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Hideyuki Ryu, Yoshihiro Nakata, Yutaka Nakamura, Hiroshi Ishiguro, “Adaptive Whole-Body Dynamics:
An Actuator Network System for Orchestrating Multijoint Movements,” IEEE Robotics and Automation
Magazine, vol.23, no.3, Sept. 2016. DOI: 10.1109/MRA.2016.2582725

ROBOTICS AND AUTOMATION MAGAZINE, VOL. 14, NO. 8, AUGUST 2015

1

Adaptive Whole-body Dynamics:
An Actuator Network System for Orchestrating
Multi-joint Movements
Hideyuki Ryu, Yoshihiro Nakata Member, IEEE, Yutaka Nakamura, and Hiroshi Ishiguro, Member, IEEE

Abstract—Adaptive locomotion is crucial to enable robots thatmove across rough terrain and on diverse ground surfaces tocomplete assorted tasks. Recent advances in numerical computation allow such robots to operate in real environments using
a complicated control framework based on a precise model ofthe robot’s body and surroundings. However, it is not always
feasible to obtain a precise model because unexpected factors
related to both the robot itself and to surrounding objects affectthe robot’s motion. Enhancing the ability of robots to adapt tovarious environmental conditions would significantly expand therange of activities that can be completed by robots. In this paper,
we report the development of a legged robot that can change
its locomotion by switching connection patterns among cylinders
mounted on its legs. An actuator network system (ANS) is used toswitch mutually interconnected actuators, enhancing the robot’s
ability to adapt to its environment. Locomotion experiments wereconducted for the legged robot equipped with the ANS on fivedifferent ground materials. We confirmed that the dynamics ofthe legged robot was changed and the locomotion’s performance
could be improved with the ANS.

I. I NTRODUCTIONDAPTIVE locomotion is crucial for robots that moveacross rough terrain and variable ground surfaces tocomplete various tasks [1]. Recent advances in numericalcomputation allow robots to operate in real environments byusing complex control frameworks. They are based on precise
models of the robots’ bodies and surroundings. However, it
is not always feasible to obtain a precise model because
factors such as movement, position, shape, and stiffness of
the robot and surrounding objects affect the robot’s motion. In
particular, interaction with other autonomous systems, such as
humans, can result in significant disturbances in robot motion.
Therefore, robots should possess robust mechanisms to react to
unexpected forces from their surroundings [2], [3]. Examplesof them can be seen in nature. Animals can effectively react
to such disturbances thanks to their elaborate reaction mechanisms; e.g., rapid reflexes arise from the intrinsic mechanicaldynamics of the musculoskeletal system [4].
This ability to move within and adapt to various environmental conditions (e.g., navigating around obstacles and on uneven terrain) would greatly expand the range of robots’ activities. Therefore, the development of mechanisms for adaptive
robot locomotion is an important issue in robotics. Animals

A

H. Ryu, Y. Nakata, Y. Nakamura, and H. Ishiguro are with the Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, Japan, {ryu.hideyuki, nakata, nakamura,ishiguro}@irl.sys.es.osaka-u.ac.jp.

