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What kind of floor am I standing on?
Floor surface identification by a small humanoid robot through fullbody motions
Reo Matsumuraa, b*, Masahiro Shiomia, Takahiro Miyashitaa, Hiroshi
Ishiguroa, c and Norihiro Hagitaaa

ATR-IRC, Kyoto, Japan; bTokyo Univ., Tokyo, Japan; cOsaka Univ., Osaka, Japan

Reo Matsumura received M.Eng. and Ph.D. degrees in engineering from Osaka University in2008 and 2012. From 2008 to 2012, he was an intern researcher at the Intelligent Robotics andCommunication Laboratories (IRC) and is currently a cooperative researcher at IRC, a project
researcher at Tokyo University, and an president at karakuri products inc. in Tokyo. His
research interests include the development of communication robots and their control theory.
Masahiro Shiomi received M. Eng. and Ph.D. degrees in engineering from Osaka University in2004 and 2007. From 2004 to 2007, he was an intern researcher at the Intelligent Robotics andCommunication Laboratories (IRC). He is currently a group leader in the Agent Interaction
Design department at IRC, Advanced Telecommunications Research Institute International(ATR). His research interests include human-robot interaction, robotics for child-care,
networked robots, and field trials.
Takahiro Miyashita is a group leader and a senior research scientist at Intelligent Robotics andCommunication Laboratories (IRC), Advanced Telecommunications Research InstituteInternational (ATR) since 2007. He is also a director of research planning section at Social
Media Research Laboratory Group, ATR since 2011. He received his B.S., M.S., and Ph.D.
degree in engineering for computer-controlled machinery from Osaka University, Japan in 1993,
1995, and 2002, respectively. From 1998 to 2000, he was a research fellow of the Japan Society
for the Promotion of Science (JSPS). From April to September 2000, he was researcher ofSymbiotic Intelligent Group at ERATO Kitano Symbiotic Systems Project of Japan Science andTechnology Agency (JST). From October 2000 to June 2002, he was assistant professor at
Wakayama University. From July 2002, he was a researcher of IRC, ATR. His researchinterests are ambient intelligence, sensor network, tactile sensing, tele-robotics, human
computer interaction, human robot interaction, human robot symbiosis and cloud networked
robotics.

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He is a member of the Robotics Society of Japan, the Japan Society of Mechanical Engineers
and the Japanese Society for Artificial Intelligence.
Hiroshi Ishiguro received a D.Eng. in systems engineering from the Osaka University, Japan in
1991. He is currently professor of Department of Systems Innovation in the Graduate School ofEngineering Science at Osaka University (2009-) and Distinguished Professor of Osaka
University (2013-). He is also visiting Director (2014-) (group leader: 2002-2013) of Hiroshi
Ishiguro Laboratories at the Advanced Telecommunications Research Institute and the ATR
fellow. He was previously Research Associate (1992-1994) in the Graduate School ofEngineering Science at Osaka University and Associate Professor (1998-2000) in the
Department of Social Informatics at Kyoto University. He was also Visiting Scholar (19981999) at the University of California, San Diego, USA. He was Associate Professor (20002001) and Professor (2001-2002) in the Department of Computer and Communication Sciences
at Wakayama University. He then moved to Department of Adaptive Machine Systems in the
Graduate School of Engineering at Osaka University as a Professor (2002-2009). His researchinterests include distributed sensor systems, interactive robotics, and android science. Especially,
his android studies are well-known very much in the world. He has received the Osaka Cultural
Award in 2011.
Norihiro Hagita received B.S., M.S., and Ph.D. degrees in electrical engineering from Keio
University, Tsuruoka City, Japan in 1976, 1978, and 1986. From 1978 to 2001, he was with the
Nippon Telegraph and Telephone Corporation (NTT). He joined the Advanced
Telecommunications Research Institute International (ATR) in 2001 to establish the ATRMedia Information Science Laboratories and the ATR Intelligent Robotics and Communication
Laboratories in 2002. His current research interests include communication robots, networked
robot systems, interaction media, and pattern recognition. He is a fellow of the Institute ofElectronics, Information, and Communication Engineers, Japan as well as a member of the
Robotics Society of Japan, the Information Processing Society of Japan, and The JapaneseSociety for Artificial Intelligence. He is also a co-chair for the IEEE Technical Committee on
Networked Robots.

