EXERCISES
2.4 ANTHROPOMORPHISM — A TREASURE TROVE FROM MOTHER NATURE
Robots are often motivated from mother nature. Da Vinci’s birds and lions are the earliest examples of anthropomorphism. Four particular concepts have helped robotics cross the bridge from the natural world to the artificial: (1) ‘UMWELT’ or the harmony of the inner and outer world, (2) ecological approaches which are based on vision, (3) the techniques in psychology to manipulate and modify behaviour and (4) principles to design of artificial animals like Toda’s fungus eaters.
2.4.1 Concepts from semiotics — ‘UMWELT’
Uexkull’s semiotics was an inquiry into harmony of the sensed world and the real world, studied across a large number of creatures and established sensing as a tool for the agency to construct an ‘inner world’ and thus find meaning to objects.
The word ‘UMWELT’ in German means both environment and ecology. Uexkull pictured it as unique phenomenal worlds embracing the agent, as though ‘UMWELT’ is much like a soap bubble surrounding the organism. The ‘UMWELT’ surrounds the agent and contains objects and processes pertaining to its natural workings. Also the agent is always striving towards a perception of reality, thereby actively creating its ‘UMWELT’4. Therefore, an agent’s ‘UMWELT’, is the best agreement formed by the perceptual and effector worlds together, as shown in the schematics of the functional cycle in Figure 2.4. The organism’s nervous system is equipped with receptors (sense net) and effectors (effect net). The sense net helps to represent some particular features of the organism’s UMWELT. The representation produced by a unique receptor is marked to a feature sign. The effect net produces muscle impulse patterns and stimulates effector cells to produce effector sign. If a particular feature in the organism’s UMWELT stimulates the cells of the receptors, the corresponding sense net produces a feature cue. This cycle is incremental and enriched with
4Uexkull introduced this term in 1909 in his bookThe Environment and Inner World of Animals.
FIGURE 2.4 Functional cycle and the inner worldattempts to unify sensing and acting, over the the perceptual field and the motor field respectively. This helps to incrementally build the agent’s inner world.
feedback and is therefore reafferent. Identifying features in an organisms UMWELT to suit its well being develops with gradual experience, as it explores more of its world.
A simple world for a simple organism such as a paramecium or a tick, a well-articulated world for a complex ones, such as human beings, a bat’s ‘UMWELT’ is governed by echolocation as is shown in Figure 2.5. The soap bubble analogy is most appreciable in lower animals limited by a few number of senses, and is not very visible for higher animals and human beings.
These ideas conveyed that the mind and the world are inseparable, because it is the mind that interprets the world for the organism. It will be seen, that Uexkull’s semiotics forms the basis of enactive cognition, which is now considered the fundamental model of cognition in embodied AI.
Illustrating the concept of ‘UMWELT’, with examples:
1. Female tick: The tick often nests on the human/animal body and feeds on human blood. The female tick is oblivious to most of the things that we human beings find interesting. The life of a tick is concerned with finding a warm-blooded mammal to feed on its blood and to lay its eggs and then die. The tick is both deaf and blind but has a photosensitive skin and after mating it is guided by the sun to the highest point on a blade of grass or the top of a branch, until its prey, the mammal, comes along. The tick is able to recognise its prey by the smell of its sweat (butyric acid from sebaceous follicles) typical of all mammals, and then the tick falls towards its prey. Once on the mammal, its next job is to find a warm, hairless spot to feed on, nest and lay eggs. These three biosemiotic indicators make up the tick’s ‘UMWELT’, (1) guiding by the sun, (2) sensing the sweat of the mammal and (3) sensing the heat and finding a hairless spot on the prey mammal.
2. Fighting fish: Fighting fishes do not recognise their own reflection unless at a minimum of 30 times per second [35]. This helps us make inroads into their ‘UMWELT’. These fishes prey on fast moving fishes and other sea creatures. Their motor processes are at
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FIGURE 2.5 ‘UMWELT’ — Unique Phenomenal World. Here the ‘UMWELT’ for a bat, shown as a soap bubble is more or less determined by its echolocation. The phenomenology is unique to the creature, and helps it to perceive its world. The bat is also a topic of interesting discussion in artificial consciousness, as will be seen inchapter 9.
reduced speeds — like a slow-motion camera — thus their ability to hunt down their
‘slow moving’ prey.
3. Bee: It has been observed that bees have a preference for alighting on objects which have broken shapes such as stars and crosses, and are seen to avoid compact forms such as circles and squares. A bee has the primary job of collecting nectar from blossoming flowers. Correlating this to a bee’s ‘UMWELT’, it perceives blossoming flowers as stars and crosses, while buds are perceived as compact forms — probably as circles and squares. It is worth noting that the bee is probably one of the lowest creatures that has awareness of shape and form, while lower creatures such as the paramecium, mollusk, earthworm, tick, etc., lack in such schemata, and thus have no true perceptual images in their inner worlds. All higher creatures, such as animals and human beings have an appreciation for shape, form and direction, which shows in their inner worlds.
