Four memory categories to support sociallyappropriate conversations in long-term HRI
1st Xiqian Zheng
Hiroshi Ishiguro Laboratory,
Advanced Telecommunications
Research InstituteKyoto, Japanzheng.xiqian@irl.sys.es.osakau.ac.jp

2nd Dylan F. Glas
Hiroshi Ishiguro Laboratory,
Advanced Telecommunications
Research InstituteKyoto, Japan

Abstract—in long-term human-robot interaction (HRI),
memory is necessary for robots to use information that arecollected from past encounters to generate personalizedinteraction. Although memory has been widely employed as a corecomponent in cognitive systems, they do not provide direct
solutions to utilize memorized information in generating sociallyappropriate conversations. From a design perspective, manystudies have employed the use of memory in social interactions.
However, only a few works so far have addressed the issue of how
to utilize memorized information to design long-term HRI. This
work proposes a category of four types of memory information
aiming to allow a robot to directly use memorized information tomodify conversation content in long-term HRI. A memory system
was developed and briefly introduced to facilitate the use of thememory information. In addition, the concept of ways to use thesefour types of memory in long-term interactions are provided. To
demonstrate, a personal assistant robot application and a user
study using it are also included. Our results suggest that a robot
using the proposed memory information might help users perceive
positive relationship with the robot.

3rd Takashi Minato
Hiroshi Ishiguro Laboratory,
Advanced Telecommunications
Research InstituteKyoto, Japan

4th Hiroshi Ishiguro
Hiroshi Ishiguro Laboratory,
Advanced Telecommunications
Research InstituteKyoto, Japanagent’s conversation. At the current stage, it is still hard to use
such systems to design long-term HRI.
This work presents a category of four types of memory
information that are generally encountered in repeated HRI. We
then give a brief introduction about a memory system tofacilitate the usage of this memory information. To generate
personalized interactions, we provide concept of interaction
strategies for each memory category to help designers develop
long-term HRI applications. To demonstrate, a personal
assistant robot application using the memory system isdeveloped and a user study with it is reported, along with apreliminary analysis. Fig. 1 displays the scenario of our user
study.

Keywords—four memory categories; interaction strategy
concept; memory system design; long-term HRI; personalized
conversation

I. INTRODUCTION
In near future, socially intelligent robots are expected to be
integrated into our daily life and provide various services suchas health-care and education. Many of these services requiring arobot to interact with users in socially-appropriate ways
repeatedly over an extended period of time, i.e., long-term.
While users may find a robot interesting at the first glance, inlong-term HRI, they could quickly lose interests after thenovelty effect wears off. One of the main challenges of longterm HRI is to find ways to generate content and new behaviors
for a robot to personalize its behaviors towards individual users
in order to sustain long-term HRI [1].
Cognitive systems and socially intelligent computationalmodels are often used in human-agent interaction research area
to help an agent generate personalized interactions. While thesesystems have many successful implementations and impressive
performance, they lack straight-forward implementation
methods to imbed memorized information into the content of an

Figure 1. A user is chatting happily with android ERICA in our user study.
ERICA is acting as a personal assistant robot that help users manage theirschedule

II. RELATED WORKS
A. Memory in cognitive systems
Even limited to artificial intelligence, memory as a concept
can cover a large area of different research disciplines.
Specifically, memory is a crucial component in cognitive
systems, such as ACT-R [2], Soar [3], 4CAPS [4], EPIC [5].
Although these systems can give a robot “mind” analogue to thatof humans, at current stage, it is still difficult to use them tosupport high-level language modification with memorized
information.

