The implications of embodiment for behavior and cognition: animal and robotic case studies

In this paper, we will argue that if we want to understand the function of the brain (or the control in the case of robots), we must understand how the brain is embedded into the physical system, and how the organism interacts with the real world. While embodiment has often been used in its trivial meaning, i.e. 'intelligence requires a body', the concept has deeper and more important implications, concerned with the relation between physical and information (neural, control) processes. A number of case studies are presented to illustrate the concept. These involve animals and robots and are concentrated around locomotion, grasping, and visual perception. A theoretical scheme that can be used to embed the diverse case studies will be presented. Finally, we will establish a link between the low-level sensory-motor processes and cognition. We will present an embodied view on categorization, and propose the concepts of 'body schema' and 'forward models' as a natural extension of the embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5

Activity of leg motoneurons during single leg walking of the stick insect: From synaptic inputs to motor performance

In the single middle leg preparation of the stick insect, leg motoneurons were recorded intracellularly during stepping movements on a treadmill. This preparation allows investigating the synaptic drive from local sense organs and central pattern generating networks to motoneurons. The synaptic drive comprises rhythmic (�phasic�) excitation and inhibition and a sustained (�tonic�) depolarization. This general scheme was found to be true for all motoneurons innervating the muscles of the three major leg joints. A comparison e.g. with results obtained from deafferented and pharmacologically activated preparations of the stick insect suggests that both tonic depolarization and phasic inhibition originate from central networks, while the phasic excitation is mainly generated by local sense organs. Recruitment of motoneurons was studied on the flexor tibiae muscle as an example of a complexly innervated muscle. It is innervated by ~14 slow, semifast and fast motoneurons that are firing action potentials during the stance phase of the step cycle. During slow steps or steps under small load, less motoneurons are recruited than during fast steps or steps under high load. Fast flexor motoneurons are recruited later during stance phase than slow motoneurons. All motoneurons receive substantial common synaptic drive during walking. They are recruited in an orderly fashion due to the more negative resting membrane potential of the fast motoneurons, which thus require a larger and longer lasting depolarization to reach the threshold for the generation of action potentials. Because walking is not invariable but needs to be adjusted to the behavioral requirements, it was investigated how these adjustments are implemented at the motoneuronal level. The activity of flexor and extensor tibiae motoneurons was analyzed during steps with different velocities. Extensor motoneuron activity during the extension phase of the step cycle (i.e. swing phase) is rather stereotypic and invariant with stance velocity. Flexor motoneurons show two distinct periods of depolarization at the beginning of stance. The initial depolarization is also stereotypic and most likely generated by a release from inhibition that allows the underlying tonic excitation to depolarize the neuron. The subsequent depolarization is larger and faster during fast steps than during slow steps. This indicates that in the single insect leg during walking, mechanisms for altering stepping velocity are becoming effective only during already ongoing stance phase motor output. Since a large portion of the phasic excitation arises from sense organs, it is conceivable that for the generation of different stepping velocities the effectiveness of these pathways are centrally modulated, for example by variations in the degree of presynaptic inhibition

Neuroethological concepts and their transfer to walking machines

Dürr V, Krause AF, Schmitz J, Cruse H. Neuroethological concepts and their transfer to walking machines. International Journal of Robotics Research. 2003;22(3-4):151-167.A systems approach to animal motor behavior reveals concepts that can be useful for the pragmatic design of walking machines. This is because the relation of animal behavior to its underlying nervous control algorithms bears many parallels to the relation of machine function to electronic control. Here, three major neuroethological concepts of motor behavior are described in terms of a conceptual framework based on artificial neural networks (ANN). Central patterns of activity and postural reflexes are both interpreted as a result of feedback loops, with the distinction of loops via an internal model from loops via the physical environment (body, external world). This view allows continuous transitions between predictive (centrally driven) and reactive (reflex driven) motor systems. Motor primitives, behavioral modules that are elicited by distinct commands, are also considered. ANNs capture these three major concepts in terms of a formal description, in which the interactions and mutual interdependences of the various output parameters are comprised by the weight matrix of the net. Based upon behavioral observations of insect walking, we further demonstrate how a decentralized network of separate modules, each one described by an ANN, can account for adaptive behavior. Complex coordination patterns of several manipulators are controlled by imposing simple interaction rules between limbs, and by exploiting the interaction of the body with its physical environment. Finally, we discuss the technical use of leg-like active tactile sensors for obstacle detection, and we show how specific design of such active sensors may increase efficiency of walking on rough terrain. Applied to active sensors, an example of parallel, self-organizing forward models on the basis of extended Kohonen maps is presented to emphasize the potential of adaptive forward models in motor control

From locomotion to cognition: Bridging the gap between reactive and cognitive behavior in a quadruped robot

The cognitivistic paradigm, which states that cognition is a result of computation with symbols that represent the world, has been challenged by many. The opponents have primarily criticized the detachment from direct interaction with the world and pointed to some fundamental problems (for instance the symbol grounding problem). Instead, they emphasized the constitutive role of embodied interaction with the environment. This has motivated the advancement of synthetic methodologies: the phenomenon of interest (cognition) can be studied by building and investigating whole brain-body-environment systems. Our work is centered around a compliant quadruped robot equipped with a multimodal sensory set. In a series of case studies, we investigate the structure of the sensorimotor space that the application of different actions in different environments by the robot brings about. Then, we study how the agent can autonomously abstract the regularities that are induced by the different conditions and use them to improve its behavior. The agent is engaged in path integration, terrain discrimination and gait adaptation, and moving target following tasks. The nature of the tasks forces the robot to leave the ``here-and-now'' time scale of simple reactive stimulus-response behaviors and to learn from its experience, thus creating a ``minimally cognitive'' setting. Solutions to these problems are developed by the agent in a bottom-up fashion. The complete scenarios are then used to illuminate the concepts that are believed to lie at the basis of cognition: sensorimotor contingencies, body schema, and forward internal models. Finally, we discuss how the presented solutions are relevant for applications in robotics, in particular in the area of autonomous model acquisition and adaptation, and, in mobile robots, in dead reckoning and traversability detection