estimation of the position of a mobile node in indoor environment using wireless networks is studied. In [9], the author focuses on deep convolutional neural networks, capable of differentiating between thousands of objects by self-learning from millions of images. In [10], the authors study the design of a controlling neural network using adaptive resonance theory. In [11], the authors developed a new method based on neural networks that allows learning multichain

In our previous a work, we proposed a method where the capacities of two specific kinds of neural processors [12], vehicle driving and path planning, were stacked as to control mobile robots. Each processor behaves as an independent trained agent that, through simulated evolution, is encouraged to socialize through low-bandwidth, asynchronous channels. Under evolutive pressure, agents develop communication skill and cooperative behaviors that raise the level of competence of vision-guided mobile robots, allowing a convenient autonomous exploration of the environment. In [13], a neural behavior-initiating agent (BIA) was proposed to integrate relevant compressed image information coming from other cooperating and specialized neural agents. Using this arrangement, the problem of tracking and recognizing a moving icon was solved by three simpler and separated tasks. Neural agents associated proved to be easier to train and show a good general performance. The obtained neural

that bring this genuine spontaneity and autonomy to our robots.

activity of the individual.

126 Human-Robot Interaction - Theory and Application

bees [5] and submillimeter worms [6].

redundant structure configuration during grasping.

**1.1. Previous works**

We have constructed neural models written in C++ that behave or can be trained to behave as different kinds of neural sub-processors including self-activated behavior initiators, wave generator, timing generator, and general purpose predictive units. We also develop C++ model for an expandable mechanical universe where neuro-mechanical nodes composed by muscles, sensors, joints, mechanical structures, and mechanical links can be connected together, creating wormlike robots extrapolable to many components. The robot universe includes other items such as ball, floor, fixed walls, and one flexile moving wall that can be manipulated by humans.

Through evolutive methods, the neural subcontroller learns to share clue information along a few low-bandwidth channels producing a self-motivated robot with a high level of competence. We show that this proactive robot is capable of interacting with humans through appropriate interfaces and learning complex behaviors that satisfy unknown, subjacent human purposes.
