Using Robots to Understand Animal Behavior
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ADVANCES IN THE STUDY OF BEHAVIOR, VOL. 38
Using Robots to Understand Animal Behavior
Barbara Webb
institute for perception, action and behaviour, school of informatics,
university of edinburgh, edinburgh eh8 9ab, united kingdom
I. INTRODUCTION
What does it mean to have ‘‘understood’’ or ‘‘explained’’ animal behav-
ior? Tinbergen’s (1963) four questions are often cited: What is the function,
how did it evolve, how did it develop (in the animal’s lifetime), and what are
the immediate internal and external causes? Of course, as Tinbergen himself
realized, these questions are not independent. They can be paired in at least
two ways, as concerning ultimate (functional and evolutionary) or proxi-
mate (developmental and immediate) causes, and as concerning historical
(evolutionary and developmental) or mechanistic1 (functional and causal)
accounts. More recently, the concept of mechanism has been more explicitly
developed in the philosophy of science as a general account of the nature of
explanation (Garber, 2002; Machamer et al., 2000). According to this view, a
scientific explanation is the description of a mechanism, that is, of an actual
physical system (rather than the more general sense of ‘‘mechanism’’ as
some sequence of causal events) consisting of parts or components, their
operations and their organization, which interact to produce the phenomena
of interest.
Importantly, this description can specify the function of the parts at
different levels (i.e., not requiring a full reduction to physical mechanics)
relative to the interests of the scientist. For example, a population biologist
might describe a system made up of replicators with different survival rates,
without being concerned precisely how replication comes about; a geneti-
cist, on the other hand, might want to understand how the particular
1
‘‘Mechanistic’’ explanation is sometimes identified with causal and contrasted to function-
al explanation. I am taking a mechanistic account to be an explanation of how (causally) a
system produces some behavioural capability (function), irrespective of how or why it came to
have that function.
1
0065-3454/08 $35.00 Copyright 2008, Elsevier Inc.
DOI: 10.1016/S0065-3454(08)00001-6 All rights reserved.2 BARBARA WEBB replication mechanism of DNA operates. While both would consider the system they describe to be ultimately grounded in basic physical principles, it is not considered necessary to describe the system down to this level to have provided a scientific account of the phenomenon. Moreover, the explanation at the higher levels might be the same for systems that differ at lower levels—the same population dynamics can result from different repli- cation mechanisms. Or, to take a more neuroethological example, the fact that vertebrate photoreceptors signal increases in light intensity by hyper- polarization, and invertebrate photoreceptors by depolarization, due to completely different transduction mechanisms, ‘‘appears to be trivial’’ (van Hateren and Snippe, 2006) from the point of view of understanding visual processing algorithms, since the response characteristics of the sensory cells to varying input (under daylight conditions at least) are suffi- ciently similar to be treated as equivalent. The idea of explanation as mechanism description implies that we could evaluate our explanations by building the machines so described and seeing if they produce the relevant phenomena. In this review, I will describe just such a literal approach to understand some of the mechanisms responsible for animal behavior. The discussion above suggests that we can attempt to replicate the relevant mechanisms, at least with respect to a certain level of explanation, without necessarily having to use the same fundamental mate- rial basis, for example, we can use novel electronic transduction mechan- isms as the front end for vision to replicate some biological mechanism of visual processing. Of course, it remains a matter of hypothesis that the lower level mechanism is not essential to understand the higher level function. There may well be conditions of testing that reveal the difference, for example, in range, sensitivity, efficiency, adaptation, and recovery prop- erties of photoreception. Nevertheless, if we can, for example, fabricate an electronic system that responds to visual motion by adjusting turning torque to successfully stabilize its trajectory (Harrison and Koch, 2000), then this can be considered a potential mechanistic explanation for the optomotor reflex seen in flies (Warzecha and Egelhaaf, 1996)—although, as with any explanation, it remains possible that the precise explanation encapsulated in this device is incorrect. Perhaps more importantly, if we think we have the correct explanation but, on building the described mechanism, find that it does not produce the expected phenomena, it is evident that our explanation is flawed or incomplete. Building machines that replicate animal capabilities as a means of under- standing how they work is an old idea, with a history stretching back to the automata of the Greeks. However, until the last century, technological limitations severely restricted the scope of such devices. In 1912, Hammond and Miessner (cited in Cordeschi, 2002) designed and constructed an
USING ROBOTS TO UNDERSTAND ANIMAL BEHAVIOR 3 ‘‘electric dog’’ which exhibited phototropism by connecting two light sen- sors via relays to a drive motor and a steering wheel. The design was explicitly influenced by Loeb’s descriptions of tropisms in animals, and Loeb (1918/1973) wrote that: The best proof of the correctness of our view would consist in the fact that machines could be built showing the same type of volition or instinct as an animal going to the light . . . the actual construction of a heliotropic machine not only supports the mecha- nistic conceptions of the volitional and instinctive actions of animals but also the writer’s theory of heliotropism, since this theory served as the basis in the construction of the machine (pp. 68–69). A fascinating account of similar early machines is provided by Cordeschi (2002). An important motivation for these machines was to demonstrate that ‘‘biological’’ capabilities such as goal directedness, learning, variety of response, and intelligence could be replicated (and hence accounted for) mechanistically, and did not require some unique or vitalist force. Hull (1943), for example, explicitly outlined a ‘‘robot approach’’: ‘‘Regard . . . the behaving organism as a complex self‐maintaining robot [that could be] constructed of materials as unlike ourselves as may be . . .’’ and argued for the development of ‘‘psychic’’ machines to illustrate that the principles of learning and goal directed behavior could be mechanized. Hull’s ideas inspired the robot rat of Ross (1935), which was built to illustrate that: it may be possible to test the various psychological hypotheses as to the nature of thought by constructing machines in accordance with the principles that these hypotheses involve and comparing the behavior of the machine with that of intelligent creatures (Ross, 1935, p. 387). But note that this work was (perhaps of necessity) imitation at a high level— this synthetic method is not intended to give any indication as to the nature of the mechanical structures of physical functions of the brain itself, but only to determine as closely as may be the type of function that may take place between ‘stimulus’ and ‘response’ (ibid). More recently, interest in building machines that reproduce specific behaviors of animals has revived, this time often aimed at replication at a deeper level of similarity, including the ‘‘nature . . . of physical functions of the brain itself.’’ This has been motivated by the recognition that we are still unable to build machines that have the capability and flexibility of animals to interact intelligently with real environments, despite huge advances in
