The 2010 IEEE/RSJ International Conference onIntelligent Robots and Systems
October 18-22, 2010, Taipei, Taiwan

Active Learning of Conﬁdence Measure Function
in Robot Language Acquisition Framework
Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, and Satoshi Nakamura
Abstract— In an object manipulation dialogue, a robot may
misunderstand an ambiguous command from a user, such as“Place the cup down (on the table),” potentially resulting in an
accident. Although making conﬁrmation questions before all
motion will decrease the risk of this failure, the user will ﬁnd it
more convenient if conﬁrmation questions are not made undertrivial situations. This paper proposes a method for estimating
ambiguity in the commands by introducing an active learning
framework with Bayesian logistic regression to human-robot
spoken dialogue. We conducted physical experiments in which
a user and a manipulator-based robot communicated in spoken
language to manipulate toys.

I. I NTRODUCTION
The needs of an aging society have raised the hopeof robots supporting humans in everyday situations. For
these assistive robots, the functional capability of naturalcommunication with users is crucial. However, the state-ofthe-art techniques used in current dialogue systems are wellshort of satisfactory.
It is not an easy task for robots using these techniques
to choose the appropriate action to take, such as moving
toward a cupboard or reaching for a cup on a table uponhearing the command “Bring me a cup.” Homes can have
many candidate cups within them and the speciﬁc cup thatneeds to be handed over to the user differs depending on
the situation. For example, it could pertain to either a meal
being prepared or one being cleared away.
For practical reasons, most dialogue management mechanisms adopted for service robots process verbal (user’sutterances) and nonverbal (e.g., vision, motion and context)
information separately. With these mechanisms, neither thesituation nor previous experiences are taken into accountwhen a robot processes an utterance, so there is a possibility
that it will execute motions that the user had not imagined.
In this study, we deﬁne “motion failure” as occurring whena robot has executed an undesirable motion because of arecognition error.
The goal of this study is to decrease the risk of motion failure. To focus on the relationship between language
understanding and motion, we do not deal with cases inwhich a user’s command is successfully recognized but
the executed motion has ended up with undesirable consequences. Consider a case in which a robot has successfullyrecognized the command “Pick the object,” but the robot
failed to grasp the speciﬁed object when trying to pick it up.
Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, and Satoshi Nakamura are with the National Institute of Information and Communications Technology, 3-5 Hikaridai, Seika, Soraku, Kyoto 619-0289, Japan.

komei.sugiura@nict.go.jp

978-1-4244-6676-4/10/$25.00 ©2010 IEEE

This failure is not deﬁned as “motion failure” in this paper,
since our major concern is the relationship between language
understanding and motion. Although a problem like this is
obviously important in terms of dexterous manipulation, it
is beyond the scope of this paper. We also assume that
communication between the robot and user is not conducted
via text interfaces but via speech. The advantage of a speechinterface is remarkable, especially when a hands-free device
is needed since the user himself is manipulating multiple
objects.
A simple solution to decrease the risk of motion failure
is to make conﬁrmation utterances before motion execution,such as “You said ‘Bring me the cup.’ Is this correct?” However, there are two main hurdles to generating conﬁrmationutterances: when to conﬁrm and how to conﬁrm.
The problem of when to conﬁrm is a decision-making
problem of whether a conﬁrmation utterance should be madeor not. Although making conﬁrmation utterances before all
motion executions would be simple and effective, it would
be more convenient for the user that conﬁrmation questions
are not made under trivial situations. In the ﬁeld of spoken dialogue systems, the when-to-conﬁrm problem receives
considerable attention in the context of error handling [1],[2].
The problem of how to conﬁrm is the problem of paraphrasing user’s commands. The sentence “Bring me a cup”
is ambiguous when there are multiple cups, and asking a
conﬁrmation question such as “Do you mean the blue cup?”
can disambiguate the sentence. Moreover, when direct and/or
indirect objects are omitted (object ellipsis) in the user’sutterance, such as “Place the cup down (on the table),” itwould be preferable to generate an appropriate descriptionof the objects. The how-to-conﬁrm problem deals with themapping between language and physical/virtual objects, andhas been widely explored in Natural Language Generation(NLG) studies (e.g. [3]–[5]).
The robotics community has recently been paying greaterattention to the mapping between language and real-worldinformation, mainly focusing on motion [6]–[8]. [9] presents
an application of recurrent neural networks to the problemof handling many-to-many relationships between motion
sequences and linguistic sequences. In [10], a linguistic
model based on the symbolization of motion patterns is proposed. The model integrates a motion language model and anatural language model that can be used to recognize motion
patterns as sentences and to generate motion from sentences.
Moreover, we have proposed a robot language acquisition
framework that integrates multimodal information such as

