Co-evolution of Sensors and Controllers
Komei Sugiura∗
Takayuki Shiose∗
Hiroshi Kawakami∗
Osamu Katai∗
Animal species have adapted their morphology (the placement of sensors and actuators, speciﬁc characteristics, etc.) to their ecological niche. However, so far very little is known about the relationship between
the morphology of an evolutionary system such as self-organizing robot and its adaptive behavior in theenvironment. In this paper we investigate the evolutionary development of embodied agents that are allowed
to evolve not only control mechanisms but also the sensitivity and temporal resolution of their sensors. The
experimental results indicate that the sensors and controller co-evolve in an agents through interacting withthe environments.
Keywords: sensor evolution, evolutionary robotics, embodiment, time resolution

Inspired by these biological facts, researchers have
tried to artiﬁcially evolve the morphology and behaviorof an agent in the ﬁeld of Artiﬁcial Life and Evolutionary
Robotics [18]. Sims and Lipson et al. realized a systemthat evolves the morphology and neural system of robots
inside a computer [13, 19]. Other research eﬀorts havebeen made on Sensor Evolution [1,5,12,14,15,20]. Kortmann et al. studied the evolution spatial and temporal
resolution in populations of simulated visuo-motor systems and showed that two factors are responsible for thetrade-oﬀ between spatial and temporal resolution [9–11].
In this paper, we investigated the evolution of thesensors and controller of embodied agents. We developed simulated sensory-motor systems that can change
the sensitivity and temporal resolution of their sensors.
Then, we carried out two kinds of experiments in which
populations of the sensory-motor systems perform tasks.
Finally, we describe how the physical characteristics ofagents aﬀect the diversiﬁcation of their sensors throughinteraction with the environment.

1. Introduction
Artiﬁcial Intelligence (AI) has exhibited its ability tosolve problems under very limited conditions such as
playing chess. In these cases, human programmers haveto design limited and digitized input information for theAI. Robots equipped with AI, however, are not capableof pick out meaningful inputs from the environment according to the problem to be solved. As a result of thissituation, the Frame Problem [6, 16] arises.
How do animals cope with this Frame Problem?
Uexküll insisted that animals have their own worlds (DieUmwelten) in which they are surrounded informationthat is meaningful to them [21]. Namely, animal species
have evolved their sensory systems to pick out information that is meaningful only to them, so that each ofthem ’sees’ the world diﬀerently.
Animals’ behavior and their Umwelten are closely related. The sensitivity and resolution of insects’ sensors
depend on their feeding behavior, mating behavior, velocity and so on [7]. For instance, the human visual system has high spatial resolution and low temporal resolution, whereas the ﬂy visual system has low spatialresolution and high temporal resolution. Critical Fusion Frequency (CFF) is an indicator of time resolution.
Generally, insects that move at high speed have a highCFF, while insects that move slowly have a low CFF.
The sensitivity and resolution of sensors diﬀer not only
among animal species but also in a single species. For example, the fovea/periphery in the visual systems of bothhumans and mantes have high/low spatial resolution. In
this case, the diversiﬁcation of the ability of sensors isdetermined by the animals’ physical characteristics.

2. Sensory-Motor System and Task Envi-ronments
We constructed sensory-motor systems that canchange sensitivity of the sensors, temporal resolution ofsensors and connection weights in their neural networkcontrollers. To investigate how embodied agents adapt
their sensors and controllers to the task environment,
we prepared two kinds of environments in which populations of sensory-motor systems perform diﬀerent tasks.
The individuals and environments were built using therobot simulator Webots† . Below we give the details of
the sensory-motor system and the task environments.
2.1 Sensory-Motor System
A top view of thesensory-motor system is shown in Fig. 1.

∗ Graduate School of Informatics, Kyoto University

Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japansugiura@sys.i.kyoto-u.ac.jp{shiose, kawakami, katai}@i.kyoto-u.ac.jp† http://www.cyberbotics.com

1Proximity Sensors: 1-8
2, 10

Left Wheel

Light Sensors: 9-16

1, 9

Right Wheel6, 148, 167, 15

Proximity Sensors

WheelsLight Sensors

Fig. 1. Left: Top view of the structure of the simulated sensory-motor system (agent). The system
is based on the Khepera robot; 28 mm in radius, and equipped with 8 infrared sensors used as bothproximity sensors (1-8) and light sensors (9-16). Right: Schematic view of Braitenberg’s Vehicle-likecontroller [2]. It has 16 input units, half of which receive information from the proximity sensors andthe other half receive information from the light sensors, with two output units controlling the speedof the wheels.