4 BARBARA WEBB technology, particularly computational power. With potential robotic appli- cations in mind, it is believed that imitating biological systems could be a good way to discover effective solutions (Ayers et al., 2002; Beer et al., 1997; Paulson, 2004). However, a problem quickly revealed when trying to imi- tate biology to build better robots is that our understanding of the underly- ing mechanisms of the biological systems is rarely good enough to enable a direct translation into hardware and software. The problem is more funda- mental than just continuing limitations in the available technology for implemention. It is frequently found, when replication is attempted, that supposedly ‘‘complete’’ descriptions of a biological system turn out to be missing essential components. From a technological perspective, it is not important if the solution finally implemented and working on a robot is in fact the same as that operating in the animal. An implemented and working solution has passed a strong test of plausibility as a potential explanation. Nevertheless, if we want to claim that this working mechanism is also an explanation of the biological exem- plar, we must apply a number of other criteria for assessing the likelihood of this being a correct explanation of the animal’s behavior. How similar in detail is the actual performance under comparable conditions? How consistent are the implemented mechanisms with what is known of the internal components of the animal? If precise emulation is not possible, is this simply due to technical limitations, or are there more fundamental problems with the proposed mechanism? In previous articles, I have discussed at greater length the issues involved in deciding to what degree a particular implemented mechanism on a robot might be considered a good explanation in biology, and how the approach of building a physical robot compares to other modeling approaches such as computer simulation or mathematical models (Webb, 2000, 2001, 2006b). Here, I intend instead to review in more practical terms the process and outcomes of adopting this methodological approach, which could be sum- marized as ‘‘mechanistic explanation by replication.’’ This review will describe what could be considered a series of steps toward building a ‘‘complete robot cricket’’ (Fig. 1). This choice of animal is somewhat arbitrary, but seems a reasonable aim in terms of behavioral capabilities that are not (at first sight) obviously beyond modern tech- nology. Nevertheless, if we could reproduce cricket behavior, we would have solved some interesting problems—such as localization, recognition, navigation, motor control, multimodality, and learning. There is substantial biological data on each of these issues that the robot can incorporate, and where data is lacking the robot can provide indications of the possible form of future data, that is, make predictions to guide experimentation. Of course, some other interesting problems, such as high level reasoning, will
USING ROBOTS TO UNDERSTAND ANIMAL BEHAVIOR 5 Fig. 1. Three robot platforms used to test models of cricket behavior: front, a ‘‘Khepera’’ (Mondada et al., 1994), which performs phonotaxis; middle, a ‘‘Koala’’ (K‐Team, 2001) that combines auditory and visual behaviors; rear, a ‘‘Whegs’’ (Horchler et al., 2004) that emulates insect walking. be neglected, as indeed will be a number of cricket capabilities that are not viable to copy, such as digestion, reproduction, and development. While the cricket will act as a unifying theme in what follows, the work discussed will also be drawn from research on other insects that have similar capabilities to the cricket; for example, in visual control or six‐legged walking, where knowledge of these systems in the cricket is less advanced. Nevertheless, a ‘‘complete robot cricket’’ remains the goal of this research in several important senses. First, it is considered essential to this approach that the complete loop of behavior, from environment to sensors, central processing, actuation, and subsequent effect on and feedback from the environment is modeled; in particular with full consideration of the physical interactions and how they contribute to the behavioral capabilities. Second, the implemented mechanisms must, of necessity, be completely and precisely specified—there is no room for loose definitions, approximate specifications, or unexplained black boxes. This can result in the filling of gaps by speculation that, to a conventional biologist, may seem unjustifiably beyond the available evidence. Nevertheless, as I shall argue further below, this ungrounded speculation can often usefully complement the more cau- tious approach, if only by vividly exposing limitations in the conventional account. Finally, ‘‘complete’’ is intended in the sense that more than one behavior or sensorimotor system should be implemented on the same
6 BARBARA WEBB
robot, to address directly the issue of how different behaviors interact and
what mechanisms are required to organize them into a system that more
fully resembles a whole organism.
It is worth noting at this point that there are other researchers pursuing a
similar target of implementing robotic mechanisms aimed at a ‘‘complete’’
animal, although more commonly, work in this area focuses on reproducing
just one biological capability, such as coordinated walking (e.g., Delcomyn,
2004; Espenschied et al., 1996), specific visual reflexes (e.g., Srinivasan et al.,
1999), or collective interactions (e.g., Floreano et al., 2007; Melhuish et al.,
1999) (see many further examples in the following sections). To give just
three examples here of more ‘‘complete’’ approaches: Rana computatrix
(Arbib, 1982, 1987; Arbib and Liaw, 1995; Corbacho et al., 2005) is intended
to embody a number of different visual control mechanisms identified in
frogs, and explore their interactions in closed loop behavior. Although
largely tested in simulation, the mechanisms have also been explored in
robot implementations (e.g., Weitzenfeld, 2004). The Psiharpax project
(Meyer et al., 2005) ‘‘aims at endowing a robot with a sensori‐motor equip-
ment and a neural control architecture that will afford some of the capa-
cities of autonomy and adaptation that are exhibited by real rats.’’ It
includes models of hippocampus place cells, basal ganglia action selection
(based on proposal of Gurney et al., 2004; implemented on a robot by
Prescott et al., 2006), and associative and reinforcement learning. Ayers
and Witting (2007) describe an underwater robot that aims to reproduce a
wide range of sensory, motor, and behavioral capabilities of the lobster.