1774speech, motion, and visual information [11].
In [12], LCore is extended to solve the when-to-conﬁrm
and how-to-conﬁrm problems using an adaptive conﬁdence
measure called the integrated conﬁdence measure (ICM)
function. The ICM function is acquired in the learning phase,and is used to reduce motion failure in the execution phase.
Although experimental results have shown that the risk of
motion failure is reduced in the execution phase, the problem
is that motion failure is needed in the learning phase to avoid
overﬁtting.
In this paper, we propose a method for learning the ICM
function by introducing an active learning framework with
Bayesian logistic regression into dialogue management. The
proposed method has three key features:
1) A user model corresponding to each modality is
assumed to be shared by the user and robot. This
assumption enables us to introduce an active learning
framework into human-robot dialogue. The user modelis explained in Section III.
2) Active learning is used for selecting the optimal utterances to generate, which effectively train the ICM
function. The introduction of active learning is evaluated using likelihood criteria in Section V.
3) Bayesian logistic regression (BLR) [13] is used forlearning the ICM function that enables us to estimate
the probability that the user’s utterances will be successfully understood from multimodal information.
II. TASK E NVIRONMENT
A. Object Manipulation Dialogue Task
Fig. 3 shows the task environment used in this study. A
user sits in front of a robot and commands the robot by
speech to manipulate objects on the table located between
the robot and the user. The robot is also able to commandthe user by speech to manipulate the objects. The objects
used in the experiments are shown in Fig. 2.
We assume that linguistic knowledge (e.g., phoneme sequence and, word sequence) and non-linguistic knowledge(e.g., motion and, visual information) are learned by using
LCore [11]. This knowledge is not given by the designer, but
is learned through interaction with users. Knowledge representation in LCore is explained in Section III in detail. The
main functions given by the designer are object extraction
and calculation of visual features.
The task has three phases:
1) Robot command phase (learning phase (a))
The robot commands the user to manipulate objects.
The ICM function is trained using the proposed
method.
2) User command phase (learning phase (b))
The user commands the robot to manipulate objects.
The ICM function is trained with initialization basedon the results of the robot command phase.
3) Motion execution phase
The user commands the robot to manipulate objects,
however the ICM function is not updated. Motion and

Fig. 1.

Fig. 2.
ments.

An example of object manipulation dialogue tasks.

Objects used in experi- Fig. 3. Robotic platform used in
the experiments.

conﬁrmation utterances are generated by the method
proposed in [12].
Fig. 1 shows an example of the user command phase. The
ﬁgure depicts a camera image in which the robot is told toplace Object 1 (Barbabright) on Object 2 (red box). The
solid line shows the trajectory intended by the user. The
relative trajectory between the trajector (moved object) andthe reference object is modeled with a hidden Markov model(HMM) [14]. The reference object can be the trajectory itself
or a landmark characterizing the trajectory of the trajector.
In the case shown in Fig. 1, the trajector, reference object,
and reference point are Object 1, Object 2, and Object 2’s
center of gravity, respectively.
B. Robotic Platform
Fig. 3 shows the robot used in this study. The robot consists of a manipulator with seven degrees of freedom (DOFs),a four-DOF multiﬁngered grasper, a microphone/speaker, astereo vision camera, 3D time-of-ﬂight camera (SR-4000),
and a gaze-expression unit. Teaching signals can be provided
by hitting a touch sensor on the grasper.
The visual features and positions of objects were extracted
from image streams obtained from the stereo vision camera.
The extraction and tracking of objects are done based on
their color. The visual features have six dimensions: three
for color (L*a*b*) and three for shapes. The shape features,
object area farea , squareness fsq , and width-height ratio
fwhr are deﬁned as farea = wh and fsq = Nobj /wh, whereh, w and Nobj denotes the object’s height, width, and numberof pixels, respectively. For motion learning/recognition, thetrajectories of objects’ centers of gravity are used.

