Embodied cognition: the other morphology

The very term ‘neuromorphic engineering’ implies that the structural details of neural circuits cannot be abstracted away in digital simulations: in other words, morphology (the form and function of biological neurons) matters. In embodied cognition,1 this message is also being proclaimed: it is now recognized that the agent’s physical body is plays a major role in intelligent action, and cannot therefore be abstracted away. One of the major challenges for both fields is to identify which physical aspects of the biological system under study—such as a neural circuit—support adaptation in the animal, and which are simply the result of historical accident or fulfill some other purpose besides intelligent action. For example, when considering the physical morphology of animals, the color of their fur may allow them to evade predators, but is less relevant to intelligent action than the tendon system that allows for complex locomotion. As has been argued, self-motion is one of the foundations for intelligent action in particular, and intelligence in general.2 In the sameway that an appreciation for the physical behavior of neurons is driving the creation of novel hardware in neuromorphic engineering, so too is the appreciation of the role of the body in intelligent action driving the development of novel robot hardware. In recent work3 we demonstrated an autonomous robot that exploits one of the fundamental properties that embodied agents possess: current actions influence future sensor values, an insight that is explored in more depth elsewhere.4 By moving and then recording the resulting sensor data, a robot can use such motor-sensor data pairs as training data to infer a model of the environment, but the data can also be used to create internal simulations of the robot’s own morphology. We have shown that by creating such simulations, or selfmodels, a robot may rehearse potentially dangerous actions using a self-model before attempting those actions in reality. Figure 1. An example of an autonomous robot that integrates selfmodeling and internal behavior generation. The robot executes an action (A), and then creates a set of models to describe the result of that action (B). It then uses the models to find a new action (C) that will reduce the uncertainty in the models when executed. By alternating between modeling and testing, it eventually generates an accurate model, and uses it to internally optimize a behavior (D) before executing that behavior in reality (E, F). (Courtesy of AAAS/Science.)