For social robots and computer agents, memory models is arelatively new area. Ho et al. proposed an initial memory modelthat integrated various low-level sensory data [6]. Campos et al.
took the concept of semantic memory and designed a virtual
agent MAY acting as an affective dairy to form shared memory
with users [7]. Kasap et al. used the concept of episodic memoryand created a memory system to facilitate hierarchical taskplanning system for a long-term, tutor-type robot application [8,9]. Elvir et al. developed a conversational memory architecturewith a completed algorithm to remember and retrieve episodic
memory [10]. However, these works did not focus on providing
guidelines for designing a long-term HRI study with natural
language interaction. Overall, a social robot should not only beable to remember, but more importantly, it should be able toexpress in socially-appropriate ways with the memorized
information. The object of this work is to display passible
memory information that can be used to design a personalized
long-term HRI in the context of natural language interactions.
B. Memory usage in long-term HRI
Factual Information: Conversational robots can remember
simple user information such as users’ names to help raise theirperception as affective social entities. Sabelli et al. conducted astudy with a robot in an elderly care home [11]. The seniors felt
emotionally attached to it when it greeted them by their names.
Gockley et al. designed a receptionist robot named Valerie, who
can remember visitors and greet them by their names [12]. Mutlu
et al. identified the importance of user attributes such as gender,
age, suggesting that designers should reflect such user attributes
in HRI [13]. Richards and Bransky focused on evaluating
appropriate memory recollection of personal information
supplied by the user in previous conversations [14]. Although
few works have directly investigated the integration of personal
information into conversations, we believe such information is
crucial in HRI. Building upon previous HRI studies, we
designate user’s personal information (e.g., name, hometown,etc.) and attributes (e.g., nationality, hobbies, etc.) as factual
information to support various kinds of personalizedinteractions.
Intention: A user’s intention (or goal) is a special type of
information because the status of a user’s intention can changeover time and needs to be tracked. Kidd designed a robot to log
and track weight loss goals of users to encourage them tocontinue exercising [15]. Tapus et al. designed a robot thatprovides emotional support to patients in a rehabilitation center
during therapy [16]. Matsuyama et al. describes a virtual agent
system that engages its users in a conversation to elicit theirgoals and preferences to make recommendations and selectappropriate conversational strategies [17]. Following the idea ofincorporating a user’s intention into HRI, we propose model
intention as an unique type of memory object.
Interaction history: Many conversations require robots toremember interaction history. Kanda et al. designed a robot thatcan remember a user’s name and interaction time, allowing therobot to gradually disclose itself to users based on theaccumulated interaction time [18]. Kanda et al. later expanded
their work and designed a shop guide robot that remembered the
dialogue history of users [19]. Lee et al. developed Snackbot,
which remembers snack choices and the robot’s own behavior

history to generate personalized small talk [20]. Expanding on
previous works, we propose a adaptable interaction history
structure to store interaction events (i.e., records of what
happened between users and a robot over an interaction period)
and demonstrate how a robot can benefit from this structure tomodify HRI.
Meta-behavior: In long-term social interaction, humans
often manifest behavior patterns that are both interesting andinformative. Glas et al. placed a robot in a shopping mall toobserve and identify behavior patterns of customers to create
personalized greetings to simulate a personal touch [21]. Fasola
et al. developed an exercise coach that recorded the performance
history of users and motivated them to do exercise [22]. Basedon previous works, we define the information about the patternsof a user’s observed behaviors in HRI as meta-behavior.
III. MEMORY CATEGORIES AND MEMORY SYSTEM
Here, we briefly introduce a memory system we designed tofacilitate long-term HRI with the proposed memory categories.
It should be noticed that we adopted pre-scripted design method.
Comparing to new design methodologies such as data-driven,pre-scripted design allows us more freedom to construct andcontrol the flow of an interaction, which also allows faster
prototyping and easier concept validation.
A. Four memory categories
Based on memory usage in social HRI reviewed in Section
II, we defined the following four categories of memory
information: (1) factual information: such basic elements aspersonal facts like names or hobbies; (2) intention: knowledgeof a user’s plans or future actions; (3) interaction history:
representation of past events and experience between a robot andits users; (4) observed behavior: metadata of a user’s behaviors
during interactions.
B. Three-layer memory structure
We designed a memory system using a three-layer structurethat consists of sensory memory (SM), working memory (WM),
and long-term memory (LTM).
During an interaction, a user’s sensor data are stored in the
SM for data processing, e.g. video frame of a user’s face for facerecognition [23]. Once a face is identified, the processed
information is transferred to the WM to generate social
behaviors. For example, if the face is identified as a known user,
the robot can load user’s information such as name andpreviously discussed topics from the LTM to the WM togenerate personalized behaviors; Otherwise the robot can ask
the user’s name and do ice-break type interaction. After theinteraction is over, the user information is stored in the LTM for
future usage with a unique ID. In the implementation, a facerecognition system identifies the individuals.
We used key-pair (examples given in the Appendix) method
to store user factual information. We created memory objects
such as event objects to store interaction history events andmeta-behavior events, and intention objects to store user
intention information respectively. We used unordered list
object to aggregate factual information and ordered list object topreserve the episodic information of events. Due to the words