1775III. T HE LC ORE F RAMEWORK
A. LCore Overview
The LCore [11] selects the optimal action based on an integrated user model trained by multimodal information when auser’s utterance is input. A user model corresponding to each
modality (speech, vision, etc.) is called a belief module. The
user model integrating the ﬁve belief modules – (1) speech,(2) motion, (3) vision, (4) motion-object relationship, and (5)behavioral context– is called the shared belief Ψ.

manipulated most recently”, respectively. The Minimum
Classiﬁcation Error (MCE) learning [15] is used to estimatehc .
The shared belief function Ψ is deﬁned as the weighted
sum of each belief module:

B. Utterance Understanding in LCore
An utterance s is interpreted as a conceptual structure z =(WT ,WL ,WM ), where WT , WL , and WM represent the phrases
describing the trajector, landmark, and motion, respectively.
For motion concepts that do not require a landmark object,z = (WT ,WM ). For example, the user’s utterance, “Place-on
Barbabright red box,” is interpreted as follows:+γ3 log P(Ŷk |x p t , x p r ,CV )hit i hir i h ji
+γ4 log( P(xI , xI |CV )WT : [ Barbabright ], WL : [ red , box ], WM : [ place-on ]The LCore does not deal with function words such as
prepositions and articles, i.e. the user is not supposed to use
words such as “on” and “the.”Suppose that an utterance s is given under a scene O. O
represents the visual features and positions of all objects inthe scene. The set of possible actions A under O is deﬁned
as follows:h ji

A = {(it , ir ,CV ) | it = 1, ..., ON , ir = 1, ..., RN , j = 1, ...,VN }, {ak | k = 1, 2, ..., |A|},(1)
where it denotes the index of a trajector, ir denotes the index
of a reference object, ON denotes the number of objects in
O, RN denotes the number of possible reference objects forh ji
the verb CV , and VN denotes the total number of CV in thelexicon.
Each belief module is deﬁned as follows: First, the belief
module of speech, BS , is represented as the log probabilityof s conditioned by z. Here, word/phrase orders is learned byusing bigrams/trigrams. Next, the belief module of motion,
BM , is deﬁned as the log likelihood of a probabilistic model
given the maximum likelihood trajectory Ŷk for ak . The belief
module of vision, BI , is represented as the log likelihood
hiiof WT given Object i’s visual features xI , where Object
i is either the trajector it or the landmark ir . Similar to
BI , the belief module of motion-object relationship, BR , isrepresented as the log likelihood of a probabilistic model
given the visual features of Objects it and ir .
The belief module of behavioral context, BH , represents
the adequateness of Object i as the referent under the context
hii hii
qhii = (q1 , q2 ). By using the parameter hc , BH is deﬁned
as follows:hii10 (q1 = 1)hii
BH (i, q ; hc ) = hc (qhii = (0, 1)) ,
(2)


hii
0
(q = (0, 0))
hii

hii

where q1 and q2 stand for truth values representing thestatements “Object i is being grasped” and “Object i wasΨ(s, ak , O, qhit i ) ={max
γ1 log P(s|z)P(z; G)
[BS ]
z
(
)
hi i
hi i
+γ2 log P(xI t |WT ) + log P(xI r |WL )
[BI ]
hi i

hi i

h ji

)}
+γ5 BH (it , qhit i ) + BH (ir , qhir i ) ,

[BM ]
[BR ]
[BH ]
(3)