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What kind of floor am I standing on?
Floor surface identification through full-body motions of a small
humanoid robot
This study addresses a floor identification method for small humanoid robots thatwork in such daily environments as homes. The fundamental difficulty lays in amethod to understand the physical properties of floors. To achieve floor
identification with small humanoid robots, we used inertial sensors that can beeasily installed on such robots and dynamically selected a full-body motion thatphysically senses floors to achieve accurate floor identification. We collected a
training data set over ten different kinds of common floors in home environments.
We achieved 85.7% precision with our proposed method. We also demonstratethat our robot could appropriately change its locomotion behaviours dependingon the floor identification results.
Keywords: floor identification, small humanoid robot

1. Introduction
Sensing diversity achieves robust recognition functions for robots in various
environments. Many types of sensors exist for robots, such as depth sensors, laser range
finders, RGB cameras, and microphone arrays. One typical example that shows theimportance of sensing diversity is Kinect, with which researchers can easily use depth
information. Such inexpensive depth sensors dynamically increase the sensing ability ofmany robots. Combinations of existing and cheap depth sensors enable high-quality
sensing systems. Therefore, we believe that increases in sensing diversity willcontribute to advances in robotics research fields.
Sensing through physical interaction is one unique approach to increase the sensing
diversity of robots. This approach is often used for small robots, such as pet and hobby
types, because physical interaction with them is an essential mode of communication.

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For example, researchers found that physical interaction with robots is useful in therapy
and for enjoyment [1, 2]. Related to understanding such physical interactions between
small robots and people, researchers developed sensing mechanisms to recognize
human gestures [3, 4], full-body gestures [5], attitudes toward a robot [6], and aperson’s identification [7].
However, unlike understanding the physical interactions between small robots andpeople, few related works have addressed understanding environments for interaction
with robots [8-10]. Since these works were only conducted with wheel-type robots, thekinds of physical interaction between robots and environments are such simple uses as atactile probe or changes of forward/rotation speeds during locomotion. No research has
focused on understanding the physical interaction between environments and such nonwheel-type robots as humanoid or multi-legged robots that can physically interact withvarious environments. We note that humanoid robots need to select appropriate walking
motions depending on the floor surface; different from wheel-type robot, it is difficult tosense floor surfaces through locomotion before understanding floor surfaces for small
humanoid robots, which did not have enough sensing capabilities. In other words,
increasing of a sensing diversity such as floor surface identification would contribute torealize appropriate walking motion selections for such small humanoid robots.

tFig. 1 Floor surface identification through physical interaction

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In this paper, to increase the sensing diversity of small robots, we propose a method that
identifies an environment’s property, i.e., its floor surface, by the full-body motions of asmall humanoid robot (Figure 1). We though the number of unique points of this paper
is three: 1) a use of a small humanoid robot to identify floor surfaces. Past researchworks which used humanoid robots to identify floor surfaces are limited to large
humanoid robot with high sensing capabilities (details are described in next section).
However, we tackled floor identification with only inertia sensors by focusing on
physical interaction with the environments; this approach can be applied to other kindsof small humanoid robots.2) Physical interactions with environments through full-body
motions. Different from large humanoid robots, small humanoid robots can easilyinteract with environment through full-body motions. Such interactions enable robots togather features from sensors without locomotion motions; to begin with, humanoid
robots could note use appropriate walking motions if a floor surface was unknown. 3)
Iteratively narrowed down floor surface candidates using different kinds of full-body
motions. This idea is inspired by human sensing actions by physical interactions; forexample, a person can identify floor surfaces without visual information by different
kinds of physical interactions, such as stamping or stroking.
The rest of our paper is structured as follows. Section II describes related work, andSection III introduces our proposed method that uses inertial sensors to identify floor
surfaces through full-body motions. Section IV presents our experimental methods, andSection V presents the results. Section VI provides a discussion, and Section VIIsummarizes the contributions.

