**3. The robot and its environment**

convenient interface, human-robot interaction triggers a learning process where some weights are modified, and the robot learns behaviors that satisfy human's expectations. After training, the behavior initiator network behaves as a default mode network (DMN) that assumes the control, burns energy, and uses other subjacent resources to initiate new behaviors, if required.

**Figure 1.** Autonomous neural controller. Modular neural-mechanical blocks called joints are snapped together to form long chains. The resulting structure is controlled by an arrangement of stacked neural controllers. A wave generator massively moves robot muscle in a coordinate way. Other shallow neural networks are trained with backpropagation do specific robotic tasks: handle sensor activities, timing generation, position detection, and ball sensing. The behavior initiator is an energy-consuming, self-activating network that satisfied syntactic rules and pushes behaviors by itself. The genetic combination of all these elements produces a self-motivated robot capable of learning, through human-robot

The basic building block of brains is the neuron, which by itself has a very especial nature in terms of energy consumption, higher than any other kind of cell in living creatures [14]. From human to rotifers and very simple worms, neurons group themselves into elaborated networks called brains, where the common factor seems to be carefully knitted structuring

"Whilst part of what we perceive comes through our senses from the object before us, another part (and it may be the larger part) always comes out of our own head." William James (1890) In classical studies of brain function, the main accepted model is based in task-evoked responses. In general, the used experiments encourage a reflexive view of how the brain

**2. Biological brains**

**2.1. Brain's wiring diagram**

complexity, with high job specializations [15, 16].

interaction, behaviors that satisfy human's expectations.

128 Human-Robot Interaction - Theory and Application

**2.2. A default mode of brain function**

The robot is assembled with elements that contain sensors, muscles, rigid joints, and a malefemale coupling (**Figure 2**). Each joint has a dedicated neuron that activates the corresponding muscle which, for the sake of simplicity, has both contraction and expansion capacities.

Joints are snapped together to form arbitrarily long wormlike robots (**Figure 3**).

In both biology and circuit complexity theory, it is maintained that deep architectures can be much more efficient (even exponentially efficient) than shallow ones in terms of computational power and abstract representation of some functions [18, 19]. Unfortunately, wellestablished gradient descent methods such as backpropagation that have proven effective when applied to shallow architectures do not work well when applied to deep architectures. Our method uses shallow nets trained with backpropagation, but these networks are thereaf-

Autonomous Robots and Behavior Initiators http://dx.doi.org/10.5772/intechopen.71958 131

As mentioned in the introduction, the *Drosophila* brain involves nonlinearity and the competence of only a few neurons in the final fly's behavior-initiating mechanism, deep buried in its brain. So, we are interested in neural structures with few neurons and genuine spontaneity. In the previous work [12], we presented a solution where the term behavior is defined as a finite sequence of events distributes in the space time. The initiation of these sequences is fired by using an n-flop, a robust network constructed with sigmoidal-type neurons sharing a common self-activating excitatory input K [20]. Being robust, it serves as foundation for other large-scale optimization structures that solve difficult jobs, such as the travel salesman problem (TSP). The n-flop is the basic building block beyond the concept of programming with neurons [20], and the term is derived from the flip-flop, a computer circuit that has only two stable states. n-flops

have n-stable states and the rooted capacity to solve high-dimensional problems [21].

In an n-flop, neurons are programmed by their weight interconnections to solve the constraint that only one of them will be active when the system is in equilibrium. To this end, the output of each neuron is connected with an inhibitory input weight (-1) to each of the other n-1 neuron inputs (lateral inhibition). In addition, each neuron receives a common excitatory input K which, on controlled situations, tends to force all neuron outputs toward 1. A solution or desired output is self-activated by rising K and forcing all neurons to some near-equilibrium but unstable "high-energy" state. At this point, K is set to almost zero, forcing the network to seek a low-energy or equilibrium-stable state. The solution given by a non-biased n-flop is a unique but unpredictable winner, which may be used as a behavior initiator, where "behavior" corresponds to a finite sequence of events, distributes in the space time. A unique winner guarantees a conflict-free operation in terms of robotic conduct. A good stabilized n-flop will always satisfy the syntactic rule "only one winner," even when neurons in the n-flop community share input weights with outside-world neurons, including other n-flops. This conduces toward a proactive scenery where it is possible to control, with events that happen inside or outside the robot, the initiation of behaviors that are being

Like in biological brains, the proposed behavior initiator constantly consumes energy, and

since it controls behaviors, it can affect the whole information processing of the robot.

ter stacked with other networks, thus becoming deep architectures.

**5. Neural controllers**

**5.1. Autonomous behavior initiators**

pushed from the inside (**Figure 4**).

**Figure 2.** The basic robotic joint with sensors, muscles, and neural driver. The joint can be snapped into long chains. Muscles are driven by the output neurons of an associate network.

**Figure 3.** Modular robot with 16 joints and dedicated driven neurons in its working environment.