Crickets have been studied in behavioral biology for many years, partic-
ularly for their conspicuous communication behavior (Huber et al., 1989;
Pollack, 2001). Rather than taking a traditional approach to explaining
their behavior, the following has been organized to reflect the three themes
of completeness outlined above. What is learnt by approaching mechanisms
of cricket behavior in terms of complete physical loops through the envi-
ronment? What has resulted from the attempt to fill in all the black boxes?
How has the consideration of interaction between behaviors informed the
research? The intention is to illustrate key aspects of the biorobotic
methodology.
II. BEHAVIOR AND THE PHYSICAL INTERFACE
A fundamental difference of robotic implementations from computer
simulations of animal behavior (or hypotheses expressed verbally or math-
ematically) is that the problem of physical interaction with the environment
has to be solved. That is, there must be explicit means of transducingUSING ROBOTS TO UNDERSTAND ANIMAL BEHAVIOR 7
relevant signals and of materially affecting the surroundings. While these
processes can in principle be included in simulation, in practice they are
often finessed, for example, by assuming the animal has a ‘‘mate’’ detector,
or that intended motion in a particular direction or speed actually results in
such motion.2 A real robot has to act within a world of real physics, whereas
in simulation the environment must also be modeled, in a way that is bound
to include simplification and very likely distortion. While the robotic inter-
action with its environment might introduce its own distortions—for exam-
ple in scale—and solving the problems of real physical interactions may
sometimes seem a distraction, the solution can help emphasize the direct
role of physical constraints in shaping behavioral capabilities.
It is obvious that animals cannot respond to signals for which they have
no sensors. In some conspicuous examples, sensors have evolved to serve
highly specialized functions (e.g., a chemical receptor pathway dedicated to
a specific pheromone as found in moths; Matsumoto and Hildebrand, 1981;
Sanes and Hildebrand, 1976), so their direct role in determining the behav-
ior is clear. Nevertheless, in attempts to explain animal behavior, the extent
to which the form of the physical coupling might substantially simplify the
subsequent processing needed to control the behavior is not always appre-
ciated (Wehner, 1987). For example, there are many ways to obtain depth
information from vision, but one of the simplest is to assume a flat topogra-
phy and use elevation (Collett and Udin, 1988). Crabs, which live in such a
flat habitat, appear to be specifically tuned to this cue by systematic vari-
ance in resolution along the vertical dimension of the eye, mapping equal
depth changes to equal numbers of ommatidia spanned (Zeil and Hemmi,
2006).
Understanding the physics of these interactions can similarly be critical
for actuation (i.e., the means by which animals cause effects on the world—
principally to move themselves in relation to it, or to move parts of it in
relation to themselves). Again, this is sometimes obvious, particularly
where there have evolved specific actuators to perform specialized tasks,
such as the long ovipositor enabling the female cricket to lay eggs deeper
within a substrate (Masaki, 1986). But sometimes it is more subtle, such as
the potential contribution of compliance to stabilization in locomotion
(Kubow and Full, 1999). A robotic perspective, by forcing consideration
of the physical, can be a useful way to highlight these contributions, as the
following examples further illustrate.
2
It is true that the problem is also sometimes finessed in robotics by choosing some arbitrary
but easily detected stimulus (such as uniquely coloured objects) to represent the natural
stimulus; and equally true that some simulations may include realistic details of transduction
processes.8 BARBARA WEBB
A. DIRECTIONAL HEARING IN CRICKETS
Cricket males produce calling songs, attractive to females, by moving their
wings so as to rub a comb on one against a plectrum on the other, creating a
vibration that is amplified by a resonant area on the wing (Bennett‐Clark,
1989). The carrier frequency of the song, around 4–5 kHz for most cricket
species, is a consequence of the rate at which the teeth of the comb pass over
the plectrum. This carrier frequency is one of the cues that female crickets
use to discriminate conspecifics, that is, they approach more directly songs
that are nearer the correct carrier frequency (Oldfield, 1980; Thorson et al.,
1982). Sounds at higher frequencies (more than 15 kHz) produce an avoid-
ance reaction as they usually signify bat ultrasound (Pollack and Hoy, 1981).
The wavelength of the calling song (around 6–7 cm) compared to the
separation of the crickets ears (1–2 cm) produces a problem for localizing
the sound to which evolution has provided an elegant solution, one that was
independently discovered by engineers and called a pressure difference
receiver (Autrum, 1941). The cricket’s ears consist of a pair of tympani,
on each front leg, and associated vibration receptors that appear to have
evolved from proprioceptive chordontonal organs (Yack, 2004). The crick-
et’s body provides no significant sound shadowing for sounds of the wave-
length of the calling song (Boyd and Lewis, 1983), so there is little external
amplitude difference between the ears, and the time difference in the
arrival of sound is only a few microseconds. The tympani are connected
to each other and to a pair of spiracles on either side of the prothorax by a
set of tracheal tubes (Fig. 2A) (Larsen and Michelsen, 1978). Sound thus
reaches both the external and the internal surface of the tympani, the latter
after delay and filtering in the tracheal tubes. The vibrations of the tympani
are thus determined by the combination of filtered delayed and direct
sounds (Michelsen et al., 1994).