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2. Related work
Much research has measured environment properties using visual or distanceinformation [11-13]. For example, Kuroki et al. proposed a method that identifies thesurfaces where a robot can walk by stereo camera systems [14]. Okada et al. also
developed a system that identifies flat floor spaces using a stereo camera system [15].
Hasegawa et al. used a small number of laser range finders with mirrors to find objects
on a floor surface [16], and Yokoya et al. used multiple robots to efficiently construct
3D maps of an environment including its floors [17]. These research works enablerobots to accurately measure floor properties.
From the viewpoint of increasing a small robot’s sensing diversity, several
researchers focused on physical interaction with environments to measure theirproperties. For example, DuPont et al. identified floor surfaces using theforward/rotation changes of a wheel-type robot [8]. Giguere installed a simple tactile
probe on Roomba to identify various floor surfaces in a home environment [18]. They
mainly focused on identifying floor surfaces because they are directly related to thelocomotion planning of small robots.
However, different from our study, these research works which focused on
physical interaction with environments to identify floor surfaces are limited to wheeltype robots. Such non-wheel-type robots as humanoid or multi-legged robots can
execute more complex motions than wheel-type robots, and with those motions, gather
various kinds of sensor data to accurately identify floor surfaces. For example, people
can more accurately identify floor surfaces using such full-body motions as stamping
their feet than driving over a floor by car or riding over it by bicycle. We propose amethod to identify floor surfaces through such physical interactions as full-body
motions with environments for non-wheel-type robots such as a humanoid robot; thisThe final publication is available at
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concept is also a unique point of the paper, which focused on physical interactions
between a small humanoid robot and environments.

3. Identification of interaction floor
3.1. Architecture
We propose a method to identify the floor surface on which a robot is standing byphasing the reductions of candidates, i.e., iteratively eliminating floor surfaces from apool of candidates. Fig. 2 shows an overview of our implemented system with inertial
sensory information. It selects an appropriate motion to gather the sensor data used toreduce the set of candidates (Section 3.2) and calculates the floor surface features by atime series of the inertial sensor data gathered by the selected motion (Section 3.3).
These features identify floor surface candidates by a decision tree classifier that
includes combinations of the current candidates and reduces the set of candidates(Section 3.4). The system finally identifies the floor surface on which the robot isstanding by repeating these winnowing processes until only candidate remains.
Note that our proposed method assumes that only one floor surface exists while walking,and the data from the physical interaction were gathered beforehand.

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Inertial sensors

Motion selection
Feature extraction
Candidate identification

Reduction of candidates
No

Candidates = 1
Yes
Identification of floor surface

Fig. 2 Overview of proposed method

3.2. Motion selection
Our proposed method selects a motion to gather sensor data to identify floor surfaces
from candidates. In this paper, we prepared four kinds of full-body motions that aredesigned to physically interact with an environment: bending and stretching, lying down,
tossing and turning, and stamping (Figure 3).
The reasons of why we chosen these four motions are as follows. Basically we referred
to behaviours of people, which are used to investigate floor properties without handswith preliminary testing with participants; we decided two motions, bending andstretching and stamping from observations of their behaviours. Moreover, we also
focused on behaviours of people on beds. In such situations, we can estimate properties

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of beds and bedclothes via whole-body motions such as lying or tossing and turning.
Different from large-sized robots, small-sized robot can easily conduct such behaviours;
therefore we added these behaviours as full-body motions for the robot.
To identify the interacting floor surface, the system selects an appropriate full-body
motion from above four motions (i.e., bending and stretching, lying down, tossing andturning, and stamping) to gather sensor data by considering the classifier reliability (thedetails will be described Section 3.4). At the first physical interaction with anenvironment, the candidates include all floor surfaces therefore a motion which has
maximum classifier reliability towards the combinations of all floor surfaces is used togather sensor data. After reducing the candidates, the system again selects anappropriate motion which has maximum classifier reliability towards the remaining
candidates to gather suitable data to identify the interacting floor surface.

(a) bending and stretchingThe final publication is available at
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Fig. 3 full-body motions

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3.3. Feature extraction
In this research, we calculated the first and second derivatives of the time series sensor
data. We simply employed the two features of the sensor data of both time series, i.e.,the mean and standard deviation to three kinds of sensor data: raw data, and the first andsecond derivatives of the time series of sensor data. They are defined as:

1 tn
   s (t )
n t1

(1),

1 tn
s(t )   2
n  1 t1(2),

where s(t) is the inertial sensor data (raw data, first derivative data, and second
derivative data) at times t, and n are the amount of data in the time period. We used six
kinds of features f (r,d1,d2,r,d1,d2) from each sensor to identify the floor surface.