adapt to various environments using their sophisticated bodystructures acquired through the evolutionary process [4]. Many
researchers have developed robots that are capable of adaptive
locomotion by mimicking the well-designed structures ofanimals [5]. Robots designed using this approach have realized
high performance and energy efficient locomotion [6].
Figure 1 shows the classification of mechanisms that canbe employed in robot locomotion. We have classified thesemechanisms along three axes, as discussed below.
1) Precise knowledge of the robot and its surroundings isrequired.
2) The robot utilizes an adaptive controller (software) or
intrinsic mechanical dynamics (hardware).
3) The robot uses the constant or variable parameters of
the controller to generate locomotion.
In conventional control theories (e.g., model-based control),the robot is controlled through a precise model of its body andsurroundings [7]. The controller reliant on the precise model isoperated in a well-structured space such as a factory or in anexperimental space such as a laboratory. However, the robot
cannot adapt to environmental changes and events that are
not included in the model. Adaptive control frameworks (e.g.,
reinforcement learning) do not require knowledge of the target
system (model-free) [8]. These adaptive frameworks have beensuccessfully applied to the automatic control of simple agentsin structured environments; however, learning is not easily
adapted to diverse real environments.
Control methods using motion patterns have also been
widely studied [9], [10]. These methods are inspired bybiological control mechanisms employing neural circuits. Robust locomotion is realized by constructing attractors for theentire system including the controller. Recent advances in thisarea allow the diversity of the locomotion to adapt various
situations [11], [12].
Robots exploiting their own intrinsic mechanical dynamics
have also been reported [4]. Such a robot utilizes its own
physical dynamics, such as shape, stiffness, and material
properties [13]. A precise model and a rigorous computation
are not required for its controller. This approach is calledmorphological computation since the behavior is governedby morphology instead of a controller requiring numericalcomputation. From the viewpoint of adaptive locomotion,
morphological computations are advantageous because they
do not require quick and precise control from the software.
Thus, the robot can keep moving despite the presence ofsmall disturbances (e.g., rough ground surfaces) because theROBOTICS AND AUTOMATION MAGAZINE, VOL. 14, NO. 8, AUGUST 2015

Variable2

A Legged Robot
with an Actuator Network System

Robot with
Variable Stiﬀness
Self-assembly
Actuator [20]System [13]Design of Complex
Dynamic System [16]Adaptive Control Framework [8]Parametric
Dynamic Motor Primitive [12]Model-based

HardwareModel-Based Control [7]Constant

MorphologicalComputation [14], [15]Central Pattern Generator [9], [10], [11]Model-free

SoftwareFig. 1. Classification of mechanisms used to achieve robot locomotion.
Model-based–model-free axis: precise knowledge of the robot and its surroundings is required; software–hardware axis: utilizing software or hardware
for control; constant–variable axis: constant or variable controller parameters.

robot’s hardware is sufficiently flexible to absorb the effectsof these disturbances. Since locomotion can be realized with asimple controller by using the intrinsic mechanical dynamics
of the robot’s hardware, this approach seems to be a promising way to achieve adaptive locomotion. To realize energy
efficiency and stable locomotion, mechanical parameters suchas the spring constant of the curved beam and the weight of
the rotating mass should be determined in advance [14]. In
Corucci’s study, the morphological parameters of an octopus
robot were adjusted using a genetic algorithm method [15].
The parameters were tuned in the simulation environment
before developing the hardware. Thus, it was difficult for the
robot to adapt to various, and non-stationary real environmentswith fixed hardware dynamics. To solve this problem, we
propose a mechanism that allows the robot to adapt to various
environments by changing the intrinsic mechanical dynamics
of its hardware (variable morphological computation) during
its operation. In simulation studies, a design method forcomplex dynamic systems [16] and self-assembly systems
utilizing variable shapes [13] was proposed. In contrast, ourapproach employs a mechanical system to modulate the physical dynamics of a real robot. In this approach, the robot canchange its hardware dynamics in the same manner that thesoftware parameters can be tuned in response to environmental
changes.
In this study, passive, fluid-mediated interactions between
hydraulic cylinders are used to change the robot’s hardware
dynamics. The authors propose an actuator network system (ANS) [17] composed of multiple cylinders connectedby many valves. The ANS can generate various responses
by switching the connections between cylinders using thosevalves. As a result, the robot with the ANS can modify its
physical interactions with the environment. Such a mutuallyinterconnected hydraulic cylinder mechanism has been usedpreviously in a master–slave robot system [18] and a bodypowered exoskeleton [19]. These studies have focused on thetransmission of force via fluid. In contrast, this study develops
a mechanism to change the passive mechanical response of

Fig. 2. The legged robot.