We have mimicked this auditory morphology in an electronic circuit used
on a robot (Lund et al., 1997). In a simplified approach, which illustrates the
principle, two microphone inputs are used.3 Each input is delayed (repre-
senting time for sound to travel through the tracheal tube) and then sub-
tracted from the other (representing sound on opposite sides of a
tympanum) to form a composite response (corresponding to the combined
sound vibrating the tympanum). In our circuit, the distance between the
3
In Torben‐Nielsen et al. (2005), we describe a more elaborate four input system using the
delays and propagation amplitudes as measured between the tympani and spiracles for the
cricket. However, this turns out not significantly to affect the behavioural results for the robot.0
Sound source External sound pressure 100
−30 30
Internal sound pressure
−60 50 60
Left Right
tympanum tympanum
Tracheal tube −90 90
Left ear Right ear
−120 120
−150 150
180
Fig. 2. Left, a schematic of the tracheal system of the cricket that provides auditory directionality. Right, response to sound from different
directions as measured by the electronic circuit used to imitate the cricket’s tracheal system on the robot.10 BARBARA WEBB two microphones was set at 18 mm, that is, ¼ of the wavelength of the Gryllus bimaculatus song carrier frequency of 4.7 kHz. The delay was set to 53 ms, the time for sound to propagate the distance between the micro- phones. Thus, the direct and delayed inputs to the sum were 180 out of phase if the sound was on the same side as the direct input; and if sound was on the opposite side, the direct and delayed inputs were in phase. The relative phase varied between these two extremes as the sound direction changed. By physically summing these inputs that are in or out of phase, the electronic analog of the tympani responded at an amplitude that corre- sponded to the direction of the sound source (see Fig. 2B). In the cricket, the length of the delays is fixed by the morphology of the auditory system. This appears to involve not just the tracheal tube length and shape, but also the contribution of the dividing membrane, which enhances the phase shift, particularly for the carrier frequency (Michelsen and Löhe, 1995). As a consequence, for both the cricket and the robot, the range of frequencies for which accurate directional information is available is limited by these physical factors. But this might directly contribute to the apparent selectivity of female crickets for a particular carrier frequency in the song (Fig. 3). When two simultaneous songs with different carrier frequencies are presented to our robot, it consistently ‘‘prefers’’ the 4.7 kHz song (Lund et al., 1997). Songs of other frequencies are equally audible to the robot, but less localizable, that is, this selectivity is achieved without any explicit process to filter for the sound frequency. Does this fully account for carrier frequency selectivity in the cricket? There are several caveats. The propagation of sound within the trachea is complex and several mechanisms contribute to shaping the delay and amplitude of the signal reaching the inside surface of the tympanum. Some of these processes appear to be frequency dependent (Michelsen et al., 1994) and so might contribute to selectivity. The phase delay tuning is unlikely to be precise due to natural variation in cricket size; movement of the legs while walking and of the spiracles while breathing are also likely to change the exact properties. However, it is not essential that the cricket has precise directional accuracy—in the robot experiments, a small mislo- cation of the sound direction only resulted in slightly more curved trajec- tories to a sound source rather than missing it entirely. Finally, there is also frequency tuning in the auditory receptors of the cricket (Esch et al., 1980; Imaizumi and Pollack, 1999; Mason and Faure, 2004; Oldfield et al., 1986) with a large proportion of the sensory cells responding best around the natural carrier frequency. As yet the precise transduction mechanism that produces this tuning, and the extent of its contribution to the behavioral preference, is unknown.
250 250
230 230
210 210
190 190
170 170
150 150
Y (cm)
Y (cm)
130 130
110 110
90 90
70 70
50 50
30 30
10 10
10 30 50 70 90 110 130 150 170 190 10 30 50 70 90 110 130 150 170 190
X (cm) X (cm)
Fig. 3. Tracks of the robot when played simultaneous calling songs at different carrier frequencies (4.7 kHz vs 6.7 kHz). The robot ‘‘prefers’’ the
cricket carrier frequency (bottom speaker) because of the tuned delay in the auditory periphery, rather than explicit frequency filtering. Left, each
trial is started from the same location; right, each trial is started from a different location.12 BARBARA WEBB B. VISUAL STABILIZATION Several behaviors observed in crickets (e.g., Böhm et al., 1991) but more thoroughly studied in other insects involve the use of visual motion to control heading. These include the optomotor response (Fermi and Reichardt, 1963; Goodman, 1965; Götz, 1975; Reichardt, 1965) already referred to in the introduction, and also centering responses, balancing optic flow on each side (Srinivasan and Zhang, 1997; Srinivasan et al., 1991), a tendency to approach dark bars (Atkins et al., 1987; Horn and Wehner, 1975; Robert, 1988; Wallace, 1958), chasing of small visual objects (Land and Collett, 1974; Srinivasan and Bernard, 1977), and responding to visual expansion by avoidance or landing manouvres (Judge and Rind, 1997; Tammero and Dickinson, 2002). Inspired by these behaviors, there has been much interest in devising suitable sensory arrays and processing algorithms to extract visual motion for use in robot control (e.g., Franceschini et al., 1992; Srinivasan et al., 1999; Thakoor et al., 2004). One interesting line of research is the development of visual sensors that com- bine photoreceptors with signal processing in analog electronic hardware, to obtain rapid and continuous motion information that can be used directly for controlling robot motion (e.g., Harrison and Koch, 1999; Higgins, 2001; Liu and Usseglio‐Viretta, 2001; Yakovleff et al., 1995). An important aspect of this technology is that it is potentially much more power efficient than conventional digital computation (Mead, 1989; although a combined ap- proach may be the optimum, Sarpeshkar, 1998). This reflects increasing recent attention paid to the issue of energy efficiency as a constraint on sensory processing in biology (e.g., Laughlin et al., 1998; Niven et al., 2007). There has been a particular interest in using this technology on flying robots (e.g., Zufferey and Floreano, 2006), which are strongly constrained in the weight of the power source they can carry (Ellington, 1999). Typically, these sensors have much lower resolution than conventional cameras, but neither animals nor robots want more powerful sensors if the cost in energy to transport, maintain, and operate them is too high. For most of these tasks, it is not necessary that the processing recovers the actual motion, but rather that it recovers ecologically relevant aspects of the motion, as required for appropriate motor responses. This may involve a system highly tuned to just one motion field, as in the locust lobular giant motion detector (LGMD) (Gabbiani et al., 1999, 2002; Rind and Simmons, 1997), which has a systematic dendritic layout making it selective for motion on a collision course with the animal. This system has been success- fully copied in several robot implementations (e.g., Blanchard et al., 2000), most recently by Yue and Rind (2006) who introduced a convolution interaction between inputs that weakened the relative input of isolated
