ACQUISITION OF JUMPING BEHAVIOR ON THE ARTIFICIAL

CREATURE UNDER VIRTUAL PHYSICAL ENVIRONMENT

Yuta Umemura, Ikuo Suzuki, Masahito Yamamoto and Masashi Furukawa

Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan

Keywords: Artificial life, Physics modelling, Jumping, RCGA, Behavior simple, Behavior composed.

Abstract: Walking and jumping are very effective movement in a debris area. However, it is difficult to jump

successively because it has a lot of difficulties (e.g. controlling the strong power at taking off and

suppressing an impact at landing). This paper proposes how to acquire the successive jumping motion. We

model an artificial creature like a locust under the physical virtual environment and control it by using

Artificial Neural Network (ANN). In order to realize the successive jumping motion, this paper proposes a

concept of “Behavior Simple (BS)” and “Behavior Composed (BC)”. The concept of BC is that a complex

behavior is composed of plural simple behaviors. We consider that the successive jumping is divided into

three BSs, taking off, getting up and returning leg back motion. After three BSs are trained by using the

Real-Coded Genetic Algorithm (RCGA) independently, BC is trained by using RCGA as well. Experiments

verify that the efficient successive jumping can be acquired.

1 INTRODUCTION

A study on jumping motion has been applied to

various fields such as robotics (Fu et al., 2010),

biological analysis (Gronenberg, 1996) and so on.

Jumping is a complex motion because it has a lot

of difficulties (e.g. controlling strong power at

taking off, keeping correct posture and suppressing

an impact at landing) so that most of studies focus

on only a part of jumping motion. Sutton and

Burrows (2008) analyzed the mechanics of taking

off of the locust. (McKinley et al., 1983) and

(Nauwelaerts and Aerts, 2005) analyzed the power

of landing forces. However, it is required to build up

a more practical jumping model and a system

considering some difficulties in jumping.

We create the locust model as jumping model

under the virtual physical environment. It can jump,

walk and act various kinds of movement. This paper

proposes how to acquire the successive jumping

motion that capable of repeating big jumping.

We consider that the successive jumping motion

is divided into three simple behaviors, the kicking

ground, getting up from the overturning situation

and returning leg back motion. A concept of

"Behavior Simple” (BS) and “Behavior Composed”

(BC) (Furukawa et al., 2010) is applied to this idea.

This concept is that a complex behavior is realized

by switching plural simple behaviors. In this paper,

the successive jumping motion is BC and the

kicking ground, getting up and returning leg back

motion are BSs. Optimizing the system of switching

three BSs realizes the successive jumping motion as

BC (Figure 1).

The rest of this paper is constructed as follows.

Section two proposes the locust model and a simple

experiment. Section three proposes three

experiments to get three simple behaviors. Then,

section four proposes one experiment to get the

successive jumping motion. Finally, our work is

summarized in section five as conclusion.

Figure 1: The successive jumping motion is realized by

switching three BSs.

2 THE LOCUST MODEL

2.1 The Locust Model

We create the locust model under the virtual

physical environment by using PhysX. PhysX is a

physics motion engine developed by NVIDIA.

311

Umemura Y., Suzuki I., Yamamoto M. and Furukawa M..

ACQUISITION OF JUMPING BEHAVIOR ON THE ARTIFICIAL CREATURE UNDER VIRTUAL PHYSICAL ENVIRONMENT.

DOI: 10.5220/0003671903110314

In Proceedings of the International Conference on Evolutionary Computation Theory and Applications (ECTA-2011), pages 311-314

ISBN: 978-989-8425-83-6

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

The model consists of four parts, body, fore legs,

middle legs and back legs as shown in Figure 2. The

density of each part is 300 [kg/m

] as the locust has.

The actuator is implemented to joints between a

body and a femur and between a femur and a tibia at

each side of back legs. The model is controlled by 4

actuators and the angle range of that is shown in

Figure 3.

Figure 2: The locust model consists of a body, fore legs,

middle legs, and back legs.