the locomotive robot depending on external forces. A variable
response of a single robot joint can be realized using a variable
stiffness actuator [20]. The proposed ANS, on the other hand,allows the interactions among multiple joints to be adjusted.
According to the system, adaptive locomotion based on morphological computation is achieved by adjusting the various
mechanical dynamics in response to environmental changes
even in the absence of changes in hardware composition.
This paper describes the development and performance
evaluation of a legged robot implemented ANS (Fig. 2).
The walking distances of the robot with and without mutualinterconnection between the fluid actuators are compared forfive different ground surfaces. Based on the results, we discuss
the feasibility of adaptive robot locomotion using variablemorphological computation.
The rest of the paper is structured as follows. Section
II presents the structure and locomotion mechanism of thedeveloped legged robot and explains the proposed actuator
network system. Section III presents the experimental results
for robot locomotion under different conditions of mutually interconnected actuators and different floor materials. Based onthese results, this section also evaluates the robot performance.
In addition, the feasibility of adaptive robot locomotion using
ANS is discussed. Section IV concludes this paper.
II. L EGGED ROBOTIn this section, the mutually interconnected mechanism
among actuators and the ANS concept are introduced, anda legged robot developed based on this concept is presented.
A. Actuator Network System
Figures 3(a) shows the typical motion of two separate
cylinders. In contrast, Fig. 3(b) shows one of mutually interconnected cylinders. In Fig. 3(a), when a force is applied tothe cylinder c′ , it is moved by the external force. On the other
hand, in Fig. 3(b), when a force is applied to the cylinder, thecylinder c advances due to the force transmission via fluid. As
a result, the joints j and j ′ move simultaneously. Therefore,

ROBOTICS AND AUTOMATION MAGAZINE, VOL. 14, NO. 8, AUGUST 2015

3

c1
Air tube
c
j

c2

c'
j'

c3

B

A

b1

a1

b2

a2

Reservoir
Valve
Cylindera3(a)Reservoir

lc1
(a)

l c2

(b)

Fig. 3. Comparison of joint motion in response to external force. Two
cylinders are mounted on each joint. (a) The two cylinders are separated.
(b) The two cylinders are mutually interconnected.

the joints’ motions in response to the external force change
when the connections between the cylinders mounted on the
joints are switched, and the robot can adjust its mechanicaldynamics.
The proposed ANS framework is explained as follows.
Figure 4(a) shows a typical fluid flow diagram of the ANS. The
proposed ANS includes two groups of valves: A and B. If thevalves in group A and B are closed, the cylinders maintaina constant advancing length. For example, by closing both
valves a1 and a2 , cylinders c1 and c2 maintain a constantadvancing length (Fig. 4(b)). The advancing length of eachcylinder can be adjusted by opining the corresponding valve
in group A. However, if the valves in group B are opened while
those in group A are closed, the external force applied to one
cylinder is transmitted to the other (Fig. 4(c)). For example,
by opening valve b1 when valves a1 and a2 are closed, theexternal force applied to one cylinder can be transmitted to theother via fluid. This process corresponds to the functionalityof the multiarticular muscle, which connects multiple joints totransmit forces from one joint to another. Consequently, theinteraction between actuators can be modified by opening or
closing valves (i.e., switching the actuator network).
B. Structure of the Legged Robot
We developed the legged robot’s locomotion mechanism
by referencing a hopping robot developed in a previous
morphological computational study [14], as shown in Fig. 5(a).
This robot is composed of a body, two legs, and a swinging
mass (Fig. 5(b)). Four hydraulic cylinders and six valves aremounted on the legs, and the swinging mass is moved by aservomotor.
1) Leg: The leg mechanism of the robot has two degrees of
freedom, as shown in Fig. 5(c). This mechanism is based on
the Scott Russell linkage, which is driven by two cylinders.