USING ROBOTS TO UNDERSTAND ANIMAL BEHAVIOR 13 motion compared to locally coherent motion. Whether this additional process, which substantially enhanced the ability of the robot to deal with complex backgrounds and varying light levels, has a counterpart in the locust visual system is as yet unknown. Other identified neurons that appear tuned to specific motion fields corresponding to self motion (Borst and Haag, 2002; Egelhaaf et al., 2002; Krapp, 2000; Krapp and Hengstenberg, 1996) have been shown to be sufficient for extracting egomotion informa- tion using a very simple and computationally efficient linear estimator, tested on a gantry robot (Franz et al., 2004). Franceschini et al. (2007) have shown how a minimalist (two photorecep- tor) optic flow sensor based on insect elementary motion detectors (Pudas et al., 2007) can be used on a robotic helicopter to address the long‐standing issue of how a flying insect controls its height above the ground, for example in take off, landing and maintaining a constant height above varying terrain. The insect could attempt to measure altitude, or derive it from the relation- ship of ventral optic flow (VOF) to groundspeed—basically, the higher it is, the slower will be the apparent motion of the ground directly below it. Instead, it appears that the directly available cue—VOF—is used in a feedback control loop, with the insect altering its lift to maintain a set‐ point VOF, and thus a constant groundspeed:height ratio. This simple mechanism has a number of desirable properties. If the animal increases its forward speed, it will automatically increase its height as it takes off. If it gradually decreases speed, it will gradually decrease height and thus land smoothly. If the terrain rises, the VOF will increase and the insect will compensate by increasing its height. If the insect is slowed by a headwind, it will descend; this is a strategy likely to reduce the headwind, or even to lead the insect to land if it cannot make any progress against the headwind. All these features could be demonstrated on the robotic implementation and compared to reports of insect flight in these different conditions (Franceschini et al., 2007). Visual stabilization of trajectories, in insects generally, and crickets spe- cifically, is not only based on optical flow information. Crickets, like a number of other insects, have a distinct visual area at the top of the eye, the dorsal rim, specialized for polarized light vision (Labhart and Meyer, 1999). It has been shown that crickets will maintain a consistent walking direction with respect to the plane of polarization (Brunner and Labhart, 1987). Each ommatidia has orthoganally oriented receptors, and polarized light sensitive interneurons in the medulla exhibit an opponent response (Labhart, 1988), that is, to the difference between the orthogonal receptor responses, thus eliminating the effects of background illumination levels. These POL1 interneurons have three basic orientations, at 10 , 60 , and 130 with respect to the body orientation (Labhart, 1988). They also show
14 BARBARA WEBB wide field spatial integration, which improves estimates of the sky polariza- tion under cloudy conditions and increase overall sensitivity (Labhart et al., 2001). This sensory system has been copied using photodiodes and linear polarized filters on the Sahabot robot (Lambrinos et al., 1997) with three polarization opposition units at the same orientations as the animal. The signal was shown to be effective as a compass input for accurate path integration, as has been studied in detail in desert ants (Wehner, 1994; some evidence that crickets use polarized skylight for path integration is provided by Beugnon and Campan, 1989). Lambrinos et al. (2000) also noted that the compass direction could be obtained from the polarization pattern in several different ways: by finding the direction providing the maximum response, by pre‐scanning the full range of directions and storing the response in a look‐up table, or by deriving the direction analytically. The last is perhaps unlikely for the animal, but does provide a measure of what is the best possible information that might be obtained from the sensor array, which could be used in comparisons with the neural and behavioral response. Recent results from locusts (Heinze and Homberg, 2007) have suggested that there is a systematic encoding of e‐vector information in the central complex (for related results in crickets, see Sakura and Labhart, 2005). Another result from the Sahabot robot, and its testing in the real desert environment of ants, was the observation that reliable use of the polarization pattern was dependent on stable tilt and pitch position of the sensors with respect to the sky. This problem of stabilization relative to the horizon might be solved by the additional visual sensors, found in many insects including crickets, known as ocelli (Goodman, 1981). These single lens sensors, separate from the main eye, occur as three dorsal units on the head in most flying insects, one on each side and one centrally at the front. As a consequence of their low resolution and simple optics, these sensors are thought to provide only coarse information about the light level in their respective pointing directions (but see Berry et al., 2007). Their positioning makes them ideal for sensing the change in light level that occurs when deviation in roll or pitch takes the sensor above or below the horizon (Stange, 1981; Taylor, 1981; Wilson, 1978). Several robotic implementations have demonstrated the effectiveness of such a sensor system for maintaining a steady pose. Chahl et al. (2003) describe a sensor based on ocelli in the dragonfly. This is designed to detect correlated changes in illumination (e.g., one side decreases as the other increases) which are likely to be due to self motion rather than external changes. These cause a reactive motor response to attempt to rebalance the light levels. By using UV/green spectral oppo- nency (Chappell and DeVoe, 1975), the system also eliminates the effects of