Figure 3: Each actuator rotates within specified angles.

2.2 Rule-based Successive Jumping

Avoiding Overturning

One of the ideas to realize successive jumping is

repeating jumping motion with avoiding overturning.

This jumping motion is achieved by repeating the

kicking ground motion and kicking legs back motion.

Rule-based system is acquired by trial and error. In

this system, the time during which the tarsus touches

the ground is regarded as the touch time. We define

the change time as a certain time needed for

recovering the attitude. If the touch time reaches the

change time and the tarsus touches the ground, the

model acts by kicking the ground motion. The touch

time is reset when the tarsus leaves the ground and

until the touch time reaches the change time again,

the model acts by the kicking legs back motion.

Figure 4 shows the initial state of the model. It is

set on the origin of the coordinate system and its

heading is set along the x-axis.

Figure 4: The model turns its head to forward direction of

the x-axis of world axis at the initial state.

Figure 5 shows the trajectory of the successive

jumping motion by using appropriate rules found

empirically with the height of the model along the

vertical axis and a travel distance of the model along

the horizontal axis.

It is noticed that the rule-based successive

jumping is not satisfactory because as jumping is

repeated, the height and distance becomes small

(Figure 5). Actually, the posture of the model

becomes poor step by step because the model does

not have the recovery motion. In addition, once it

overturns, it cannot get up and jump any more. This

paper aims at acquiring the successive jumping

motion with big jumping and generalization.

Figure 5: The successive jumping motion is acquired by

repeating specified motion.

3 THE BEHAVIOR SIMPLE

To realize our purpose, it is not enough to control

the model with simple control system.

Then, Artificial Neural Network (ANN) is

employed to control all actuators. The model

acquires appropriate motion by optimizing synaptic

weights of ANN by Real-Coded Genetic Algorithm

(RCGA). The evaluation function is defined in each

experiment.

In this experiment, the ANN has nine units in the

input layer, 10 units in the hidden layer and four

units in the output layer. The following items

represent nine inputs.

 angles at four actuators I

[0, 2π]

 a touch sensor of back legs and the ground I

(off：1 or on：-1)

 1/(H+1) (H is the height of model) I

[0, 1]

 the inner product of the local axis (Figure 6) and

the world axis (Figure 4) I

[-1, 1]

Four outputs, O

[-10°, 10°] is displacement

angles every 1/120 [s] for each actuator.

In RCGA, we have 50 individuals as a

population. RCGA is terminated when the

generation number becomes 1000. As genetic

operations, elite preserving strategy, crossover and

mutation operations are used.

ECTA 2011 - International Conference on Evolutionary Computation Theory and Applications

312

Figure 6: The local axis of the model is defined.

3.1 Acquiring the Kicking Ground

Motion

The model acquires the kicking ground motion as

the taking off motion. In this experiment, the posture

of the model and the landing are not considered. The

initial state and position are shown in Figure 4. We

simulated 300 steps in each generation of RCGA for

evaluating the individual. 1 step is 1/120 [s] on

PhysX.

The fitness function is set as expressed in Eq.(1).

Maximizing E means maximizing a square area of a

rectangle consists of the initial point and the top

point on a single jumping.

xyE





max

(1)

The trajectory of acquired motion and rule-based

successive jumping motion is shown in Figure 7.

The height of the acquired motion is about twice as

high as the rule-based successive jumping motion.

Figure 7: The height of the kicking ground motion is about

twice as high as the rule-based successive jumping motion.

3.2 Acquiring the Getting Up Motion

The model acquires the getting up motion as a part

of recovery motion. At first, the vector U (Figure 8)

is defined which is one of the local axes (Figure 5)

and represents an upward direction of the model.

Five initial states are defined as the overturning

states. Those are rotated from one side position to

the opposite side position every 45 degrees (Figure

8). We simulated 1000 steps for each individual in

this experiment.

The fitness function is set as Eq.(2). Maximizing

leads the model to get up from the initial state as

soon as possible.