Reservoir

l c1

lc2

(b)

(c)

Fig. 4. (a) Fluid flow diagram of the ANS for adaptive robot motion. (b) A
schematic of the cylinders without mutual interconnection. In this condition,lc1 and lc2 are constant. (c) A schematic of the cylinders with mutualinterconnection. In this condition, lc1 + lc2 = Const.. lc1 and lc2 are theadvancing lengths of cylinders C1 and C2 , respectively.
Servomotor

Motor
Elastic beam
Swinging
mass
M1

M2

Rotating mass

Front leg Back leg

Base

Reservoir

(a)

(b)

Link 1

Front leg
C1

V1

Back leg
C2

V2

C3

C4
V3

CylinderLink 2V4
V5

V6Moving range

Reservoir

(c)

(d)

Fig. 5. (a) A hopping robot developed in a previous study on morphologicalcomputation [14]. (b) The legged robot with the ANS developed in this study.
(c) Leg mechanism of the robot, which has two degrees of freedom and wasbased on the Scott Russell linkage. (d) ANS implemented in the robot.

A guide slot that constrains the motion of a shaft reduces
the bending moment that is applied directly to this shaft.
These properties improve the mechanical rigidity of the leg
mechanism. The lengths of links 1 and 2 are 156 and 48
mm, respectively, and the motion range of the cylinder is 20
mm. Note that the legs are passive during locomotion in thefollowing experiment that is, no energy is supplied to cylinders

ROBOTICS AND AUTOMATION MAGAZINE, VOL. 14, NO. 8, AUGUST 20154

III. L OCOMOTION E XPERIMENT
Swinging mass

Servomotor

Front
θm
M 2M 1

M1

M2

θbBack legThis experiment investigated the effect of the switching
hardware dynamics on the robot’s locomotion. The motionof the robot was analyzed while it walked for a designatedamount of time by swinging a mass on its body. For simplicity,
manual valves were used instead of electronically controlledvalves to change the connection among hydraulic cylinders,
and the connection pattern was fixed during locomotion.

Front leg

A. Experimental settings
Fig. 6. Swing angle θm and rotation angle θb of the robot.
Front leg
C1

V1

V2

Back leg
C2

C3

C4
V3

V4
V5

V6
Reservoir(a)
Front leg
C1

V1

V2

Back leg
C2

C3

C4
V3

V4
V5

V6
Reservoir(b)
Fig. 7. (a) Fluid flow diagram of the legged robot under condition with mutualinterconnection. (b) Fluid flow diagram of the legged robot under condition
without mutual interconnection.

from outside the system.
2) Body structure: As shown in Fig. 5(b), two leg mechanisms are symmetrically mounted on the robot body. A T
shaped foot is mounted at the end of each leg to prevent the
robot from falling down to the side. The total weight of therobot is 1,012 g.
3) Swinging mass: A mass of 82.6 g on the robot isswung through a radius of 140mm by a servomotor (LEGO
MINDSTORMS EV3 Large Servo Motor). This is the soleenergy source for generating the locomotion. The height ofthe center of the swing from the ground is 408 mm. The
frequency of the swing is 1 Hz, and its angle is denoted by θm(Fig. 6). The angle of the swing in the vertically downward
direction is 0◦ and that in the frontal direction of the robot
is positive. The maximum and minimum angles of the swing
are 128◦ and -148◦ , respectively. The servo controller (LEGO
MINDSTORMS EV3 Intelligent Brick) is located away fromrobot.
4) Valve system: The fluid flow diagram of the ANS
implemented in the legged robot is shown in Fig 5(d). The
details of switching connection patterns are described in thenext section.

