A CONCEPT LEARNING BASED APPROACH TO MOTION

CONTROL FOR HUMANOID ROBOTS

Kiyotake Kuwayama, Shohei Kato and Hidenori Itoh

Dept. of Intelligence and Computer Science, Nagoya Institute of Technology

Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan

Keywords:

Humanoid robot, learning-based motion control, concept learning.

Abstract:

This paper proposes a concept learning-based approach to motion control for humanoid robots. In this ap-

proach, the motion control system is implemented with

decision tree learner

for the acquisition of balancing

property of itself body and movement and

depth ﬁrst search technique

for the motion control based on the

knowledge concerning balance and stability in the motion. Some performance results by humanoid robot

HOAP-1 is reported: stable and anti-tumble motions to stand up from a chair. This paper also reports some

performance for the change in the environments; stand up from a chair on slope and different in height.

1 INTRODUCTION

Recently, the research of humanoid has been attract-

ing much attention in robotics. The latest research and

development brings several humanoid biped robots

in our lives (e.g., (Murase et al., 2001), (Kuroki

et al., 2002)). Sophisticated motion control tech-

niques for symmetric and cyclic motion, such as two-

legged locomotion (Taga, 1995), and asymmetric var-

ious movement of entire body, such as dance and

body exercise (Noritake et al., 2003), have been per-

formed by the humanoid robots. For these technolo-

gies, some learning-based approaches, such as rein-

forcement learning, neural network and so on, have

made a substantial contribution to motion control for

humanoids (e.g., (Morimoto and Doya, 2000), (Capi

et al., 2002) ). Reinforcement learning and neural

network approaches are, however, highly vulnerable

to a small change in the environments. The change

imposes re-learning, thereby making the motion con-

trol computationally very expensive. The advantage

of humanoids should be a diversity of motion because

of their link structure with high degree of freedom.

In this paper, we, thus, propose a concept learning

based motion generation system. The aim of this ap-

proach is to discover the knowledge for generating the

stable motion in balance. The system can generate a

stable and anti-tumble motion by the concept learn-

ing and the searching in the motion space: extract-

ing some generalized motion guideposts by decision

tree learner and motion generation with tracking the

guidepost by depth-ﬁrst search. The system attempt

Training

1 . Making training motions

2 . Executing training motions

3 . Making training data sets

training

data set

Learning

1 . Building decition trees

2 . Extracting guideposts

Generating

guideposts

attributes (posture, sensor value

and success or failure)

training motion

1 . motion generation

by depth first search

Figure 1: The outline of the system.

to reduce the limitation of a motion variation and ex-

ecutive environment.

2 THE LINK MODEL AND THE

MOTION STABILITY VALUES

Preliminary to the description of our system, we give

a link model of our humanoid robot and its motion

stability values.

A posture of a humanoid robot is uniquely decided

from joint angles and body gradient. At arbitrary time

t, posture of humanoid robot is uniquely determined

by its joint angles and its body gradient. A time series

of postures becomes a motion of the humanoid.

Motion stability value

s are criteria of motion stabil-

ity. These value are position data concerning state of

the balance, such as center of mass (COM) and zero

moment point (Vukobratovic et al., 1970) (ZM P ), or

sensor value, such as body accelerate sensor.

335

Kuwayama K., Kato S. and Itoh H. (2004).

A CONCEPT LEARNING BASED APPROACH TO MOTION CONTROL FOR HUMANOID ROBOTS.

In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics, pages 335-338

DOI: 10.5220/0001142903350338

 SciTePress

motion 1 :

motion :

. . .

Learning with C4.5

TIME :

. . .

Learning with C4.5

. . .

: an extracted guidepost

TIME :

COM ZMP

...

motion1

motion

0.039

0.026

0.045

2.302

TIME :

...

Result

success

failure

COM ZMP

...

motion1

motion

0.030

0.016

0.178

1.371

...

Result

success

failure

Figure 2: Training data sets for learning.

3 A CONCEPT

LEARNING-BASED MOTION

GENERATION SYSTEM

The section describes our motion generation system

for humanoid robots. This system can generate a

stable and anti-tumble motion which transforms the

robot from an initial posture into a target posture. Fig-

ure 1 shows the outline of the system. The system has

three parts:

Training

Learning

and

Generating

part.

Each part of the system is described below.

Training Part

Firstly, the system makes training data sets for learn-

ing part.

1. Preliminary to motion generation, lots of motions,

by which the robot moves between the initial and

the target posture, are made. These motions are

independent of feasibility consideration.

2. The robot, then, executes the motions. The motions

are, after that, classiﬁed into two groups:

positive

negative

examples, according to the feasibility

of the motions.

3. Each example has some attributes as the reason for

the classiﬁcation.

Motion Stability Values

are con-

sidered as the attributes.

Motion and sensor values are time-series data. In this

paper, the training data set is decomposed by time (see

Figure 2).

Learning Part

Secondly, the system extracts some guideposts for

stable and anti-tumble motion from training data sets

by a concept learning system C4.5 (Quinlan, 1993).

1. The system builds a decision tree from a training

data set by C4.5, which is generally considered to

: Failure

: Success

85%

15%

the highest accuracy

Figure 3: A model of decision tree by concept learning.

Initioal Posture

: subgoal

: final goal

Time

a generated motion

data structure

Figure 4: A model of search tree by our system.

be one of the best empirical decision tree learners.

It should be noticed that decision trees are built at

arbitrary time intervals.

2. The highest accuracy path from a root node to a

successful leaf is extracted as a guidepost from

each of the decision trees. One guidepost has some

conditions of the

Motion Stability Value

s for the

robot so as to execute the motion stably (see Fig-

ure 3).

Through the above procedure, guideposts are com-

posed in a time-series. A robot motion is generated by

successively tracking the guideposts as subgoal from

initial posture.