2.2 Task Environments
We carried out the following two kinds of experiments.
(I) Phototaxis
One individual is put in an arena (100 mm × 100 mm)
and performs a phototaxis task. Individuals acquire ﬁtness if they arrive at the circle in which a light source
exists (see Fig. 2). At each generation, an individual isgiven one trial. The position and direction of each individual is initialized randomly at the start of each trial.
A trial ends either when the individual arrives at thegoal or when 300 time steps (approximately 20 seconds
in real time) are performed.
(II) Predator-Prey Pursuit
Two species of agents (predator and prey) are used inthis experiment. Several researchers have investigatedpredator-prey co-evolution in both simulated [3, 4] andreal robotic experiments [8, 17]. Cliﬀ and Miller also
showed that task-dependent morphological parameters(view range) are evolved in co-evolving predator andprey agents [3, 4]. In this experiment, we investigateddependencies on the ecological niche and the sensitivity
and resolution of agents’ sensors.
The same arena as (I) is used, but there is nolight source. Instead, each predator and prey agent isequipped with a light source on it. They are also provided with sensors having the same range.
At each generation, all couples of randomly chosen
predator and prey agents perform tournaments. In eachtournament, both the predator and the prey are put randomly in the arena. Once the tournament has started, it
continues until the individuals collide or 300 time steps
are performed. We examined several cases in which theprey has diﬀerent maximum speed.

Sensors
This Khepera-like agent has eight infrared sensors thatare used as both proximity sensors and light sensors.
The two kinds of sensors are physically one uniﬁed device, but the simulator models it as two diﬀerent devices
by applying diﬀerent computing algorithms.
Each sensor has diﬀerent sensitivity and resolution
values. Here, the sensitivity si stands for a radix used
to digitize the ith sensor’s input. The bigger si becomes,
the more the sensor is able to detect slight changes.
The temporal resolution ∆ti stands for the intervalthat the ith sensor’s input is updated. Consequently,
the sensor cannot detect changes occurring within ∆ti .
Therefore, CFF is modeled by 1/∆ti .
We put a restriction on si and ∆ti in order to holdthe amount of acquired information I constant. Here, I
is deﬁned as information that an agent can acquire fromall of its sensor in a unit of time. si and ∆ti must fulﬁll
the following equation.
 si=I(1)∆tiController
The system is controlled by a simple neural network,
which has only inputs, outputs, and multiplying connections. The right-hand side ﬁgure of Fig. 1 shows aschematic view of the controller. The rotation of eachwheel is stimulated or inhibited in proportion to the normalized signal strength that each sensor senses. The
connection weights are encoded in the individual’s genotype, so they are ﬁxed during one generation.
Actuators
Wheels are driven by two independent motors. The
maximum angular speed ωmax and the maximum angular acceleration αmax can be varied. Agents with highωmax and αmax simulates light insects that move at highspeed.

3. Evolution of Sensors and Controllers
Genetic algorithms (GAs) are used in order to makesimulated embodied agents adapt both their sensor
2Co-evolution of Sensors and Controllers

100mm

100mm

Light source

PreyPredator

100mm

100mm

Fig. 2. Left: Schematic View of the arena used in (I). Agents are asked to perform phototactic behavior. Right: Prey evades from the predator in the arena. Sensors equipped on the two species havethe same range. A light source is also put on each agent.

properties and behaviors to their morphologies andtasks. The GA is allowed to optimize four kinds of parameters of each sensor: sensitivity si , time resolution∆ti , left wheel input multiplier li , and right wheel input
multiplier ri .

where t is the time to arrive at the goal and Tmax is themaximum time steps performed in the experiment.
For (II), We adopted a ﬁtness function similar to thatof [8]. Namely, both Φpr for a predator and Φpy for aprey are based on the time to contact:

3.1 Genetic Representation
The genotype of
an individual has the following form:Φpr = 1 −G = (s1 , t1 , l1 , r1 , s2 , t2 , l2 , r2 , . . . , s16 , t16 , l16 , r16 ),Φpy =where si and ti are the ratios of spatial and temporal
resolution. Each of the parameters (si , ti , li , ri ) is binary
encoded by four bits in the genotype. An individual has
16 sensors altogether, therefore the length of a genome
is 256 bits.
The weights (li , ri ) are decoded into real values ranging from −1 to 1. On the other hand, si and ∆ti arecalculated as follows to fulﬁll the equation (1): I
si = si   sj