USING ROBOTS TO UNDERSTAND ANIMAL BEHAVIOR 15 sun and varying sky color. Embedded in a small aircraft, it can be used to apply simple proportional control for roll and pitch to stabilize flight rela- tive to the ocelli cues. There is still much to be discovered about how the various visual—and nonvisual—stabilization mechanisms in insects may be integrated. For example, Parsons et al. (2006) have identified a lobular tangential cell, sensitive to large‐field visual rotation from the compound eyes that also responds to rotation signaled by the ocelli. Reiser and Dickinson (2003) have developed a hybrid computational/mechanical model for testing dif- ferent models of fly flight that combine visual information with propriocep- tive information from the halteres (gyroscopic sensors) under appropriate conditions of dynamic feedback. C. MECHANORECEPTORS AND AVOIDANCE Most insects are covered with a variety of mechanoreceptors, which play a role in many behaviors, particularly providing proprioceptive signals. One nonproprioceptive function is wall‐following behavior, during which the mechanical response of the antennae is used to maintain or modulate the distance from the wall (Camhi and Johnson, 1999). Real antennae have a large and complex range of tactile responses. There are campaniform sensilla, marginal sensilla, and terminal pore hairs found on each of the 150–170 segments of the cockroach flagellum; and mechanoreceptive hairs, chordontonal organs, and Johnston’s organ in the base segments (Seelinger and Tobin, 1981). However, it is unclear how much of this complexity is needed for behaviors such as wall following. We have used IR proximity sensors on a robot (Chapman and Webb, 2006) to mimic lightweight, low inertia antennae held in a fixed position relative to the body, as observed in running cockroaches (Camhi and Johnson, 1999). A minimal ‘‘distance’’ map, distinguishing stimuli at the tip versus the base of the virtual antenna, was found to be sufficient as input to a neural network that controlled both fast wall‐following‐ and escape behaviors (see Section III.B). For example, the robot could track a zig‐zag shaped wall at a speed of over 20 cm/s. It was unable to follow walls closely if the only sensing was at the antennae base, suggesting that the animals are using contact distance information from the antennae in performing this behavior, not just simple contact. Lee et al. (2008) used sensors designed to emulate different mechanical properties of cockroach antennae for wall following. A tapered tube of polyurethane emulated decreasing stiffness along the length of the anten- nae. The tube contained several flex sensors, from which an estimate of the bending of the antennae and hence the distance from the wall could be obtained. This was mounted on a robot and used to test a simple model for
16 BARBARA WEBB continuous angular adjustment using proportional‐derivative (PD) feed- back control (i.e., turning rate proportional to the distance and to the rate of change in distance). This model had already been shown to fit cockroach behavioral data (Cowan et al., 2006), and neurophysiological data from the antennal nerve suggested that sensors can provide the required distance and velocity information (Lee et al., 2008). Testing on the robot showed that this model could suffice under more realistic dynamic conditions such as friction between wall and antennae and forward speed dynamics. Although a wheeled robot was used, the model was also elaborated by connecting it to an established model of leg dynamics, showing that it is consistent with the control parameters needed for six‐legged running. The aim was ‘‘to address increasingly refined questions about the underlying biological system’’ at appropriate levels (Lee et al., 2008). Another well‐studied mechanoreceptor system in crickets and cock- roaches are the cerci, a pair of appendages on the rear of the abdomen covered in sensory hairs specialized for detecting air currents (Jacobs, 1995). One behaviorally significant source of these signals is the air move- ment created by predators, such as wasps (Gnatzy, 1996) or wolf spiders (Dangles et al., 2006). The animal can produce a very rapid (
Motor
Thoracic
direction
Thoracic CPG
Excitation
Inhibition
Facilition
Depression
Combined
Thoracic
integrator
LGI MGI
TAG GI TAG GI
trigger direction
Cercal SN trigger Cercal SN direction
Fig. 4. Top left, a robot with wind‐sensitive hairs mounted on rear cerci. Lower left, a close up of the hairs, which are constructed from light‐bulb
filament wire. Right, The neural circuit controlling the escape behavior.18 BARBARA WEBB
filiform hairs on the cerci. It seems unlikely that they are contributing only
to escape behaviors, but may in fact be involved in more sophisticated
detection of environmental features signaled by wind flow.
One limitation in this work is that the scale of the robot hair sensors
differs by several orders of magnitude from those on the cricket. Conse-
quently the transduction of the stimulus will differ, for example, in the
extent to which the hair projects from the boundary layer of airflow around
the cerci. We have recently been investigating the design of a micro‐
electromechanical system (MEMS) version of the hair sensors (Argyrakis
et al., 2007). A similar aim but rather different design is described
by Krijnen et al. (2006). These sensors should be of comparable scale to
cricket filiform hairs, although it will be difficult to obtain comparable
sensitivity, as cercal hair sensors have been estimated to be the most sensitive
receptors of any animal system (Shimozawa et al., 2003). Designs exploiting
the mechanics of silicon, as for designs exploiting subthreshold electrical
properties such as the analog electronic visual sensors discussed in Section
II.B, are much more subject to variation, distortion, and breakage than
conventional mechanical and electronic design. In this there is a clear resem-
blance to natural systems, leading us to explore how sensory interfaces and
subsequent processing can best deal with this level of variation. Redundancy,
robustness to noise, and/or adaptivity become essential elements of the
problems to be explored. We have recently proposed a mechanism for
homeostatic tuning of receptors and shown how it might allow a robot to
adjust to the variation in its sensor properties (Gonos and Webb, 2008).
D. ACTIVE SENSING
The pattern of movement made by the sensor itself can extract specific
kinds of signals. Dürr and Krause (2001) describe how stick insects make
continuous cyclic movement patterns with their antennae, coordinated to the
stepping cycle. The motion is well suited to detect obstacles of a height that
requires alteration of the stepping pattern. They describe this as using a ‘‘leg‐
like sensor to guide a leg.’’ Kaneko et al. (1998) have studied antennae‐
inspired artificial sensors and shown, for example, how the contact point of
an object on a flexible antennae (i.e., distance of an obstacle) can be recovered
by measuring the rotational compliance at the base when it is actively driven.