Generating Part

The system, ﬁnally, generates a motion by search in

the motion space.

1. Depth ﬁrst search generates sequences of joint an-

gles to transform the robot into the target posture.

In general for humanoid robots, the search space for

motion generation exponentially explodes because of

the large numbers of DOFs. In our system, search

space is reduced by tracking the guideposts. The

search tree generated by our system is intuitively il-

lustrated in Figure 4. A node of the search tree has

the data structure written on the right side of the ﬁg-

ure. A

subgoal

means an intermediate guidepost for

target posture.

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336

Figure 5: HOAP-1.

centor of mass

Figure 6: The link

structure for stand-

ing motion.

Table 1: Learning Results

COM

> −0.035 GP

COM

> 0.215

COM

<= −0.026 GP

COM

> 0.215

COM

> −0.056 GP

COM

> 0.233

COM

<= 0.018 GP

COM

= 0.008

COM

> 0.197 COM

= 0.237

4 EXPERIMENT

The section gives a performance of our system. The

target motion is to stand up from a chair.

4.1 Humanoid Robot

In this paper, we consider the motion control of a hu-

manoid robot, HOAP-1 (Humanoid for Open Archi-

tecture Platform) produced by Fujitsu(Murase et al.,

2001), shown in Figure 5. The total weight is 5.8 (kg)

and the height is 480 (mm). HOAP-1 has 20 DOFs in

total, 6 in each leg and 4 in each arm.

4.2 Standing Motion from a Chair

In this paper, we suppose that standing motion from

a chair is made by changing the servo motor of coxa,

guidepost ID 0 1 1 1

time (msec) 0 500 1000 1250

guidepost ID 1 5 FINAL

time (msec) 2000 3000 3250

Figure 7: The snapshot of a standing motion generated by

the system.

0.17

0.18

0.19

0.2

0.21

0.22

0.23

0.24

-0.08 -0.06 -0.04 -0.02 0 0.02 0.04

Figure 8: Trajectory of COM changing the height of a

chair.

knee and ankle joint. The motion is supposed to be

symmetric. The link structure is, thus, simpliﬁed to

three links model shown in Figure 6. A training mo-

tion is made by the linear interpolation at twice be-

tween a sitting posture and a middle posture and be-

tween a middle posture and a standing posture for

2000 (msec). The height of the chair on a ﬂat ﬂoor

is set 120 (mm). We have prepared 477 motions by

changing the middle posture. HOAP-1 has executed

these motions in advance to the motion generation,

and then the motions are classiﬁed into two groups:

success or failure. Horizontal and vertical compo-

nents of COM are given as the attributes of the mo-

tions. We have made 5 training data sets of 477 ex-

amples, and built 5 decision trees and 5 guideposts by

C4.5. Table 1 shows the guideposts for standing from

the chair. HOAP-1 stood up from a 120(mm) tall chair

on a ﬂat ﬂoor by tracking these guideposts.

We have made some experiments that the some

changes in the environment are imposed on the mo-

tion generation. In these experiments, it should be

noticed that each of the guideposts is the same with

the guidepost extracted from the above learning(see

Table 1); there is no re-learning, that is, these experi-

ments is to verify the admissibility of our system for

the changes in the environment. The results may indi-

cate how learned knowledge is generalized, and how

search control recovers the mistracking of guideposts.

Stand up from a Chair in Different Height

We have made some experiments for the stand up mo-

tion by changing the height of a chair. For a 100 (mm)

(i.e., lower than that for learning) tall chair, GP

and

3·4

were most effective for the motion generation.

For a 140 (mm) (i.e., higher than that for learning)

tall chair, GP

and GP

were most effective. Fig-

ure 8 shows the trajectory of COM of HOAP-1 when

it executes the motions obtained by search.

Stand up from a Chair on a Slope

We have made some experiments for the stand up mo-

tion from a chair on forward and backward slopes. In

A CONCEPT LEARNING BASED APPROACH TO MOTION CONTROL FOR HUMANOID ROBOTS

337

this particular case, the system can generate the stand

up motion with only GP

. Figure 9 shows the snap-

shots of a standing motion by our system, where the

gradient of ground is 10.0 (deg) backward. In this

particular case, the system can generate the stand up

motion with only GP

and GP

3·4

. Figure 10 shows

the trajectory of the gradient of HOAP-1 body when

it executes the motions obtained by search. In the ﬁg-

ure, solid, dashed and dotted lines show the trajectory

of the gradient of HOAP-1’s body standing up from

chair on the ﬂat, the forward slope and the backward

slope, respectively. The two broken lines, at the be-

ginning of the motion, show that the gradients of the

body on the slopes are both different from that on the

ﬂat. The difference corresponds to the gradient of the

slope. This is obvious, for HOAP-1 is sitting on a

chair on the slope. Through the movement, the differ-

ence of the gradient is attenuated gradually. The re-

sults indicates that the motion control adapts the mo-

tion to the different environments.

5 CONCLUSION

This paper proposed a concept learning-based ap-

proach to motion control for humanoid robots. The

motion generation system had been implemented with

decision tree learner C4.5 and depth ﬁrst search tech-

nique. Some stable and anti-tumble motions to stand

up from a chair were performed by humanoid robot

HOAP-1. In future work, we will dedicate to the im-

provement of our system for more complex motions

and to the investigation of the relations of the suitable

number between guideposts and examples for learn-

ing.

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guidepost ID 0 1 1 1

time (msec) 0 1000 2000 3000

guidepost ID 5 5 5 FINAL

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Figure 9: The snapshot of a standing motion generated by

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-10

-5

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

flat

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10.0 (deg) backward

Figure 10: Trajectory of gradient of the body changing gra-

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