In this experiment, two types of actuator network patterns
were compared. In the first pattern, valves V5 and V6 wereclosed, whereas valves V1 , V2 , V3 , and V4 were opened. In
this setup, cylinder C1 was mutually interconnected to C2 andC3 was mutually interconnected to C4 . The cylinders’ motions
were constrained so that the total advancing length of the twocylinders remained constant (20 mm). This locomotion withmutual interconnection condition is referred to as W/ (Fig.
7(a)). In contrast, in the second pattern, all valves were closed;thus the motions of all cylinders were fixed. This condition isreferred to as W/O (Fig. 7(b)).
In each trial, the robot was held up and positioned in its
initial posture. The initial posture was set to the gravitationallystable posture wherein the body leans backward (Fig. 8(a)).
The advancing lengths of C1 , C2 , C3 , and C4 were 16 mm,
4 mm, 0 mm, and 20 mm, respectively. Specific valves in
conditions W/ or W/O were closed, and the robot was thenplaced at the starting position. The robot’s mass was swung
eight times. Where the initial moving direction of the mass
was opposite of the frontal direction of the robot.
Trials were performed on five ground materials (Fig. 9(a)):
A, B, C, D, and E, which were comprised of: flooring(A88.6/S), carpet (N/A), vinyl chloride resin sheet (A71.1/S),
polyethylene foam mattress (N/A), and urethane sponge (N/A),
respectively. The above numbers in parenthesis indicate hardness values measured using a durometer (KR-14A, KORI
SEIKI MFG.CO.,LTD) in compliance with ISO7619. The
hardnesses of materials B, D and E were not available becauseof the property of the material surface such as surface shape.
We employed a motion capture system (Trio, NaturalPoint,
Inc. DBA OptiTrack) to determine the robot’s motion. Reflective markers were attached to the robot, as shown in Fig. 5(b).
The position of the robot was defined by the middle point of
two markers, M1 and M2 . The rotation of the robot θb wasdefined by the angle between the line M1 M2 and horizontal
plane (Fig. 6).
B. Results
Figures 8(b) and (c) shows the superimposed stroboscopic
photographs of the robot and the temporal changes in the
instantaneous moving distance and rotation of the robot during
a typical trial, respectively. In every trial, the robot moved
with periodic motion, and the motion began a maximum of
two seconds after the mass started to swing. Although thefrequency of the mass swinging is 1 Hz, the temporal changes
in the instantaneous moving distance and the rotation of therobot include multiple frequency components.

ROBOTICS AND AUTOMATION MAGAZINE, VOL. 14, NO. 8, AUGUST 2015

Front

Start
End

(a)

(b)
Instantaneous moving distance
Rotation of the robot
Rotation of the mass

Distance [mm]

8

2

6

0

4

−2

2

−4

0

−6
3.0

3.5

4.0

4.5

5.0

5.5

Rotation [°]1 period of swinging mass

4

−2
6.0

Time [s](c)Fig. 8. (a) The initial posture of the legged robot. (b) Superimposed
stroboscopic photographs of the legged robot. (c) Temporal changes in the
instantaneous moving distance and rotation of the robot during a typical trial.

The results of the locomotion trials under different groundmaterial conditions are summarized in Fig. 9. Figure 9(b)shows that the traveled distances under condition W/ are longer
than those under condition W/O on ground type A, B, and
C. In contrast, the results are opposite on ground types D
and E. Under all ground conditions, the traveled distances
for conditions W/ and W/O were statistically, significantly
different (p<0.001) due to the t-test performance. Figures9(c) and (d) show the instantaneous moving distance androtation angle of the robot during a 1-sec period (1 cycle)
under conditions W/ and W/O, respectively. In these plots,
the starting time is the moment at which θm changed frompositive to negative. The median (thick solid line), 75% point(thin solid line), and 25% point (thin solid line) for the 3rd-7th
cycles of all trials (25 trials) are also plotted.
Figure 9(e) shows the power spectrum of the frequencyof rotation angle change. The first peak appears at 1 Hz,
corresponding to the swinging mass, and stronger peaks appear
at integral multiples of the base frequency. Under every
condition, the peak at 3 Hz is the strongest. Figure 9(f) shows
the proportion of the power of each frequency.
C. Discussions
The experimental results show that the robot can change its
behavior by changing the connection pattern. The traveling distance depends not only on the physical properties of the ground5

