**4.1 Traditional symbolic approaches**

The symbolic paradigm sees cognition as a type of computation between any information that can be represented in code and the outcome of the operations carried out on those codes (Vernon, 2008). In other words, symbols are manipulated through a set of rules. This determines that the way an artificial system sees the world around it is only through the eyes of the designer/programmer of that system. All its freedom and limitations will be dependent on the amount of symbols and rules that the person who created this model wrote in code.

Symbolic approaches make use of mathematical tools that help them select the best response within a specific goal. Among the most important are probabilistic modeling and machine learning techniques. Both of these have been relatively successful in solving task specific problems. Hundreds if not thousands of commercially available products for character, voice, and face recognition are on the market right now. Classification and regression problems are easily solved by this approach. Nonetheless, the dependency of the system to the programmer's view of the world challenges symbolic approaches in solving problems such as the symbol grounding problem, the frame problem, and the combinatorial problem (Vernon, 2008).

Rob has seen Asimo tripping and falling a couple of times without any reaction to protect itself from the fall. This gives Rob a clue about the limitations of the symbolic approach for controlling unexpected situations. At some point a group of engineers devised the most likely situations that Asimo would encounter when climbing or descending a number of stairs given that they would be flat, non slippery and nobody else would be around. So they knew about the symbols present in an specific sequence of events, they knew the constraints needed and imposed and finally, they wrote enough rules to fulfill the task.

Rob starts thinking about those events that cannot be foreseen and therefore cannot be calculated. What if a pebble is in the wrong place or at the wrong time? What if a cat crosses my robot's path? What if a person pushes my robot? After all, my robot will be moving around in places where any other human moves around, and we encounter these types of situations all the time. Rob has already considered the importance of a good design for both actuators and their wrapping materials. This would help by transferring some of the problems from the digital control part into the physical interactions with the environment. Yet a symbolic approach does not seem to be as open as needed in the unconstrained environments that humans work and live.

#### **4.2 Embodied and cognitively inspired approaches**

Rob thus comes to the realization that the traditional symbolic approaches do not fulfill his requirements. In this part, he therefore discusses embodied approaches. In a nutshell, this class of approaches sees the particular body of an agent as intertwined with its mind: the cognitive abilities of an agent is a consequence of both and cannot be understood by studying one in the absence of the other.

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 morphological computing (Pfeifer et al., 2006).

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 approach explicitly built on ideas from embodied cognition.

#### **4.2.1 Embodied cognition**

10 Will-be-set-by-IN-TECH

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

The symbolic paradigm sees cognition as a type of computation between any information that can be represented in code and the outcome of the operations carried out on those codes (Vernon, 2008). In other words, symbols are manipulated through a set of rules. This determines that the way an artificial system sees the world around it is only through the eyes of the designer/programmer of that system. All its freedom and limitations will be dependent on the amount of symbols and rules that the person who created this model wrote in code. Symbolic approaches make use of mathematical tools that help them select the best response within a specific goal. Among the most important are probabilistic modeling and machine learning techniques. Both of these have been relatively successful in solving task specific problems. Hundreds if not thousands of commercially available products for character, voice, and face recognition are on the market right now. Classification and regression problems are easily solved by this approach. Nonetheless, the dependency of the system to the programmer's view of the world challenges symbolic approaches in solving problems such as the symbol grounding problem, the frame problem, and the combinatorial problem (Vernon,

Rob has seen Asimo tripping and falling a couple of times without any reaction to protect itself from the fall. This gives Rob a clue about the limitations of the symbolic approach for controlling unexpected situations. At some point a group of engineers devised the most likely situations that Asimo would encounter when climbing or descending a number of stairs given that they would be flat, non slippery and nobody else would be around. So they knew about the symbols present in an specific sequence of events, they knew the constraints needed and

Rob starts thinking about those events that cannot be foreseen and therefore cannot be calculated. What if a pebble is in the wrong place or at the wrong time? What if a cat crosses my robot's path? What if a person pushes my robot? After all, my robot will be moving around in places where any other human moves around, and we encounter these types of situations all the time. Rob has already considered the importance of a good design for both actuators and their wrapping materials. This would help by transferring some of the problems from the digital control part into the physical interactions with the environment. Yet a symbolic approach does not seem to be as open as needed in the unconstrained environments that

Rob thus comes to the realization that the traditional symbolic approaches do not fulfill his requirements. In this part, he therefore discusses embodied approaches. In a nutshell, this class of approaches sees the particular body of an agent as intertwined with its mind: the cognitive abilities of an agent is a consequence of both and cannot be understood by studying

imposed and finally, they wrote enough rules to fulfill the task.

