**1.3. Autonomous neural controller**

The proposed self-activated neural controller is developed around the ambient in **Figure 1**. The mechanical assembly is defined by a set of repetitive, neural-mechanical blocks called joints, snapped together to form long chains. The wave generator block is a shallow network directly connected to the actuators, one neuron per muscle. Its function is to massively move the muscle in a coordinate way. The timing generator, position detector, and ball detector blocks are all shallow, three-layer neural networks, trained with backpropagation to do robotic tasks related to sensor activities. The behavior initiator block is an energy-consuming network that satisfied a local, weight-encoded syntactic rule. By evolutive algorithm and through a

works, ignoring that brain functions may be mainly intrinsic, connecting by themselves information and processing it to respond to environmental demands. By carefully analyzing the allocation of the brain's energy resources, Raichel [4] argues that the essence of brain function is indeed mainly intrinsic and components of signal transduction and metabolic pathways are

Autonomous Robots and Behavior Initiators http://dx.doi.org/10.5772/intechopen.71958 129

*"In the mid-1990s we noticed quite by accident that, surprisingly, certain brain regions experienced a decreased level of activity from the baseline resting state when subjects carried out some task. These areas—in particular, a section of the medial parietal cortex (a region near the middle of the brain involved with remembering personal events in one's life, among other things)—registered this drop when other areas were engaged in carrying out a defied task such as reading aloud. Befuddled, we labeled the area showing the most depression MMPA, for "medial mystery parietal area. This cuing among the visual and auditory parts of the cortex, for instance—probably ensures that all regions of the brain are ready to react in concert to stimuli. Further analyses indicated that performing a particular task increases the brain's energy consumption by less than 5 percent of the underlying baseline activity. A large fraction of the overall activity—from 60 to 80 percent of all energy used by the brain—occurs* 

According to [3, 4], the human brain has a default mode of function controlled by a default mode network (DMN) which serves as a master organizer of its dark energy. The DMN is thought to behave like an orchestra conductor, issuing timing signals, much as a conductor waves a baton, to coordinate activity among different brain regions. This orchestrated way of doing things is described in a neat story in [4] where during a quite beach afternoon a placid tourist does daydreaming watching nowhere. In his lap rests a magazine that he's been reading for a while, suddenly a weird looking insect lands in its naked leg, firing a cascade of stimulus. The point is that during the following chains of events, where the human tries to get rid of a potential danger, the brain in fact consumes less energy during daydreaming. Raichel found that the default mode network burns energy and maintains the control of the whole body, while many other powerful neural processors (vision, sense of touch, etc.) return to the

The lesson about this biological brain story is that to survive in a complex physical world, our robots and robot controllers should have a safe default mode that keeps itself in charge, burns energy, preserves the mechanical structure in a safe condition, and is ready to evocate other

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**).

borderline of activity and keep on burning energy, ready to actuate.

Consider this functional aspect of human brain, described in Raichel's research [3, 4]:

all in a continuous state of flux.

*in circuits unrelated to any external event."*

behaviors under stimuli.

**3. The robot and its environment**

**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 interaction, behaviors that satisfy human's expectations.

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.