3.4. Candidate identification and reduction of candidates
We applied a C4.5 decision tree classifier [19], which is widely used in robotics, tocorrectly identify the candidate that is interacting with the robot at each time step. Thisalgorithm builds decision trees from training data set, based on the concept of
information entropy. Based on the normalized information gain, the decision threes
have node to split the classes from the training data. Each node automatically selects
extracted features which are appropriate to split the classes, therefore this method isuseful for a situation when it is still unknown, which features are appropriate for

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classification. In our method, mean and standard deviation of each sensor data are usedas features to build decision trees.
We prepared multiple decision trees that considered all of the combinations among thecandidates in advance of their phased reductions during the voting process. Eachdecision tree includes at least two classes, and thus the number of decision trees iscalculated by this function:
Nc1

NumOfTree  Mn  Mn  C ( Nc, x )

(3),

x 2

where Mn is the number of the full-body motion Nc is the number of classes, i.e., thenumber of candidates, and C(Nc, x) is the number of possible combinations of x from a
set of Nc.
In our proposed method, the system stores the results from a decision tree using theextracted features to reduce the candidates. The candidate at each time step is calculatedas

Candidate (t )  decision ( RemainingC andidates , f ( r , d 1, d 2 ,  r ,  d 1,  d 2 ))(4),
where decision is a function that outputs a candidate using a decision tree classifier that
includes the RemainingCandidates. In the first process, RemainingCandidates includes
all candidates: RemainingCandidates = {A, B … Nc}.
Next, the system narrows down the floor surface candidates based on theirreliability. The reliability of each candidate is calculated from a confusion matrix of thedecision tree classifier. For example, the confusion matrix of the decision tree classifier
shown in Table 1, reliability of “floor A” is 80% and “floor B” is 20 % when thedecision tree classifier’s result was “floor A.”
Table 1 Confusion matrix example
A
80
40

B
20 floor A
60 floor B

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If a reliability is lower than a threshold, the candidate is removed in the next
calculation process. The threshold value is defined asTh  1 /( NumOfCandidates   )(5),where NumOfCandidate is the current number of candidates and is a coefficient for
the threshold calculation to increase the speed of convergence. The rest of thecandidates are defined as

RemainingC andidate (6),{candidate  CurrentCandidate | ( Ratio (candidate )  Th)}
where CurrentCandidate is the set of the current candidates.
If RemainingCandidate includes more than two candidates, the system repeats
the above processes by selecting a motion to gather sensor data until only one candidate
remains. To select a motion for the next calculation, the system considers the values of
the average recognition ratio toward RemainingCandidate and its standard deviation
calculated by each classifier. For example, in a case of table 1, average recognition ratio
is 70% and standard deviation is 14.14. The system selects the motions with the largest
average recognition ratio towards RemainingCandidate for identification (if the numberof selected classifiers exceeds 2, it uses the minimal standard deviation from them). We
note that at the first interaction between the robot and the environments, the systemselects a full-body motion with the largest average recognition ratio of all the floor
surface candidates.
We describe an example case where the system identifies a ceramic tile from ten
candidates. Firstly the robot selected "stamping" motion to gather sensor data towards
the combinations of all floor surfaces because it has maximum classifier reliability forten candidates. The robot extracted features from sensor data through the stamping
motion. Through these processes, RemainingCandidate is reduced to two kinds: ceramic
tile and tatami. The robot again selected the stamping motion to gather sensor data

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towards the combinations of ceramic tile and tatami because this motion also has
maximum classifier reliability for the candidates. Finally, the robot identifies the floor
surfaces as "ceramic tile."

4. Experiment methods
To investigate our proposed method’s performance, we collected data when a small
humanoid robot physically interacts with various floor surfaces.

4.1. Robot hardware
For the data collection, we used a small human-like robot called Robovie-MA (Fig. 4)that has two arms (4 DOFs for each), a body, a waist (2 DOF), and two legs (6 DOF foreach). To record the inertial sensor data during the interaction, we attached two 2-axis
acceleration sensors (ADXL202E, ANALOG DEVICES Inc.) to its body (Fig. 4). The
sensor arrangements are designed to record 3-axes (X, Y, and Z) using two inertial
sensors with 60-Hz sampling rates (Fig. 4). Therefore, the system calculated 18 featuresfrom the physical interactions between the robot and the environments. We adopted thesimple moving average method as a kind of low-pass filter to reduce the noise of thesensor data. In this work, we used three steps data to calculate the average values.