4. Results and Discussion
4.1 Performance
We carried out experiments
in ﬁve populations for both (I) and (II). We kept I = 32
and αmax ﬁxed. The factor ωmax varied between thepopulations, but was kept ﬁxed within a populationin (I). In the experiments (II), we kept the predators’ωmax = 60, but varied the preys’ ωmax between the prey
populations.
In Fig. 3, the average ﬁtness of the individuals (left)
and the predators (right) is plotted against the number of generations. All populations converged within
300 generations in (I), while the predators’ ﬁtness values stay unsteady in (II).
Fig. 3 shows that the ﬁtness of the agents (ωmax =100) reaches lower values than that of the agents(ωmax = 20). This result can be explained by the factthat the range of the sensors is not enough for the agentswith high ωmax to avoid the wall.

tjI

3.2 Genetic Algorithm
Each genotype of anindividual in the ﬁrst generation is a random combination of 0 or 1. In each generation, the GA assesses theﬁtness of all individuals. Roulette selection is used to
select individuals that participate in mating. The GAthen generates new children from selected individuals toreplace the 10% worst individuals of the population.
Each population consists of 100 individuals in (I). In(II), 100 predators and 100 preys compose independent
populations. We made the cross-over rate 50% and theprobability of mutation per bit 0.002%.

4.2 Diversification of Sensors
We also studied the variation of the degree of dependence on eachsensor. Here, the degree of dependence on the ith sensor
is calculated as follows:
si3.3 Fitness Function
The ﬁtness function Φ forindividuals in (I) is calculated as follows:
Φ=1−tTmax

where t is the time the predator needs to contact theprey.

  s

j
 tj


∆ti = tit
Tmax

di = ∆ti
IIn Fig. 4, the average degree of dependence on eachsensor is plotted against the number of generations. ThetTmax
3

1

0.8

0.8

0.6

0.6Fitness

Fitness

10.4
ωmax = 100
ωmax = 80
ωmax = 60
ωmax = 40
ωmax = 200.2

0050

100

150
Generation

200

2500.4ωmax = 100
ωmax = 80
ωmax = 60
ωmax = 40
ωmax = 200.20300

050

100

150
200
Generation

250300Fig. 3. Left: Each curve represents the average ﬁtness of the ﬁve diﬀerent populations between whichωmax is varied. Right: Experiment on predator-prey pursuit problem showing the average ﬁtness ofthe predators’ performance. Each curve represents the average ﬁtness of the ﬁve diﬀerent populations
between which the preys’ ωmax is varied.

Fig. 4. The average degree of dependence on each sensor (S1-S16) in (I). Left: ωmax = 100. Right:ωmax = 20.

left-hand side ﬁgure shows that the agents (ωmax = 100)after 300 generations acquire information from bothproximity sensors and light sensors. On the other hand,the agents with low ωmax acquire almost all informationfrom one light sensor (see the left-hand side ﬁgure). This
result can be explained by the fact that agents with highωmax need more information from the proximity sensors
so as not to hit the wall.
In Fig. 5 and Fig. 6, average degree of dependence oneach sensor is plotted against the number of generations(Left: predators, Right: preys).
Fig. 5 shows that the predators concentrate attention
on two light sensors to catch the relatively quick preys(cf. Fig. 6). Fig. 3 and Fig. 5 show that the ﬁtness ofthe predators drastically increases with the increase in
the degree of dependence on the ﬁfteenth sensor. This
clearly indicate that both the sensor morphologies (sensitivity and resolution) and control mechanisms evolvedin these sensory-motor systems.

5. Conclusions
In this paper, we investigated the dependencies on thebehaviors and morphologies in populations of sensorymotor systems. Genetic algorithms (GAs) were used
to make simulated embodied agents adapt both their
sensor properties and behaviors to their morphologies
and tasks. We carried out two kinds of experiments inwhich agents were asked to perform phototactic and pursuit/evading behaviors. The experimental results showthat the physical characteristics of an agent and the task
environment aﬀect the sensitivities and the resolution ofits sensors, and thus task-dependent morphologies areevolved. Future work will include the evolution of sensor
morphology including placement, resolution, and sensitivity in agents performing tasks in noisy environments.

4Co-evolution of Sensors and Controllers

Fig. 5. The average degree of dependence on each sensor (S1-S16) in (II). The predators’ ωmax = 60,
and the preys’ ωmax = 100. Left: In the predators. Right: In the preys.

Fig. 6. The average degree of dependence on each sensor (S1-S16) in (II). The predators’ ωmax = 60,
and the preys’ ωmax = 20. Left: In the population of predators. Right: In the population of preys.

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