**4.2 Embodied and cognitively inspired approaches**

Rob's robot in particular.

2008).

humans work and live.

one in the absence of the other.

**4.1 Traditional symbolic approaches**

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 particular.

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 to ground the symbols used.

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

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Rob's Robot: Current and Future Challenges for Humanoid Robots 291

utility of the overall architecture in three tasks. First, they show that the architecture enables a robot to learn hand movements by imitation. Second, they demonstrate that it can learn both end-point and cyclic movements. Finally, they illustrate the ability of the architecture to

Yamashita & Tani (2008) similarly use a recurrent neural network at the heart of their robot controller but endow it with neurons that have two different timescales. The robot then learns repetitive movements and it is shown that the neurons with the faster timescale encode so-called movement primitives while the neurons with the slower timescale encode the sequencing of these primitives. This enables the robot to create novel behavior sequences by merely adapting the slower neurons. The encoding of different movement primitives within the neural structure also replicates the organization of parietal mirror neurons (Fogassi et al., 2005), which is at the core of other computational models of the mirror system (Chersi et al.,

While the RNNPB architecture encodes behavior as different parametric bias vectors, Demiris & Hayes (2002); Demiris & Johnson (2003) propose an architecture in which every behavior is encoded by a separate module. This architecture combines inverse and forward models, leading to the ability to both recognize and execute actions with the same architecture. Learning is done by imitation, where the current state of the demonstrator is received and fed to all forward modules. These forward modules each predict the next state of the demonstrator based on the behavior they encode. The predicted states are compared with the actual states, resulting in confidence values that a certain behavior is being executed. If the behavior is known (a module produces a high confidence value), the motors are then actuated accordingly. If not, a new behavioral module is created to learn the novel behavior being demonstrated. A somewhat similar model of human motor control, also using multiple forward and inverse models has been proposed by Wolpert & Kawato (2001), with the main difference being that in this work, all models (rather than simply the one with the highest confidence value) contribute to the final motor command (albeit in different amounts). Finally, Wermter et al. (2003; 2005; 2004) developed a self-organizing architecture which "takes as inputs language, vision and actions . . . [and] . . . is able to associate these so that it can produce or recognize the appropriate action. The architecture either takes a language instruction and produces the behavior or receives the visual input and action at the particular time-step and produces the language representation" (Wermter et al., 2005, cf. Wermter et al., 2004). This architecture was implemented in a wheeled (non-humanoid) robot based on the PeopleBot platform. This robot can thus be seen to "understand" actions by either observing them or from its stored representation related to observing the action. This is therefore an example of a robot control architecture that makes use of embodied representations of actions. In related work on understanding of concepts/language in mirror-neuron-like neural robotic controllers (Wermter et al., 2005) researchers use the insight that language can be grounded in semantic representations derived from sensory-motor input to construct multimodal neural network controllers for the PeopleBot platform that are capable of learning. The robot in this scenario is capable of locating a certain object, navigating towards it and picking it up. A modular associator-based architecture is used to perform these tasks. One module is used for vision, another one for the execution of the motor actions. A third module is used to process linguistic input while an overall associator network combines the inputs from each module. What all these examples illustrate is that insights from mirror neuron studies (in particular their potential role in grounding higher-level cognition in an agent's sensory-motor experiences) can be useful in robotics. In terms of using insights from embodied cognition,

associate word sequences with the corresponding sensory-motor behaviors.

2010; Thill et al., In Press; Thill & Ziemke, 2010).

theories and computational models thereof (Pezzulo et al., 2011) while on the other hand, insights from embodied cognition can allow the better design of robots. For Rob's robot, the latter is clearly the most interesting aspect and hence the focus of the remainder of this section.