In Dürr et al. (2007), contact distance is instead derived from the frequency of
oscillation measured at the tip after active contact with an obstacle, using a
bionic sensor based on stick insect antennae morphology. In addition, the
damping characteristics of the vibration can be used to rapidly and reliably
differentiate materials with different compliance. In each case, the useful
signal is generated by the active movement of the sensor.USING ROBOTS TO UNDERSTAND ANIMAL BEHAVIOR 19 Movement can often disambiguate a sensory signal: A good example is that of a two‐input auditory system which, for any sound difference at the ears indicates a cone of possible locations in three‐dimensional space, sometimes called the ‘‘cone of confusion’’ (Blauert, 1997). But each possi- ble position would predict a different change in the sound at each ear for a particular movement (Wallach, 1940). In an animal or robot doing phono- taxis, equal strength of signals in both ears could mean the sound is directly behind rather than directly in front. One might expect this to result in the robot moving away from sound as often as toward it. However, as soon as there is deviation from exactly 180 (direct retreat from the sound), the resulting ear difference will induce further turning away from 180 and toward the sound. A noise‐free simulation might get stuck moving in exactly the wrong direction, but this does not happen with the robot. Note that as the cricket has its ears on its legs, simply stepping will produce the required deviation. Active vision has been a very important approach in robotics. It has often been noted, for example, that depth information can be extracted from the relative visual motion of surroundings caused by an animal’s or robot’s self motion, and it has been shown that insects can use such information (Lehrer et al., 1988). Kral (1998) reviews research on how the praying mantis moves its head to obtain depth from motion parallax before making a jump or strike. Katsman and Rivlin (2003) have built a ‘‘mantis head camera’’ that mimics these movements, and they show that the accuracy that can be algorithmically expected from this system (and occurs in the implementa- tion) is closely comparable to that reported for the animal. It has been suggested that the saccadic flight motion (straight segments followed by sharp turns) in the fly allows it to separate translatory and rotational visual motion (Land and Collett, 1997) so that the former can be used for depth information; this principle has been used successfully on robots such as the fly‐vision inspired robot described in Franceschini et al. (1992). Kern et al. (2005) recorded from large‐field motion detectors in the fly using a replay of the natural stimuli sequence created in free flight, and found strong responses to translatory optic flow. They note that: ‘‘Our conclusions obtained with behaviorally generated optic flow do not match previous conclusions based on conventional stimuli exclusively defined by the experimenter.’’ E. SIX‐LEGGED WALKING As for sensing, sometimes the problems of locomotion can be solved by simple physics. Cockroaches and other insects, at least during straight walking on flat surfaces, typically use a tripod gait (Delcomyn, 1985;
20 BARBARA WEBB Graham, 1985; Wilson, 1966) in which the front and rear legs on one side of the body move in phase with the middle leg on the other side, providing three points of support at all times. In principle, this gait pattern can be very easily reproduced. The robot RHex (Saranli et al., 2001) uses a single motor per leg, which rotates quickly while the leg is in the air to emulate the swing phase of a step, and more slowly while on the ground to emulate the stance phase. The pattern can be generated by a clock signal to produce a basic tripod gait, which along with the natural compliance of the leg allows the robot to negotiate uneven terrain at a rapid speed. In Spagna et al. (2007), the agility of this robot is improved by two further mechanical adjustments: changing the leg orientation to have a broader ‘‘foot’’ contact with the substrate; and adding spines to the sides of the leg. The demonstration, using this robot, that no explicit adaptation of the gait is necessary for running over uneven surfaces has led to tests on the cockroach itself of the amount of variation in muscle activation during escape runs over flat versus uneven terrain. It was found (Sponberg and Full, 2008) that despite substantial perturbation of the body axes on rough terrain, it could be traversed almost as rapidly as flat terrain and there was no evidence of adjustment of the muscle action potentials; suggesting that distributed mechanical feedback is the main mechanism for stabilization in this situation. Another simple hexapod robot design (Quinn et al., 2001) uses one drive motor for all six legs to produce a tripod pattern of footfalls by replacing the swing of individual legs with the rotation of three‐legged wheels (called ‘‘Whegs,’’ see Fig. 1). The rotating legs also mechanically imitate two other features of cockroach behavior when surmounting obstacles (Watson et al., 2002): that they swing their front leg high, and if encountering large bar- riers, change gait to move contralateral legs in phase. In the robot, torsional compliance in the axles means that if one leg is unable to raise the body over an obstacle, the other leg will rotate to come in phase and thus help surmount the obstacle, that is, variation in the gait pattern is obtained purely through the mechanics. Because of the efficiency and reliability of the Whegs design, we have used it to implement a version of the cricket robot that can operate over uneven surfaces, such as outdoor terrain normally encountered by the insect (Horchler et al., 2004). While this was successful in practice in showing the sound source could be located under such conditions, the limitations of this robot platform raised several new issues for understand- ing cricket behavior (Reeve et al., 2005). One is a simple issue of self‐ generated noise—from the motor in the case of this robot, from vibration due to foot‐falls in the cricket (Schildberger et al., 1988b). Another is that both robots (RHex and Whegs) described above are very limited compared
USING ROBOTS TO UNDERSTAND ANIMAL BEHAVIOR 21 to real insect walking mechanisms. An important advantage of having three or more degrees of freedom per leg, as insects do, is that this permits motion at any time in an arbitrary direction. The Whegs robot, which is steered essentially like a car, has a large turning circle that makes it effectively impossible to replicate cricket‐like paths toward the sound source. The RHex robot is able to turn on the spot by separate control of the rotation of legs on each side, but cannot manouver sideways, and is effectively no more ‘‘insect‐like’’ in the movement of its body than a dual drive wheeled robot. While this might be sufficient (with the additional effects of compli- ance and springiness in the legs) for replicating fast running in cockroaches, it is probably not adequate for reproducing realistic turning control when modeling slower moving insects that are altering their heading direction toward an attractive stimulus. With the aim of understanding how a robot with a more insect‐like leg morphology might best be able to control the complexity that results from having three joints on each of six legs, we have studied and modeled turning behavior in stick insects (Rosano and Webb, 2007). The underlying coordi- nation mechanism of the model uses a set of distributed inter‐leg influences to create a walking pattern, rather than a central generator, as described by Cruse (1991), which has been successfully implemented in a number of robots (e.g., Espenschied et al., 1996; Ferrell, 1995; Kindermann, 2001; Weidemann et al., 1994). An interesting addition made by Cruse to his control model is that coordination between the legs can be aided by a feed‐forward principle (Cruse et al., 1996, 2007). If one joint is pulling in the desired direction of motion, and there is some passive compliance in all joints, then the deviation in each joint created by the pulling leg is in fact exactly the motion required for that joint to act in coordination with the others. By using a positive feedback of this motion, each joint can make the right response for globally coordinated action, without any explicit top–down calculation or control. This is another example where the physics (the fact that the legs are mechanically linked via the body and the ground) has simplified the problem to be solved. However, actually implementing such a control scheme in a dynamic walking device is still nontrivial. For example, to avoid the system respond- ing to any external force (such as gravity), there must also be negative feedback. Also for it to change its behavior (such as turning toward a stimulus), there must be some active control, not just passive responsive- ness. From close analysis of the leg movements of freely turning stick insects, generated in response to an attractive visual stimulus (Rosano and Webb, 2007), we observed that turning motions could be well described by the following assumptions:
22 BARBARA WEBB
The front legs attempt to follow a trajectory that pulls the prothorax
(and hence the rest of the body) in a straight line toward the target.