but also on the connection pattern. This demonstrates us that an
adequate choice of connection patterns allows the robot to exploit the energy generated by the swinging mass. The different
patterns can change the physical interaction between the robot
and its environment. That is, by developing a method to selecta connection pattern according to the environment, more efficient locomotion can be realized. Because the proposed robotic
system (or ANS) may be adaptable to diverse environments
without any changes to the hardware of the robot, this systemrepresents an extension of the adaptive locomotion generation
framework utilizing the physical properties of the robot (i.e.,a robot with morphological computation).
A method must be developed to select an appropriate
connection pattern for the environment. Figure 9(e) suggeststhat the larger the power at 3Hz, the faster the locomotion,
regardless of ground conditions. The investigation of therelationships among some intrinsic physical properties of therobot (e.g., resonance frequency and gait pattern) will be a
subject of future work.

Flooring

Carpet

2

50

100

W/O

W/O

W/

200

250

300

350

(b)

Traveled distance [mm]

150

W/

W/O

W/

W/O

W/

W/O

W/

400

450

*

*

*

*

0.8

0.8

0.2

0.4

0.6

0.8

0.4

0.6

0.8

−6
0.0

Condition W/(c)Time [sec]

0

−4
1.0

−2

2

0
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−4

4

6

−4
1.0
8

−2

2

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−4

0.2

2
0

4

2
0

* −2

6

4

−4
1.0
8

−2
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−2
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2

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2
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6

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2
0

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0.0
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6

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−4
1.0
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−2
0.8

0

−2
−4
0.6

2

0.4

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2
0

0.2

6

−6
0.0
6

8

6
4

0.8

0.8

0.8

−6
0.0

−4

−2

0

2

4

0.6

Time [sec]

0.4

0.8

Condition W/O
(d)

0.2

−4
1.0

−2

0

2

4

6

−4
1
1.0
8

−2
0.6

0

−2
−4
0.4

2

0.2

4

2
0

−6
0.0
6

6

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−4
1.0
8

−2
0.6

0

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2
0

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0.0
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1.0
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−2
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−2
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2
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−4
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0.6

2

0.4

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2
0

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−6
0.0
6

8

6
4

Instantaneous moving distance
Rotation of the robot

8

10

400

0

6

600

200

4

800

600

0

200

400

4

6

8

8

(e)

Frequency [Hz]

2

6

10

10

14.3

0

W/O
200
0
20

13.2

12.6

22.1

21.5

9.4 7.9

W/

400

600

800

1000

0

12.9
4

200
2

W/O

400
0

7.1
600

W/
200

0

800

10
1000

8

6.4

22.6

20.3

15.7

19.7

W/O

4.9

W/

6.6

W/O

6.1

W/

10.0

W/O

600

6

W/
6.7 9.5

400

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Condition W/
Condition W/O

Condition W/O amplitude
Condition W/ amplitude

60

59.2

(f)

11.1

12.6

3.6

6.0

14.9

80

10.6

7.3

9.4

100

5.6 7.7

17.1

15.6

19.7

24.5

23.5

20.8

21.4

21.7

17.8

6.5Hz-8.5Hz
8.5Hz-10.5Hz

14.4

Proportion [%]

40

55.0

51.7

41.7

32.8

37.5

38.9

39.5

48.9

60.1

0.5Hz-2.5Hz
2.5Hz-4.5Hz
4.5Hz-6.5Hz

Fig. 9. Experimental results for robot locomotion on five ground types. (a) Photographs of the different ground materials. Since material A is a room flooring, its thickness is not measurable. (b) Comparison of
the traveled distances under conditions W/ and W/O. (c) Instantaneous moving distance and rotation of the robot under condition W/. (d) Instantaneous moving distance and rotation of the robot under condition
W/O. (e) Power spectrum of the frequency of the rotation angle change. (f) Proportion of the power of each frequency.