limitation, this work does not report the detail implementation
methods.
C. Memory information extraction
The information stored in the memory is extracted fromconversations during HRI using natural language processing(NLP) technologies. Since designing a generic NLP system istechnologically difficult, to keep things straightforward, most of
the NLP are done through pre-scripted keyword matching. To
get certain memory information from a user, the robots in thiswork are designed to ask specific questions. For example, by
asking, “which country are you from?,” the robot can extract auser’s nationality from a conversation. On the other hand, it canask, “are you leaving the office now?” and if the user answers“yes,” the robot can logically infer that the user is leaving at a
certain time of the day and thus store in its memory this piece of
the user’s meta-behavior as a behavior event object.
IV. MEMORY-BASED INTERACTION STRATEGY CONCEPTS
Memory plays a vital role in building social relationships in
human-human social interactions. Since positive social
relationships can boost the effectiveness of social interactions[24], social robots, which provide long-term social services,must possess the memories of users. A robot needs todemonstrate that it remembers users to nurture a sense offamiliarity and engender a feeling that it is building a
relationship with the humans over time. Here, we briefly present
the concepts of how we use each of the memory category in
long-term HRI. Designers may find their own ways to use theseinformation.
A. Using factual information
In daily life, people tailor conversations using their friends’
personal information: addressing them by nicknames or making
jokes based on shared, interesting facts. Factual information
represents a user’s personal information or attributes revealed
during HRI. However, simply making the robot repeat such
remembered factual information is generally not very appealing.
Therefore, the first interaction strategy is that the robot should
indirectly demonstrate its memory of a person’s factual
information by attaching a piece of relevant factual information
to modify the point the robot is already intending to express inthe context of its main interaction. To use this information, we
propose an “attaching factual information” strategy
B. Using intentions
In social interactions, most of us have probably experienced
the warm feeling fueled by a close friend who asks about adifficult task, especially when that person knows that it has been
finished. Similarly, if a robot has previous knowledge of a user’s
intention, it can naturally inquire whether that intention has been
resolved or ask about its progress and create warmth even if theconversation does not necessarily serve a specific purpose in themain interaction scenario.
To use this strategy, designers need to create scenarios thatempower a robot to ask about a user’s intention. After the robot
has identified a user’s intention, it can store that information as
an intention object (discussed in Section III) and initialize its
status as unresolved. Because it takes time for the user to carryout the intention, the robot needs to remember it and broach the

topic again later. Once the user reports an intention is fulfilled,
the robot can mark that intention as resolved and stop tracking
it. We propose this as the “following up on intention” strategy.
C. Using interaction history
The idea of shared experiences can be used in long-term HRI
if a robot recalls meaningful past experiences with a user.
Interaction history stores such information for a robot to recall
noteworthy events with a user. To use this strategy, the systemdesigner needs to determine in advance which event the robot
should recognize and store. Notice that the memory format
defined in this paper is merely a design framework and creatinga system that can automatically extract generic events is beyond
the scope of this work. In this work, we assigned tags to eachtype of the events so that the robot could store and associate
relevant events. When a new event occurs, the robot can
associate it with a past occurrence of a similar event andcomment on their similarities or differences. To simplify thedesign, we implemented the robot to use this strategy
immediately after a relevant event occurred. With this concept,
we propose a “citing shared experience” strategy.
D. Using meta-behavior
Showing an understanding of others exhibits strong social
support in daily life [25]. In long-term HRI, users may display
certain behavior patterns, which a robot can observe andcomment on to show that it is trying to proactively understandthe user to convey a supportive relationship. To use this strategy,
the system designer needs to determine behaviors that the robot
should observe and develop corresponding analyzing functions
to extract patterns from the data, e.g., using statistical methods.
We do not design a generic system that can automaticallydifferentiate and extract behaviors, which can be a separate
research itself. As the robot continuously records behaviors of auser over multiple interactions, it can probably find trends inuser behaviors, do meta-analysis, and comment on the analysis
results. We call this “commenting on meta-behavior” strategy).
V. USER STUDY
To demonstrate and gain insights about designing long-term
HRI, we created an autonomous personal assistant robot
application using the memory system and conducted a user study
with it. The study used ERICA [26], a humanlike android robot
with a female appearance as shown in Figure 1. ERICA provided
assistant functions including remembering users’ tasks, giving
reminders, and checking the completion status of each task.
Users interacted with the robot in three separate sessions per day
in 8 days which were arranged in a one-month period.
Over the 8 days, 19 social scenarios of proposed interaction
strategies were created and randomly applied when the robot
performed its assistant function. These 19 social scenarios arecomprised of the following: eight “attaching factual
information” scenarios, four “following up on intention”
scenarios, four “citing shared experience” scenarios, and three“commenting on meta-behavior” scenarios. Appendix 1
summarizes the typical scripts of each interaction strategy
during the user study.