hi i

where x p r denotes the position of Object i, and γ =(γ1 , ..., γ5 ) denotes the weights of the belief modules. The
MCE learning [15] is used for the learning of γ.
Inappropriate speech recognition results are re-ranked
lower by using Ψ. There are several methods for re-ranking
an utterance hypothesis (e.g. [16]). In contrast, information
on physical properties such as vision and motion is used in
Ψ, since object manipulation requires physical interaction.
IV. ACTIVE L EARNING OF THE I NTEGRATEDC ONFIDENCE M EASURE F UNCTION
A. Modeling Conﬁdence for Utterance Understanding
The proposed method quantiﬁes ambiguities in a user’sutterances. In this subsection, we ﬁrst explain the ambiguity
criterion used in this study.
Given a context q, a scene O, and an utterance s, theoptimal action â is obtained by maximizing the shared belief
function.
â = argmax Ψ(s, ak , O, q)(4)k

We deﬁne the margin function d for the action ak ∈ A as
the difference in the Ψ values between ak and the action
maximizing Ψ, a j (a j 6= â) :d(s, â, O, q) = Ψ(s, â, O, q) − max Ψ(s, a j , O, q)
a j 6=â(5)

Let al be an action that gives the second maximum Ψ value.
When the margin for the optimal action â is almost zero, theshared belief values of â and al are nearly equal; this meansthat the utterance s is a likely expression for both â and al .
In contrast, a large margin means that s is an unambiguous
expression for â. Therefore, the margin function can be used
as a measure of the utterance’s ambiguity.
We now deﬁne the integrated conﬁdence measure (ICM)
function by using a sigmoid function, as follows:f (d; w) =1,
1 + exp−(w1 d+w0 )(6)

where d is the value of the margin function for an action,
and w = (w0 , w1 ) is the parameter vector. The ICM function
is used for modeling the probability of success.

1776

We now consider the problem of estimating the parameters
w of the ICM function based on logistic regression. The ith
training sample is given as a pair consisting of the margin diand teaching signal ui . Thus, the training set ThNi contains
N samples:data set ThNi . The log loss L(ThNi ) is deﬁned as follows:

ThNi = {(di , ui )|i = 1, ..., N},

In this case, L(ThNi ) can be regarded as the sum of entropy.
Let V = {v j | j = 1, ..., |V |} denote the utterance candidates
in the scene O, and e j denote the margin linked with v j . Here,V means the possible combinations of a word sequence thatconsists of learned words. We make V a ﬁnite set by limiting
the length of a sequence. The proposed method selects theutterance that minimizes the Expected Log Loss E(ThNi , e j ).
E(ThNi , e j ) is deﬁned as follows:(7)

where ui is 0 (failure) or 1 (success).
BLR [13] is used to obtain the MAP estimate of w. We
use a univariate Gaussian prior with mean mi and varianceτi (i = 0, 1) on each parameter wi .
B. Utterance Selection as Active Learning
The schematic of the proposed method is illustrated inFig. 4. First, the utterance candidate generator generates
all possible linguistic expressions for each action ak andcalculates their margin. The set of margins is input into thescoring function based on the Expected Log Loss Reduction(ELLR) [17] and Bayesian logistic regression (BLR) [13].
Next, the optimal margin linked with an utterance is selected
and the utterance is output as a speech command to theuser. The true/false judgment module recognizes the motion
performed by the user, and judges the result as 0(false) or1(true). The result is input into the training set and used by
the scoring function.

Fig. 4.

Schematic of the proposed method.

Basically, a training sample for learning the ICM function
is obtained when a robot has executed a motion. Note that
we assume that belief modules and Ψ are shared by the user
and the robot to introduce active learning. Based on thisassumption, we can train the ICM function by using training
data obtained when the robot commands the user by speech
to manipulate an object.
The proposed method selects the utterance that is most effective for learning the function based on Expected Log Loss
Reduction (ELLR) [17]. Among many criteria, uncertainty
sampling [18] is the most basic method in active learning,
however it selects a sample with the most entropic prediction.
In contrast, ELLR asks for labels on examples that, once
incorporated into training, will result in the lowest expected
error on the test set [17].
Now, let fˆhNi (d) denote the ICM function trained by the