Rotation is aided by a sideways movement of the middle legs and
differential forward motion of the rear legs (but without explicit target-
ing in either segment). Note that this improves tightness of turns but is
not in fact necessary to produce a turn (Rosano and Webb, 2006).
Each leg joint also responds to the passive motion caused by the
movement of the other legs, so that the actual movement is a combina-
tion of feedback and feed‐forward influences (a similar control ap-
proach, also inspired by the stick insect, has been developed
independently by Schneider et al., 2006).
Implementing this control on a dynamic simulation of a six‐legged robot,
it was possible to produce very effective and insect‐like turns (Rosano and
Webb, 2007). Suitable coordination of the legs also emerged (without
having been pre‐programmed) for several other situations such as walking
on tilted surfaces.
F. SUMMARY
The examples discussed in this section illustrate how aspects of physical
embodiment can be critical to the mechanisms of behavior, often simplify-
ing the problem to be solved. Replicating these physical details can be very
informative. Indeed, independently of attempts to replicate the behavior, a
robot equipped with sensors or actuators that resemble those of the animal
can be usefully employed to characterize the nature of the problem in the
real (or experimental) environment of the animal. For example, Grasso
et al. (1996) used a lobster‐like robot to understand odor‐dispersal patterns
in turbulent water. Labhart (1999) used an opto‐electronic model of the
cricket’s POL1 neuron to measure the effect of aperture size (effective
spatial field) on polarization estimates under clear and cloudy skies. The
chemical plume that attracts moths to a mate is characterized using robot‐
mounted sensors by Pyk et al. (2006), showing that the temporal structure
(patchiness) decreases with increased flow rates (wind speed), and there is
an optimum match of the sensor temporal dynamics and the flow velocity.
Ideally, to best explore and exploit these kinds of physical interactions, a
robotic model of a biological system would use sensors and actuators as
similar as possible to those of the real animal. However, the approach is still
limited by the difference in capabilities between artificial and biological
sensors and actuators. For example, in chemical sensing, most available
sensors are far less sensitive and also much more sluggish in response thanUSING ROBOTS TO UNDERSTAND ANIMAL BEHAVIOR 23 biological chemoreceptors, prompting some researchers to use the actual biological sensors, dissected from a silkworm moth and interfaced to electronics on a robot (Kuwana et al., 1999). Another plausible strategy is to attempt to preserve the mapping from the stimulus to some higher stage of processing rather than precisely mimicking every stage of the sensory processing. For example, the precise neural wiring of the insect vision system laminar is unknown, and hence a direct replication of the mechanisms of motion detection is not possible; yet many systems have been built that aim to replicate the response properties of the lobular plate large‐field cells which integrate this motion, and use these in closed loop behavior (see Section II.B). Artificial replication of biological sensor properties, though limited, is still substantially more advanced than replication of actuation. We lack any technology with the properties of muscle: able to act simultaneously as a motor, brake, shock absorber, spring, and strut (Kornbluh et al., 2002); with comparable energy efficiency, response time, stroke, force, velocity char- acteristics, accuracy, repeatability, reliability, and so on. There is active research into possible alternatives that may reproduce some of these char- acteristics, such as electroactive polymers (Bar‐Cohen, 2006). Often, failure to replicate the behavioral capabilities of animals has made more apparent the role that these physical factors play in achieving such capabilities. Abstracting away from physics and believing that a successful account of a behavioral mechanism has been provided is shaky ground. Pfeifer et al. (2007) discuss how convergent ideas on the critical importance of embodiment have emerged from biorobotic research. They note that physical constraints, by shaping the dynamics, not only can be used to obtain stability and efficiency, but also to induce regularities, such as time‐locked correlations in feedback from actions, that enhance more complex information processing such as learning. While a variety of new and yet to be developed technologies are needed to replicate the physical interface of animals to their environment, it is generally assumed that the internal neural processes connecting sensors to actuators can be adequately replicated with electronic computation. This may turn out not to be true. Perhaps there are explicit properties and capabilities that can only be obtained by chemically identical processes. In modeling, the internal processes, lower‐level properties (which are often not included in standard neural network computation) can turn out to be important. For example, song pattern recognition in bushcrickets can be elegantly accounted for by including subthreshold oscillation properties in a single model neuron (Webb et al., 2007). Again, attempts to replicate the behavior using an alternative medium (silicon vs neurons) should help us discover which properties of the substrate are in fact critical to reproduce.
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