2-axis acc. sensor
1

Y

X
Z

2-axis acc. sensor
2

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Fig. 4 Robovie-MA
4.2. Data collection procedures
For the data collection, we used ten kinds of floor surfaces found in homeenvironments: ceramic tiles, linoleum, wood, tatami mats (a type of mat used as aflooring material in traditional Japanese-style rooms), cushions, bedding, carpets, bathmats, blankets, and artificial turf. We installed these floor surfaces to a flat place asshown in figure 5.
The robot executed for four kinds of full body motions on each floor (described in
Section 3.2) to gather the inertial sensor data 60 times. Finally, we gathered a data set
comprised of 2400 sensor data (60 times x 10 floors x 4 motions). Figure 6 and 7
shows acceleration data when the robot used the full-body motions on the bathmat andthe robot used the stamping motion on the different surfaces; as shown in the figures,properties of sensor data are different due to the floor surfaces; for example, frequencies
and maximum values of XYZ data are different between the floor surfaces. These
differences are essential to identify floor surfaces in our method; therefore proposed
method selects an appropriate full-body motion to gather sensor data to identify
interacting floor surfaces.

(a) ceramic tile(b) linoleum(c) wood(d) tatami mat

The final publication is available at
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Fig. 5 Floor surfaces for data collection

LSB

x

y

z

40
30
20
10
0
-10
-20
-30
0

100

200
300
Timestamp(a) bending and stretching400

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LSB

x

y

z

40
20
0
-20
-40
-60
-80
0

100
200
Timestamp

300(b) lying down

x

y

z

150

LSB

100
50
0
-50
0

50

100
150
Timestamp(c) tossing and turning

200

The final publication is available at
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LSB

x

y

z

20
15
10
5
0
-5
-10
-15
-20
-25
0

50

100
Timestep

150(d) stamping
Fig. 6 Sensor data with stamping motion at different floor surfaces. The unit of Y axis is
LSB value of acceleration sensor, the range is 128 (+2G) to -127(-2G).

x

y

z

30

LSB

20
10
0
-10

-20
0

50

100
Timestamp(a) ceramic tile

150

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LSB

x

y

z

20
15
10
5
0
-5
-10
-15
0

50

100
Timestep

150(b) futon
Fig. 7 Sensor data with stamping motion at different floor surfaces. The unit of Y axis is
LSB value of acceleration sensor, the range is 128 (+2G) to -127 (-2G).

4.3. Experimental design and data analysis
In the experiment, we measure performance of our floor surface identification though
off-line process. For this purpose, we conducted a 5-fold cross-validation for gathereddata. We used a 1920 data set (48 times x 10 floors x 4 motions) to construct decision
tree classifiers (these processes can be done before the experiment but not in real time)and a 480 data set (12 times x 10 floors x 4 motions) to test the identification in eachvalidation.
At the first step, the system selects an appropriate full-body motion to gather
inertial sensor data toward the candidate which includes all floor surfaces.

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Inertial sensor data was replayed by the collected data, and the system reduces thecandidate floor surfaces based on the sensor data. Then, the system again selects anappropriate full-body motion to gather inertial sensor data towards the remaining
candidates until identifying the candidate. The maximum number of calculation cycles
is 12; if the system failed to reduce the number of floor surface candidates to one during
the 12 calculations, it selects the floor surface with the maximum ratio from thecandidates as an identification result. We determined the coefficient of in Eq. (6) tobe 2.5. These parameters are based on heuristic tuning with the test data.
In this experiment, we prepared four alternative methods to investigate theeffectiveness of our proposed method. These methods only used one full-body motion
to gather the sensor data, unlike the proposed method that selected full-body motions togather sensor data that depend on the floor surface candidates. We compared thealternative methods to reveal the effectiveness of selecting full-body motions to identify
floor surfaces.

5. Results
5.1 Performance evaluation
Table 2 shows the performance of each method (Precision, Recall and F-measure) and
Figure 8 shows the results of the floor surface identification. Table 3 shows a confusion
matrix with the proposed method, which achieved 85.7% floor surface identification
through physical interactions; the alternative methods achieved 80.7%, 65.8%, 46.5%,
and 73.8% precisions. In the proposed method, wrong identification mainly occurred for
relatively hard floors such as ceramic tile, linoleum and tatami mat. On the other hand, there
are good performances for soft floors such as cushions and blanket.

