Biologically inspired Robotics

Through experimentation and simulation scientists are able to get an understanding of the underlying biological mechanisms involved in living organisms. These mechanisms, both behavioral and structural, serve as inspiration in the development of neural based robotic architectures. Some examples of animals serving as inspiration to robotic systems are frogs and toads [SN1], praying mantis [SN2], cockroaches [SN3], and hoverflies [SN4]. To address the underlying complexity in building such biologically inspired neural based robotics systems we usually distinguish among two different levels of modeling, behavior (schemas [SN5]) and structure (neural networks [SN6]):
  1. At the behavioral level, neuroethological data from living animals is gathered to generate single and multi-animal systems to study the relationship between a living organism and its environment, giving emphasis to aspects such as cooperation and competition between them. Examples of behavioral models include the praying mantis Chantlitlaxia ("search for a proper habitat") [SN2] and the frog and toad (rana computatrix) prey acquisition and predator avoidance models [SN7]. We describe behavior in terms of perceptual and motor schemas [SN5] decomposed and refined in a recursive fashion. Schemas are mainly characterized as perceptual or motor schemas, decomposed and refined in a recursive fashion. Schema hierarchies represent a distributed model for action-perception control. Behaviors, and their corresponding schemas, are processed via the Abstract Simulation Language ASL [SN8].
  1. At the structural level, neuroanatomical and neuronphysiological data are used to generate perceptual and motor neural network models corresponding to schemas developed at the behavioral level. These models try to explain the underlying mechanisms for sensorimotor integration. Examples of neural network models are tectum and pretectum-thalamus responsible for discrimination among preys and predators [SN9], the prey acquisition and predator avoidance neural models [SN10] and the toad prey acquisition with detour behavior model involving adaptation and learning [SN11]. Neural networks are processed via the Neural Simulation Language NSL [SN12].
Due to the heavy processing load, most models are designed and implemented at only one of the above two levels of granularity. For example, in Arkin et al. [SN13] we describe a praying mantis prey-predator model simulated and experimented in a fielded robotic system exclusively at the behavior level. These models have server as basis for new areas of robotic applications, such as ecological robotics [SN14]. On the other, models that actually involve neural networks are usually limited in scope as in [SN15], while more complex models [SN16] are simplified in terms of their inherent neural complexity. Yet, unless we can fully experiment with the more complex models, we will be limited in terms of their comprehension. For example, let us consider an extension to the latter, the toad´s prey-predator visuomotor coordination model described in Weitzenfeld et al. [SN17], with schema and neural level components shown in Figure 2.

Figure 2. Prey-Predator Frog and Toad Model Architecture

The diagram shows the two levels of modeling granularity. At the schema level, blocks correspond to schemas representing animal or robot behavior. At the neural level, blocks represent neural networks, some having a direct correspondence to brain regions [SN18]. Our goal is to be able to execute such a model in a mobile robot in an efficient manner while interacting with it and its different behavioral and structural components in real time. A preliminary architecture for this system was presented in Weitzenfeld et al. [SN19].