This was a radically different attempt to understand biology, not as a technical model but as an ever-unfolding and ever-transforming harmonious agreement between the autonomous being and its ‘UMWELT’, where the focus is not to study agents as objects, but as active subjects via their interaction with their environments. This approach has helped to develop the concept of embodiment and ecology in embedded AI, as will be discussed in the rest of the chapter.
2.4.2 Concepts from ecology — Uniqueness of vision
Visual perception has a special place in AI. It is usually the strongest of all human perceptions, and forms the most persistent memories. Vision comprises both sensation, as a reactive stimuli (to see) and also as a deliberative perception, relating objects and events in the environment (to watch). Various theories for vision have been forwarded over
Gibson [121] in the 1980s and Marr [221] in the 1990s provided the impetus towards modelling vision as an ecological phenomenon. The function of vision is to produce descriptions and representations of what’s out there in the field of view, the shape and space and spatial arrangement, and also help in acquiring higher level information such as reading a sign board. Gibson suggested that vision is not merely limited to such cognitive processes but often the mechanism to orchestrate motion. Gibson’s bottom up approach was the first in psychology to relate motion in tandem with perception, a radical departure from traditional theories of vision. Gibson was critical of both behaviourism and internal representations and developed the concept of optical flow. Similarly Marr rejected image processing and considered vision as information flow than interlinking of isolated standalone phenomenon.
Gibson’s approach was based on information flow and considered the environment and the observer as ‘inseparable pairs’. Where the environment should not be modelled as a coordinate frame but rather in ecological aspects; medium, substance and surfaces etc. as animals perceive the latter not the former. Vision is modelled on the optic array, which is formed of all the rays converging on a given point. The optic array is different at each point, so for an observer in motion the array changes continuously creating an optical information flow field. The transformations in the optic array sampled by a moving observer simultaneously specify the path of locomotion, rather than the more traditional coordinate frame for start point and end point etc. The Optic flow contains information about both the layout of the surface and the motion of the agency. Gibson’s model states that properties of the environment as perceived by the agency are usually due to the physical and physiological ability of the observer. For example, (1) surfaces of a certain height, size and inclination afford sitting on by humans, those of a different height and size afford stepping up on and (2) objects moving at a certain speed afford catching. Others are too fast or too slow etc. It is to be noted that these actions are ubiquitous reactions from human psychology and not learned with experience etc. Perception of these possibilities for motion are essential, and they are contained in the optic array. To start locomotion is to contract muscles so as to allow the forward optic array to flow outward; to stop locomotion is to make this flow stop.
Therefore, the agent’s internal forces are a function of the optic flow [98].
Finternal=g(f low) (2.2)
Where Finternal is the internal force and f low is the optic flow. Utilising such control laws and extending ecological psychology into robotics has seen promising results in robots interacting in a real-time dynamic environment with obstacles and human beings. Such a radical theory of vision clearly lacks in quantifying optic flow or internal force, as it is dependent on the context and the agency etc. However, it does hint towards ideas that locomotion is not rote Newtonian mechanics, but is driven by perception which is triggered
45 for psychological and ecological reasons. Extending Gibson’s framework in biology, Duchon [99] summarises the following principles for ecological robotics.
1. The agent and the environment are ‘inseparable’ and treated as a system 2. The agent’s behaviour emerges out of the dynamics of the system
3. Depending on the relationship between perception and action, the agent is tasked with mapping available information to the control, to realise a desired state for the system 4. The environment provides information and hints to encourage adaptive behaviour 5. Since the agent is a part of the environment, no a priori or real-time 3D map or model
is needed
Duchon demonstrated robot navigation and obstacle avoidance using the above principles.
Since, cognition is not something that occurs ‘inside’ an agent, but is attributed to its embodiment, the cognition of the agency in its environments is marked in the adaptive interaction itself. Therefore, the environment that is experienced by the agent is not only conditioned by its own agency, but is enacted-in, in such a way that it emerges through the bodily activities of the agent. The experienced world is portrayed and determined by mutual interactions between the physiology of the agent, its sensorimotor circuit and the environment5 as is shown in Figure 2.6. This means that the agent’s body is a live, experiential structure and also the context for all cognitive actions [333] and therefore perception doesn’t happen to an agency or inside an agency, but rather the perceiving is the action. According to the enactive approach [320], agency ’brings forth’ their own cognitive domains, with the ability to exercise some control on their own selves, primarily for well being and sustenance. Therefore, the agent makes its agency in direct interaction with itself and its environment. Symbolic computation and the informational model is not the essence of cognition, neither can external events dictate the cognitive process. Cognition is contextual, and never happens in abstraction, and it is the adaptive coordination and control of actions achieved by the overlap of embodied and situated cognition. Lastly, Experience is important to the understanding of cognition and the mind.