L(ThNi ) =N {}∑ fˆhNi (di ) log fˆhNi (di ) + (1 − fˆhNi (di )) log(1 − fˆhNi (di ))
i=1

hN+1i

E(ThNi , e j ) = fˆhNi (e j )L(T+hN+1i) + (1 − fˆhNi (e j ))L(T−

hN+1i
T+
, ThNi ∪ (e j , 1),
hN+1i
T−, ThNi ∪ (e j , 0)

),

(8)

Thus, Equation (8) takes into account the effect of a notyet-obtained sample. In ELLR, fˆhN+1i (e j ) is trained inadvance of obtaining the (N + 1)th sample. On the other
hand, uncertainty sampling [18] does not take into accountthe effect of selecting the (N + 1)th sample.
V. E XPERIMENTS
A. Experimental Setup
To evaluate the proposed method, we conducted two kindsof experiments: (1) active learning of the ICM function,and (2) evaluation of the proposed method. The objectiveof Experiment (1) is to investigate the number of samples
necessary for convergence of the learning. Experiment (2)was aimed at evaluating the effectiveness of using the resultof Experiment (1) as the prior distribution. Although theeffectiveness is unclear since the assumption that the user
and robot share the ICM function is not always true, we will
clarify the advantages of the proposed method.
In Experiment (1), the robot commanded the user byspeech to manipulate objects based on the proposed method.
This ﬂow which starts from the robot’s utterance and ends
with the user’s manipulation is called an episode. The maximum number of episodes was set to 30. The prior distributionof the parameter wi was deﬁned as a univariate Gaussian
distribution. The parameters of the prior, or hyperparameters,
were set as (m0 , m1 , τ0 , τ1 ) = (0, 1, 100, 100). The hyperparameters (m0 , m1 ) were set as (m0 , m1 ) = (0, 1) so as to makethe initial ICM function be the standard logistic sigmoid
function. The maximum length of (WT ,WL ,WM ) were set to(3,3,1), respectively.
In Experiment (2), we obtained the training and test data
as follows. First, the subject was instructed to command
the robot in the same environment as Experiment (1). This
enabled us to obtain 60 pairs of camera image and speech,
which we labeled with the indices of {motion, trajector,
landmark}. Half of the data was used as a training set andthe other half was used as a test set.

1777

We compared the case in which the parameter estimatedin Experiment (1) was used as the prior distribution witha case involving a “standard” prior distribution withoutparameter tuning. The parameters of the standard prior wereset as (m0 , m1 , τ0 , τ1 ) = (0, 1, 100, 100). To evaluate thesemethods, we compared test-set likelihood, where ten different
combinations of a training and test set were used. Similar toExperiment (1), we use the word “episode” to represent theﬂow that begins from the user’s utterance and ends with therobot’s manipulation.
In Experiment (2), the number of motion failures was
also compared. We compared the average number of motion
failures occurring from the ﬁrst to the ic th episodes, where
ic represents the episode in which a convergence condition
regarding log likelihood L was met. The convergence condition is set as L < −20, based on the results of the experimentin [12]. Although we continued the actual experiment after
the convergence condition was met, the learning should beterminated here for efﬁciency.
The lexicon used in the experiments contained 23 words (8nouns, 8 adjectives, and 7 verbs). The user taught the names
or properties of objects in Japanese1 by showing the objects
to the robot. Unsupervised learning was used for obtaining
the phoneme sequences of the words [11]. Those words
had been grounded to the physical properties of objects andmotions in the learning phase of the lexicon [11], [14].

¤£¡[Situation: Object 1 was manipulated most recently]
R: Jump-over Pooh-doll Kermit.
U: (The user makes Object 3 jump over Object 2.)¢

Fig. 5. Dialogue example in the learning phase. The correct action is tomake Object 3 (Pooh-doll) jump over Object 2 (Kermit).