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Table 2 Average success ratio of methods

Proposed method
Bending and stretching only (a)Lying down only (b)Tossing and turning only (c)Stamping only (d)Precision Recall
F-measure
85.7
87.4
86.0
80.7
82.2
80.5
65.8
67.0
65.8
46.5
47.6
45.8
73.8
82.1
73.5

Table 3 Confusion matrix of the proposed method
a b c d e f
g h i
j
44 1 13 0 0 2 0 0 0 0
4 43 13 0 0 0 0 0 0 0
0 11 47 0 0 0 2 0 0 0
1 0 3 55 0 1 0 0 0 0
0 0 0 0 60 0 0 0 0 0
11 0 9 0 0 40 0 0 0 0
0 2 1 2 0 0 55 0 0 0
0 0 0 0 2 0 0 58 0 0
0 0 0 0 0 0 0 0 60 0
0 0 0 0 3 0 3 0 2 52

a = ceramic tileb = linoleumc = woodd = carpete = cushions
f = tatami mat
g = bath mat
h = bedding
i = blanket
j = artificial turf

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Fig. 8 Identification rate of each floor

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5.2 Frequency of use of motions
Table 4 shows the ratio of the full-body motions in our proposed method. The ratios ofbending and stretching and stamping are relatively higher than the other full-body
motions, but all of them were selected as appropriate motions for floor surface
identification during the calculations. For example, "Tossing and turning" showed
higher reliabilities towards specific combinations of candidates, such as "Cushion" and"Blanket" than other full-body motions. In other words, this motion was appropriate to
identify the floor surfaces from the combinations of soft floor surfaces. These results
showed that mixing of various motions contribute to increase the performance of the
proposed method in total.
Table 4 Ratio of full-body motions

Bending and stretching
Lying down
Tossing and turning
Stamping

Usage rate
32.9
19.0
9.2
38.9

6. Discussion
6.1 Applications
The identification of floor surfaces can be used to change/modify a robot’s motion. In
particular, the locomotion planning of such non-wheel-type robots as a humanoid robot
is strongly affected by the floor surfaces. Our proposed method will help such robots
move in home environments that include various floor surfaces.
Next we show an example application of our proposed method for a functionthat enables a small humanoid robot to use suitable movements depending on the floor
surfaces. Fig 9(a) shows a robot walking on bedding. Since its motion is not stable, it
falls down. After that, the robot used a stamping motion to gather sensor data to identify

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the floor surface (Fig. 9(b)); after correctly identifying the floor surface, it used acrawling motion to move on it; the robot walked over the bedding (Fig.9(c)).

1

2

3

4

5

6

(a) robot falling down on bedding

1

2

3

4

5

6

(b) robot uses stamping motion to gather sensor data

1

2

3

4

5

6

(c) robot uses crawling motion to move on this floor surface
Fig. 9 Examples of locomotion behaviors on beddingThe final publication is available at
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Such applications can be realized by referring to past research that identified
floor surfaces. However, as noted in the introduction, the unique point of our proposed
method is increasing the sensing diversity for small robots. Unlike typical methods, itdoes not need any visual or distance information to identify floor surfaces. For example,
our proposed method can identify floor surfaces under complete darkness or those thateasily change their form due to the robot’s weight, such as cushions.
Combinations of our proposed method and past research will increase thesensing capability of small robots. Our proposed method enables greater robust sensing
systems by collaborating with previous work.

6.2 Scalability for number of candidates
We evaluated our proposed method with data from ten floor surfaces using four fullbody motions. If the number of floor surfaces increases, our proposed method should be
able to identify them, although it might require more interaction with the floor surfaces
using more full-body motions. With more targets, since similar floor surfaces will also
increase, more interaction with the floor surfaces is needed for identification.

6.3 Scalability for number of candidates
In total we gathered each 60 sensor data set for each floor (10 floors) with each fullbody motion (4 motions). To confirm the validity of the number of trials, weinvestigated differences between the data set. Figure 10 shows the plot data by using thefirst/second principal components. Circles represent data set gathered by each full-body
motion (a= bending and stretching, b=lying down, c= tossing and turning, d= stamping).
We note that contribution rate of them is 0.55, and the ratio of between-class variance

The final publication is available at
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and within-class variance was 5.70. Figure 11 showed a part of floor data sets: futon andwood (a) and artificial turf and linoleum (b). As shown in the figures, the data sets
gathered by each full-body motion at each floor are crowded; the differences of data
sets within each floor are smaller than the differences of them between each floor.
Therefore, we thought that the gathered data in the experiment would be enough toevaluate the performance of the proposed method, by considering the variances ofgathered data sets.