O’Regan and Noe [259,260] have suggested that vision and visual consciousness is indeed a sensorimotor activity which is strongly tied to action, and works more as exploratory sensing than as a strict sensorimotor pair. This explorative process is mediated by the knowledge of what the authors have termed ‘sensorimotor contingencies’. This approach emphasises the phenomenal character of vision rather than its more traditionally held representational nature.‘Sensorimotor contingencies’ can be defined as the regularities of sensory stimulation as per the action of the perceiver. The perceiver’s vision acquaints itself to known shapes, colour, texture, lighting and active processes, which helps it to discern the known world.
For example, since vision is enabled as sampling of a two-dimensional projection of three-dimensional space the top view of a 2D square and a 3D cube may appear to be the same, but a slight movement towards or away from the object, leads to expansion or contracting of the amount of light entering the retina and therefore the eye will perceive it differently. Another example is, since each colour dictates the amount of light reflected, each colour patch corresponds to a unique contingency, and thus often conveys psychological meaning, viz. red which has light reflectance values between 0.4 and 0.5, reflects nearly half of the incidence radiations, and therefore causes higher retinal excitation than other colours.
Hence, red is associated with excitation, warmth etc.
5The enactive approach was suggested by Varela, Thompson and Rosch, by extending Merleau-Ponty’s phenomenology of the body.
FIGURE 2.6 Enactive agencyis a continuous process of exploration of the environment where the self-constitution is the agent’s identity, which is conserved during coupling with its environment (continous arrows). The coupling relations change with adaptivity (dotted arrows). Adapted from Froese and Di Paolo [113].
The experience of vision occurs when the agent has mastered or experienced, over a good number of times, these known laws of how the brain codes visual attributes, to develop
‘sensorimotor contingencies’ and enable explorative sensing in other words, enactive inquiry of the world. As we have seen, vision is strongly tied to action and it is arguably the most important sensing capability of enactive agency.
2.4.3 Concepts from psychology — Behaviourism
Behaviourism, a branch of psychology, is the study of the relation between one’s environments and the impending behaviour. It is broadly a ‘black box’ approach and the cerebral functions of the brain are irrelevant. Behaviourism found great favour between 1920s to 1950. Early pioneer were Pavlov, Twitmyer and Thorndike, all working independently.
Pavlov’s experiments in the 1890s were focused on digestion in dogs as shown with a hint of humour inFigure 2.7, where the dogs would first be exposed to the sound of the metronome, and then food was immediately served. After several such trials, it was observed that the dogs began to salivate after hearing the metronome. The metronome had acquired the property of stimulating salivary secretion. Pavlov’s findings confirmed that a previously neutral stimulus, the metronome, after many trials had become a conditioned stimulus that would provoke salivation. Similar results were reported by Twitmyer. This modification of an animal behaviour where a biological stimulus is paired with a previously neutral stimulus (such as sound or light etc.) is known as classical or Pavlovian conditioning.
In the 1930s, Skinner developed operant conditioning, which relied on modifying behaviour by its consequence, either by reinforcement or punishment and not by manipulating a reflex of the Pavlovian conditioning. Typical Skinner box experiments on rats, as shown inFigure 2.8, presented the subject rat with positive reinforcement such as food on pressing a particular lever and negative punishment such as denying food or positive punishment such as subjecting the rat to a minor electric shock or a spray of cold water on pressing a different lever or a button. Over time, the rats would press the food lever more frequently and avoid the punishment causing levers/buttons. Over time a stimuli worked as
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FIGURE 2.7 Pavlov’s dogs.One of the earliest experiments in behaviourism was conducted by Pavlov, where he studied digestion in dogs for his theory of conditioned reflex. (c) 2003 Mark Stiverswww.stiverscartoons.com, used with permission.
a means for manipulating response and therefore as a control of the subject. The five types of operant conditioning are shown inFigure 2.9.
Reinforcement can happen in two ways, in positive reinforcement a response is followed by a reward such as, viz. presented with food on pressing a lever, and in negative reinforcement a response leads to a stemming of an unpleasant influence such as, viz. the rat was subject to a loud annoying noise which was switched off when it pressed a lever/button.
Punishment also has two modes, in positive punishment a response is followed by something unpleasant, and in negative punishment the response removes something pleasant. Both scenarios discourage the response. It is not always easy to discern between punishment and negative reinforcement. Usually, punishment is characterised by modulating fear and engages an aggressive response, and punishment is suppression and the response is seen once when punishment is removed over a long period of time. In extinction, a previously reinforced response is no longer reinforced with either positive or negative reinforcement, and as a result of not experiencing an expected outcome weakens the response. Skinner believed that operant conditioning can be used to design in an organism extremely complex and rich behaviour.