B. Results (1): Active Learning of the ICM Function
First, we address the qualitative results. Fig. 5 shows anexample dialogue between the subject (U) and the robot(R). In this case, the number of possible combinations ofobjects and motion were 45, which means that 45 pairs ofa word sequence and margin are generated by the utterance
candidate generator shown in Fig. 4. Among the pairs, themargin d = 13.4 is selected based on Equation (8). Here, theutterance linked with the margin d = 13.4 was “Jump-overPooh-doll Kermit (Make the Pooh doll jump over Kermit.),”In Fig. 6, the selected margin is plotted against an episodethat represents the number of robot utterances. In the ﬁgure,the dotted line shows the episode in which motion failure by
the user has occurred. From the ﬁgure, we can see that thelarger margin is selected at the (i∗ + 1)th episode comparedwith the i∗ th episode, where i∗ represents the episode inwhich such motion failure occurred. This means that anutterance with less ambiguity is selected at the (i∗ + 1)th
episode.

Fig. 6. Margin selected by Equation (8). The dotted line shows an episodein which motion failure by the user occurred.

average log likelihood, where ten different combinations ofa training and test set were used. The ﬁgure clearly indicatesthat the proposed method outperformed the baseline in theearly episodes.
The right-hand ﬁgure of Fig. 8 compares the average
number of motion failures occurring from the ﬁrst to theic th episodes. From the ﬁgure, we can see that the numberof motion failure could be reduced by using the proposed
method if we terminated the learning at the ic th episode.
The reduction in the number of motion failures supports thevalidity of the prior pre-trained by using active learning.

C. Results (2): Evaluation of the Proposed Method
Fig. 7 shows examples of camera images input for the
proposed method. The inputs into the system were the visualfeatures of the extracted objects, context information (Object
X was manipulated most recently, etc), and user utterances.
The left-hand ﬁgure of Fig. 8 compares the average testset log likelihood of (1) the proposed method and (2) thebaseline which used a standard prior. The lines show theFig. 7. Training samples. Yellow frames represent the most recently manipulated objects. Left: The input user utterance was “Move-away Kermit,”
and the correct output was to move Object 86 away from Object 82. Right:
The input user utterance was “Place-on red box,” and the correct output
was to place Object 76 on Object 77.

1 In this paper, the utterances are translated into English.

1778

at
http://mastarpj.nict.go.jp/∼ksugiura/
video gallery/video gallery en.html.
VII. ACKNOWLEDGMENTS

Fig. 8. Left: The test-set log likelihood of (1) the proposed method and(2) a baseline. Right: Average number of motion failures.

¥¨[Situation: Object 2 was manipulated most recently]
U: Move-closer box Elmo.
R: Move-closer green box?
U: No.
R: Move-closer blue box?
U: Yes.
R: (The robot moves Object 3 closer to Object 1.)§¦

Fig. 9.
Dialogue example (2). Motion execution with a conﬁrmationutterance. The correct action is to move Object 3 (the blue box) closer
to Object 1 (Elmo).

D. Additional Result in Motion Execution Phase
In this subsection, we show an additional qualitative resultin the motion execution phase. The experiment conditions
are explained in detail in [12]. Fig. 9 shows a dialogue
example in which a user’s utterance is disambiguated byusing grounded information.
In Fig. 9, the ICM value of the optimal action â was small.
Therefore, the robot ﬁrst asked whether “green box” wasthe trajector. Here, the word “green” was used to describe
the major difference between Object 2 (the green box) andObject 3 (the blue box). In the second conﬁrmation utterance,
the word “blue” was inserted into the phrase WT , since thisgave the maximum margin. In contrast, the landmark was
not mentioned in either generated utterance since no word
insertion into WL had a signiﬁcant inﬂuence on the ICM
values.
VI. C ONCLUSION
Safe interaction with users is a critically important
requirement for assistive robots supporting users ineveryday environments. In this paper, we proposed amethod that decreases the risk of motion failure in thelearning phase. One of the contributions of this study is theintroduction of active learning into a multimodal spoken
dialogue system. Some demo video clips can be foundThe authors would like to thank Masahide Imaki for
his assistance in the system integration. This research waspartially supported by the Ministry of Education, Science,Sports and Culture, Grant-in-Aid for Scientiﬁc Research (C),20500186, 2008, and the National Institute of Informatics.
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