Floor

2nd Principle Component

artificial turf

ceramic tile(b)tatami mat
wood

(c)

bedding(a)linoleumcushioncarpet(d)blanketbathmat

1st Principle Component
Figure 10 Plotted data with principle components

The final publication is available at
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Wood

Artificial turf(a)

2nd Principle Component

2nd Principle Component

Futon

1st Principle Component(a) Wood and futon

Linoleum(c)

1st Principle Component(b) Artificial turf and linoleum

Figure 11 Plotted data of specific floor combinations

6.4 Limitations
Our proposed method identified floor surfaces through physical interactions with 85.7%
accuracy, but we did not compare its performances with other state-of-the-art timeseries classification algorithms, such as Support Vector Machine [20], an existing
method that uses inertia sensors to identify environments [18], and an approach that
discriminates different textures using whisker sensors [21-23].
Our method can only identify one floor surface because it assumes that a small
robot interacts with just a single floor surface at a time. Moreover, winnowing
candidates requires observation of a full-body motion within a certain time to find thedifferences between floor surfaces. We also evaluated our proposed method with one
small humanoid robot with a specific sensor arrangement. If our proposed method isused with different kinds of robots, the parameters must be calibrated and the sensor
positions adjusted.

The final publication is available at
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Surface identification was conducted using an existing data set. Therefore, aregistration process is needed beforehand, such as gathering physical interaction datawith floor surfaces. Such processes are also needed for other types of floor surface
identifications, such as vision-based systems, but our proposed method needs more time
for registration than these vision-based methods.
In this research work, we used a specific humanoid robot and inertial sensors. It
would be difficult to share the same decision trees between different kinds of robots; wethought that our method can be applied to other kinds of humanoid robots with different
sensors by rebuilding decision trees by using them. We note that one concern of our
method is for a humanoid robot which is covered by soft materials. If the robot wholebody is covered by sponge or silicon skins, inertial sensor data might become moresimilar between different kinds of floor surfaces.

7. Conclusion
We proposed a method that identifies floor surfaces through the physical interaction of asmall robot. Our work’s unique concept is to increase the sensing diversity for nonwheel-type small robots such as a humanoid robot; it focuses on the differences in theextracted features from the inertial sensor data during physical interaction to identify thefloor surface on which the robot is standing. Our proposed method extracts the featuresfrom the inertial sensor data history and narrows down the floor surface candidates
through physical interaction. We evaluated its performance, and our evaluation results
showed that it identified floor surfaces through physical interactions with 85.7%
precision. We believe these results demonstrated the concept that physical interaction
with an environment is effective to increase the sensing diversity for robots by dealing
with floor surface identification problem. Accurate floor surface identification would

The final publication is available at
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contribute motion planning for robots, in particular small humanoid robots, which workon daily environments where various floor surfaces exist.

Acknowledgements: This research was supported by the Strategic Information andCommunications R&D Promotion Programme (SCOPE), Ministry of Internal Affairs andCommunications (132107010).

References:[1] K. Wada and T. Shibata, “Living With Seal Robots-Its Sociopsychological andPhysiological Influences on the Elderly at a Care House,” IEEE Transactions on
Robotics, vol. 23, No. 5, pp. 972-980, 2007.
[2] M. D. Cooney, T. Kanda, A. Alissandrakis, and H. Ishiguro, “Interaction Design for
an Enjoyable Play Interaction with a Small Humanoid Robot,” Humanoids 2011, pp.
112--119, 2011.
[3] T. Salter, F. Michaud, K. Dautenhahn, D. Létourneau, and S. Caron, “Recognizing
interaction from a robot’s perspective,” Proceedings IEEE International Workshop
on Robot and Human Interactive Communication, pp. 178-183, 2005
[4] T. Salter, F. Michaud, D. Letourneau, D. C. Lee, and I. P. Werry, “Using
Proprioceptive Sensors for Categorizing. Human-Robot Interactions,” 2nd
ACM/IEEE International. Conference on Human-Robot Interaction, 2007.
[5] M. D. Cooney, C. Becker-Asano, T. Kanda, A. Alissandrakis, and H. Ishiguro,“Full-body Gesture Recognition Using Inertial Sensors for Playful Interaction with
Small Humanoid Robot,” IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS2010), 2010.
[6] D. François, D. Polani, and K. Dautenhahn, “On-line behaviour classification andadaptation to human-robot interaction styles,” Proceedings of the ACMIEEEInternational Conference on Human Robot Interaction, pp. 295-302, 2007.
[7] R Matsumura, M. Shiomi, T. Miyashita, H. Ishiguro, and N. Hagita, “Who isinteracting with me? Identification of an interacting person through playfulinteraction with a small robot,” IEEE Transactions on Human-Machine Systems, pp.
1-11, 2014.