(a)Thickness 41mm

1

Urethane spongeE

Thickness 7mm

3

4

5

6

7

8

9

10

Polyethylene
foam mattressD

Thickness 7mm

Vinyl chloride
resin sheet

C

Thickness 7mm

B

Thickness N/AA* p<0.001
m oving dist ance[ m m ]m oving dist ance[ m m ]moving dist ance[mm]
Distance[mm]
m oving dist ance[ m m ]m oving dist ance[ m m ]m oving dist ance[ m m ]robot 's post ure[ degree]

robot 's post ure[ degree]m oving dist ance[ m m ]moving dist ance[mm]
Distance[mm]

robot 's post ure[ degree]robot's post ure[ [°]degree]Rotationm oving dist ance[ m m ]m oving dist ance[ m m ]robot 's post ure[ degree]

robot 's post ure[ degree]
robot 's post ure[ degree]robot's post ure[ [°]degree]Rotationrobot 's post ure[ degree]
robot 's post ure[ degree]

Instantaneous moving distance
Rotation of the robot

ROBOTICS AND AUTOMATION MAGAZINE, VOL. 14, NO. 8, AUGUST 2015
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ROBOTICS AND AUTOMATION MAGAZINE, VOL. 14, NO. 8, AUGUST 2015

IV. C ONCLUDING REMARKSIn this paper, we reported the development of a legged
robot that can change its locomotion by switching connection
patterns among cylinders mounted on the legs. The mechanism
used to switch the mutually interconnected actuators (i.e.,
ANS) expands the robot’s ability to adapt to its environment
using morphological computation. Locomotion experiments
were performed on five ground materials. We confirmed thatthe locomotion of the legged robot was changed by switching
the connection among cylinders, and the appropriate connection pattern enabled efficient locomotion on each groundmaterial.
In the locomotion experiments, hand valves were used tochange the connection among cylinders. The robot can switch
connection patterns during its locomotion using electronicallycontrolled valves. In future work, we will develop a legged
robot that can automatically switch the connection dependingon the properties of the ground surface. Increasing the numberof degrees of freedom is important to expand the space inwhich the robot can move. For example, an ANS-equipped
robot with three legs spread in a radial fashion is expected torealize locomotion on a 2D plane.
By connecting multiple actuators using the ANS, the motionof joints driven by the connected actuators is restricted basedon connection patterns. Multiarticular muscles in animals have
similar functionality and are used to manipulate animals’
responses to external forces; that is, switching the connection
patterns of the ANS corresponds to morphological changes in
the muscular systems of animals (musculoskeletal systems).
This suggests that the ANS might allow the robot to adapt
to highly diverse environments; that is, the ANS may be
a mechanism that allows the realization of variable morphological computation. Biological systems have only simple
connection patterns in which successive joints are connected;
in contrast, arbitrary connection patterns can be constructed by
the ANS. This advantage is expected to allow ANS-equipped
robots to adapt to various environmental changes by drasticallychanging their physical properties.
Through evolution, animals have developed elaborate bodystructures that can realize efficient locomotion. For ANSequipped robots, it is necessary to switch a connection patternbased on the environment to realize efficient locomotion, andthe learning mechanism of the switching rule is an importantconsideration. In principle, it is possible to construct an ANS
system in which all connection patterns can be realized;
however, that is not feasible from the viewpoints of hardware
implementation and software optimization. Thus, it is important to develop an adaptive robotic system in which a diversityof behaviors can be realized by a simple network (hardware)with a few switching rules (software). For example, by using a
network with star topology (i.e., each cylinder is connected to areservoir (central hub) with a point to point connection througha valve), the connection between an arbitrary pair of cylinders
can be realized with small number of valves compared to byusing fully meshed network. To develop such a structured ANS
is our future work.

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ACKNOWLEDGMENTS
This work was partly supported by JSPS KAKENHI(#25220004 and #26700026).
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