Figure 8: The vector U and 5 initial states are defined.

Figure 9 shows the one of the successful getting

up motion and the model gets up from all of initial

states.

Figure 9: The model gets up from plural overturning states.

()

5 1000

EUt

åå

(2)

The model gets up from not only 5 initial states but

also other initial states. We set 180 additional initial

states which are rotated from one side position to the

opposite side position in every 1 degree. The model

gets up from 65 additional initial states by acquired

motion. It verifies that acquired getting up motion is

effective to recover from overturning.

3.3 Acquiring the Returning Leg Back

Motion

The locust acquires the returning leg back motion as

a part of recovery motion. In experiment acquiring

the getting up motion, it is not evaluated about the

angle of each actuator. When the model gets up, it

may not be the appropriate angle for the next

jumping.

Figure 10: The model gradually bending each joint to the

jumping initial position.

ACQUISITION OF JUMPING BEHAVIOR ON THE ARTIFICIAL CREATURE UNDER VIRTUAL PHYSICAL

ENVIRONMENT

313

The model must learn the angle ready for the

next jumping.

We simulate 1000 steps and the initial position is

set as shown in Figure 10 (a). The fitness function is

set as shown in Eq.(3).

200 4

E jq

=- -

åå

(3)

Where, φis the initial angle of each actuator andθ

is the angle of each actuator at t step. The model

acquires the motion for bending each actuator to the

goal position (Figure 10 (e)).

4 THE BEHAVIOR COMPOSED

After the locust acquires three BSs, the switching

BSs system is optimized and it leads to acquire

successive jumping motion. A higher ranked ANN is

used as switching system. It consists of nine units in

the input layer, 10 units in the hidden layer and three

units in the output layer. Its nine inputs are the same

as those of BSs’ ANN. Its three outputs [0, 1]

correspond to three BSs’ ANN. One of the BSs’

ANN is selected which corresponding output has the

largest value among those of three.

We set 3000 steps for each individual. A higher

ranked ANN select one of BSs’ ANN in every 120

steps. We define the initial position as shown in

Figure 11 to make it easy to acquire the successive

jumping motion. The fitness function is set as Eq.(4).

Maximizing the accumulated heights of the model

leads to jump successively.

3000

(4)

Figure 12 shows the trajectory of the acquired

motion and rule-based successive jumping motion.

The lower part indicates the selected BS at that time.

The acquired motion is more than twice as high as

rule-based successive jumping motion at all jumps.

It verifies that the switching BSs system works well

and appropriate BSs’ ANN is selected at that time.

Figure 11: The initial state of acquiring the successive

jumping motion is one of the initial states of the getting up

motion.

Figure 12: Successive jumping acquired by BC and

selected BS.

5 CONCLUSIONS

We create the model under the virtual environment

as jumping model and use ANN as the controller.

Three BSs, the kicking ground, the getting up

and the returning leg back motion are acquired

respectively by RCGA. After that, the model acquire

the successive jumping motion as BC by

compositing three BSs.

More complex behavior which includes jumping,

walking and so on are acquired in a future work.

REFERENCES

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Design of a bionic Saltatorial Leg for Jumping Mini

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Masashi Furukawa, Makoto Morinaga, Ryosuke Ooe,

Michiko Watanabe, Ikuo Suzuki, and Masahito

Yamamoto. (2010). Behavior Composed for Artificial

Flying Creature. SCIS & ISIS.

W. Gronenberg. (1996). Fast actions in small animals:

springs and click mechanisms. J Comp Physiol A,

178(6), 727-734

P. A. McKinley, J. L. Smith, and R. J. Gregor. (1983).

Responses of elbow extensors to landing forces during

jump downs in cats. Experimental Brain Research,

49(2), 218-228.

S. Nauwelaerts and P. Aerts. (2006). Take-off and landing

forces in jumping frogs. Journal of experimental

biology, 209(1), 66.

G. P. Sutton and M. Burrows. (2008). The mechanics of

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