The final publication is available at
http://www.tandfonline.com/doi/abs/10.1080/01691864.2014.996601?journalCode=tadr20[8] E. M. DuPont, G. C. A. Moore, E. G. Collins Jr, E Coyle, “Frequency response
method for terrain classification in autonomous ground vehicles,” Autonomous
Robots Volume 24, Number 4, 337-347, DOI: 10.1007/s10514-007-9077-0[9] E. Coyle, E. G. Collins, , and L. Liang, “Updating control modes based on terrain
classification,” 2010 IEEE International Conference on Robotics and Automation(ICRA), 4417-4423, 2010[10]

I. Halatci, C. A. Brooks, and K. Iagnemma, “A study of visual and tactile terrain

classification and classifier fusion for planetary exploration rovers,” Journal
Robotica Volume 26 Issue 6, November 2008, 767-779[11]

S. Kagami, K. Nishiwaki, J. Kuffner, K. Okada, Y. Kuniyoshi, M. Inaba, and H.

Inoue, “Low-Level Autonomy of the Humanoid Robots H6 and H7,” Robotics
Research, pp. 83-98, 2003.
[12]

W. Yin-Tien, L. Ming-Chun, and J. Rung-Chi, “Visual SLAM and Moving-

object Detection for a Small-size Humanoid Robot,” International Journal ofAdvanced Robotic Systems, Vol. 7, No. 2, 2010.
[13]

D. Maier, M. Bennewitz, and C. Stachnis, “Self-supervised obstacle detection

for humanoid navigation using monocular vision and sparse laser data,” IEEE
International Conference on Robotics and Automation (ICRA), pp. 1256-1262.2011.
[14]

Y. Kuroki, M. Fujita, T. Ishida, K. Nagasaka, and J. Yamaguchi, “A Small

Biped Entertainment Robot Exploring Attractive Applications,” Proc. of the 2003
IEEE International Conference on Robotics and Automation, pp. 471-476, 2003.
[15]

K. Okada, M. Inaba, and H. Inoue, “Walking Navigation System of Humanoid

Robot using Stereo Vision based Floor Recognition and Path Planning with MultiLayered Body Image,” In Proc of IEEE/RSJ International Conference on Intelligent
Robots and System (IROS‘ 03), pp. 2155-2160, 2003.
[16]

Y. Nohara, T. Hasegawa, and K. Murakami, “Floor sensing system using laser

range finder and mirror for localizing daily life commodities,” IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS), 2010 pp 10301035, 2010[17]

T. Yokoya, Tsutomu Hasegawa, and Ryo Kurazume, “Autonomously generatinga 3D map of unknown environment by using mobile robots equipped with LRF,”
Proceedings of the 2009 international conference on Robotics and biomimetics(ROBIO'09), pp. 269-274, 2009.

The final publication is available at
http://www.tandfonline.com/doi/abs/10.1080/01691864.2014.996601?journalCode=tadr20[18]

P. Giguere and G. Dudek, “A Simple Tactile Probe for Surface Identification by

Mobile Robots,” IEEE Transactions on Robotics, pp. 534-544, 2011.
[19]

J. R, Quinlan, “C4.5 Programs for Machine Learning,” Morgan Kaufman, 1993

Kendon A., Conducting Interaction: Patterns of Behavior in Focused Encounters.
Cambridge University Press; 1990.
[20]

V. Vapnik., “The Nature of Statistical Learning Theory,” Springer, 1995.

[21]

M. Fend, S. Bovet, H. Yokoi, and R. Pfeifer, “An active artificial whisker arrayfor texture discrimination,” IEEE/RSJ International Conf. on Intelligent Robots andSystems (IROS’03), vol. 2, pp. 1044-1049, 2003.
[22]

M. Fend, “Whisker-Based Texture Discrimination on a Mobile Robot,”

ADVANCES IN ARTIFICIAL LIFE, Vol. 3630, pp. 302-311, 2005.
[23]

A. K. Seth, J. L. McKinstry, G. M. Edelman, and J. L. Krichmar,” Texture

discrimination by an autonomous mobile brain-based device with whiskers,” IEEE
International Conference on Robotics and Automation (ICRA’04), Vol. 5, pp. 49254930, 2004.