**4. Computational requirements**

Having considered his mechanical requirements, Rob now turns to computational requirements for his robot. In his mind, this means anything that could be needed to make the hardware perform interesting behaviors. This includes, but is not limited to, learning how to solve (not necessarily pre-defined) tasks in uncertain environments under changing or undefined conditions and interacting with humans in a non-trivial manner.

Within robotics, Rob has distinguished two main approaches of interest to him: one which we shall call the traditional symbolic approach (also known as *Cognitivism*) and one that we will name embodied approach (also known as *Emergent Systems*). Although both approaches can sometimes use the same technologies (neural networks are a prime example of that), they differ philosophically in the sense that symbolic is first and foremost an AI approach

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In the simplest form, the embodiment can provide a solution to the *symbol grounding problem* (Harnad, 1990), namely the problem of attaching a real meaning to symbols. For example, the sequence of symbols *to grasp* has no meaning by itself; it only becomes meaningful when an agent can associate it with the corresponding sensory perceptions and motor actions. In a more advanced form, the embodiment of a particular agent can actually reduce the computations necessary by a controller in the traditional sense by what is called

The remainder of this section thus first briefly introduces the core concept of embodied cognition as relevant to roboticists such as Rob. We then consider examples of research which uses embodiment in the sensory-motor grounding sense as well as examples of morphological computing as such. Finally, we briefly discuss *dynamic field theory*, a particular modeling

Thill (2011) provides a brief introduction of embodied cognition as relevant to the design of artificial cognitive systems in general (rather than the specific case of robots). The brief introduction here follows this discussion, albeit adapted to suit the needs of roboticists in

The basic claim within embodied cognition (Anderson, 2003; Chrisley & Ziemke, 2003; Gallagher, 2005), as mentioned before, is that the body intrinsically shapes the mind. A simple illustration of this influence comes from an early study by Strack et al. (1988), who showed that people rate cartoons as more funny when holding a pen between their teeth (activating smiling muscles) than when holding a pen between their lips (activating frowning muscles). Another example is the SNARC (Spatial-Numeric Association of Response Codes) effect (Fischer, 2008, see Pezzulo et al. 2011). In essence, people respond to smaller numbers faster with the left hand than with the right hand and vice versa for large numbers. Similarly, when asked to produce random numbers while simultaneously shaking their heads left to right, people are biased towards smaller numbers during left turns than during right ones. A further illustration of the body's influence on mental processes can be seen in language processing, particularly when involving action verbs. Such processing (for instance while reading a sentence) can be shown to activate motor regions within the brain and lead to either facilitation of or interference with executing an action (involving the same end-effector as the

sentence, see Chersi et al., 2010, for a review and a more thorough discussion).

Although examples such as those above and several more not discussed in detail here (but see Pezzulo et al., 2011, for additional ones) clearly show that body and mind are intertwined, it is still an open debate how intricate the relationship is. While researchers like Pfeifer & Bongard (2007) or Pezzulo et al. (2011) argue strongly in favor of such an intertwined relationship, Mahon & Caramazza (2008), for instance, are amongst those who favour a view that sees mental processes operating at an abstract symbolic representation, with concepts that are merely grounded by sensory-motor information. In other words, in this view (also related to Harnad, 1990), cognition does not require a body as such, although the latter may be necessary

The relevance of embodied cognition to robotics in general is thus clear: when designing the controller for a robot, one faces decisions as to how much the higher-level cognitive abilities of the machine need to involve the particular embodiment and sensory features. Thus the relationship between robotics and the study of embodied cognition is mutually informative: on one hand, a robot provides cognitive scientists with a real body in which to test their

morphological computing (Pfeifer et al., 2006).

**4.2.1 Embodied cognition**

to ground the symbols used.

particular.

approach explicitly built on ideas from embodied cognition.

with its roots in computer science, whereas the embodied approach has its roots in the cognitive sciences. An important consequence is that the traditional symbolic approach sees computations as fundamentally disassociated from any specific hardware and therefore operating in an abstract, amodel level while this is not true for the embodied approach. This section therefore begins by briefly discussing some examples of the symbolic approach and its limitations before moving on to embodiment and its relevance to robotics in general and Rob's robot in particular.