The contrast of classical and operant conditioning is that the former warrants a reflexive behaviour while the latter works to manipulate a stimuli to control the subject’s behaviour.
Behaviourism has directly influenced agent-based robotics, the takeaways are:
1. Behaviourism is primarily concerned with observable behaviour, as opposed to internal events like thinking and emotion. Observable (i.e., external) behaviour can be
FIGURE 2.8 The Skinner box.is the experimental tool to study both operant conditioning and classical conditioning. The box is a glass-lined enclosure that contains a key, or a bar or a lever that an animal can press in response to a specific stimuli, such as a light or sound signal, which will then release food or water as reinforcement.
FIGURE 2.9 Operant conditioning.Reinforcement and punishment are the control mechanisms of Skinner’s approach.
objectively and scientifically measured. Internal events, such as thinking, should be explained through behavioural terms — or eliminated altogether.
2. People have no free will; a person’s environment determines behaviour 3. When born, our mind is a blank slate, with no memory, nor any experience.
4. There is little difference between the learning that takes place in humans and that in other animals. Therefore research can be carried out on animals as well as humans.
5. Behaviour is the result of response to stimulus. Thus, all behaviour, no matter how complex, can be reduced to simple stimulus response models. Skinner’s stimulus-response (SR) theory was an effort to reinforce a positive behaviour while eliminating an undesirable behaviour.
6. All behaviour is learned from the environment. New behaviours are learned through classical or operant conditioning.
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2.4.4 Artificial animals — ANIMAT
ANIMATs or artificial animals, are robots motivated from animal behaviour and kinematics.
After Toda, similar models of artificial animals were suggested by various others:
Braitenberg, Holland and Brooker and Wilson [356, 357], who coined the term in the mid 1980s. Wilson’s ANIMATs were an advancement on Walter’s turtles, as they not only interacted with the environment, but also learned from experience6, and behaviourism and conditioned response formed a founding principle for Wilson’s model. In particular included paradigms such as, rule-adaptiveness, genetic evolution, emergence and association. Wilson identified four principles to define an ANIMAT;
1. The artificial animal exists in a sea of sensory signals, but at any given instance only some of the signals are significant (to motor action) while the rest are redundant.
2. The artificial animal is capable of action, which in effect tends to change these signals.
3. Certain signals and/or the absence of certain signals have a special status for the artificial animal, viz. lack of food will trigger survival instincts, sight of a predator, life-threatening terrain, etc. will call into question the artificial animal’s survival and supersede all other behaviours.
4. The artificial animal acts externally and also through internal operations, so as to best optimise the occurrence of the special signals.
While the first two principles are about the concepts of sensory motor and embodiment, the third enshrines survivability as the most fundamental behaviour and the fourth incorporates conditioned response and rule adaptiveness.
It is suggested that the most suitable rule which binds the sensory signal with desired action will have to be ‘discovered’ as a serendipitous exercise by the ANIMAT, or otherwise the undesired rules should be ignored. Mirroring an animal allows for the exvivo inception [349] of behaviour without extraneous influences; thus it allows a scope to engineer these behaviours with precision, flexibility and efficiency in a concerned context that may never be observed in studies with real animals, as will be illustrated with examples in later sections.
ANIMAT research was instrumental in development of behaviour-based paradigms and also in shaping the discipline of artificial life (ALIFE).
A poetic adaptation of the biblical tale of genesis is shown inFigure 2.10, here Beavers considers various principles of ANIMAT development, such as learning, emergence, forming into complex beings with growth of intelligence, etc., to make the case for artificial evolution and growth. Darwin’s natural selection effected by natural calamities such as the lore of Noah’s flood and a futuristic dystopia robot apocalypse are there to maintain quality and these also work as fail safe techniques and the proverbial ‘kill switch’.
ANIMAT has been instrumental in probing the natural world to exploit designs from mother nature, to work in tandem with known mathematical models and technology.
However, there is no single route to design ANIMATs, and researchers have used various methods to bring their artificial animals to life. The brachiating robot controller as shown in Figure 2.11 was designed by observing primates swinging from one branch of a tree to another. Nakanishi et. al [250] modelled this motion as a modified pendulum oscillation and added machine learning facets with a neural network. In complete contrast is the gastrobot [353] and later the ECOBOT series of robots as its mature avatars, which are designed on the digestive process and the gastrointestinal system in human beings and are aimed to attain energetic autonomy using a microbial fuel cell (MFC).
6Wilson considers Walter’s turtles as ‘sub-ANIMATs’, as they lack a learning capability.