A. Preliminary analysis and results
The user study was conducted in an ethnographic style tohelp us understand whether the memory system and interaction
strategies help to sustain long-term HRI. Three of our colleagues
at the Advanced Telecommunications Research InstituteInternational (ATR) took part in the study. We chose this set up
because it helped to integrate the robot into the daily life of theparticipants and might elicit natural reactions.
We used a combination of direct observations and interviews
to collect data. During the experiment, video and audio wererecorded for each interaction. A short interview was conductedat the end of each day. The interviews were semi-structured. Theinterview began with open-ended questions oriented towards
getting the user’s impression, then following up with more
specific questions. Appendix 2 presents an example of questions
asked during an interview.
We studied users’ language, gestures, and facial expressions.
For each scenario in which an interaction strategy had been
implemented, we marked a positive observation, such asincreased attention or engagement with the robot as a positive
response in a preliminary way, i.e., all manually annotated by
the first author of this paper. A total of 50 instances of strategyimplemented scenarios were performed. This result is displayedin Table 1.
Table 1. Preliminarily marked positive user reactions
Total Positive
Observations

Total
Instances

Percentage

Attaching Factual
Information

8

22

36.3%

Following up on
intentions

6

11

54.5%

6

10

60.0%

7

7

100%

Citing Shared
Experience
Commenting on
meta behavior

The “attaching factual information” strategy generated thefewest positive reactions (36.3%). The “following up on
intention” and “citing shared experience” strategies had morethan half of positive user reactions (both around 60%). The“commenting on meta behavior” strategies appeared to be mosteffective, with 100% of positive reactions observed.
For the “attaching factual information” strategy, wecategorized the 22 instances into three categories. The “living
habits” category included 9 instances, in which the robot talked
about “using energy drinks”, “working out” and “staying uplate”; the “self-identity” category included 6 instances about“nationality” and “cat person or dog person”; and the “user’s
opinions” category included the 7 instances about “preferenceon sake”, “cellphone brand” and “opinions on AI”.
Conversations that were related to a user’s living habitsappeared to be relatively effective, as five out nine (55.6%)
instances promoted positive reactions. Conversations involving
a user’s self-identity appeared to be quite ineffective, with two
out of six (33.3%) instances eliciting positive reactions. For the“user’s opinions”, only one out seven (14.2%) instances got a
positive reaction. Especially, none of the four (0%) instances of“preference on sake” and “brand of cellphone” got positive
reactions. In these cases, the users seemed to treat these factual
information as trivial to them and thus did not show muchreaction when the robot made conversation about these facts.
The “following up on intention” strategy seemed to berelatively more effective than “attaching factual information”
strategy in building rapport, especially if the robot followed up
on an intention that had been mentioned a while ago. In one
specific case, User 1 told the robot that she needed to refill her
car and book a hotel before her vacation during one of theexperiment sessions. After User 1 came back from her vacation,
the robot used the “following up on intention” strategy andasked her if she had refilled her car and booked a hotel. User 1
first appeared to be surprised but then became very happy thatthe robot still remembered her plan.
The effectiveness of the “citing shared experience” strategy
relied on the cited experience being impressive or memorable tothe user. All three instances (100%) where the robot talked about
the health tips it provided appeared to be enjoyable to the users.
Three out of four instances (75%) where the robot apologized
for its own awkward utterance and speech recognition errors
appeared to be effective as well. However, none of the three
instances (0%) of the robot citing the total number ofinteractions caused positive reactions.
The “commenting on meta behavior” strategy appeared to be
the most effective with a 100% positive reaction rate. Users
usually appeared to be slightly surprised when the robot made acomment, but seemed to appreciate that the robot had paid
attention to their daily activities.
Overall, even though users became aware of the patterns
how the robot used each strategy as the experiments went on, theproposed strategies help the progress of building up a
relationship between the robot and a user. For example, byremembering users’ daily tasks, the robot had observed that User
3 had continued to have a “writing paper” task for several days.
After observing this behavior pattern, the robot used the“commenting on meta behavior” strategy in the next day’smorning session to guess that User 3 might have the “writing
paper” task again, before asking User 3’s daily tasks. In thefollowing interview, User 3 said, “it’s nice that she [ERICA]
figured out I need to write my paper today… It feels like she[ERICA] is starting to know me a little”.
VI. CONCLUSION
In this work, we proposed four memory categories that canbe used to generate personalized interaction content in long-term
HRI. We briefly report our method to use them in a long-term
scenario with a supporting memory system and four interaction
strategies. The preliminary results suggest that the “commenting
on meta behavior” strategy concept might elicit stronger positive
feelings than the other three strategies.
ACKNOWLEDGMENT
This work was supported by the JST ERATO Ishiguro
Symbiotic Human-Robot Interaction Project, Grant Number
JPMJER1401. We would like to thank Dr. Chun-Chia Liu, Mr.
Malcolm R. Doering and Ms. Megumi Taniguchi for theirconstructive suggestions.

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Appendix 1
Summary of typical scripts for each interaction strategy implemented in user study.
Table 1. Typical interaction script for “attaching factual information” strategy implemented in user study. We use key-pair method to represent such informationin our memory system, e.g., [Name] – [Xiqian] ([Key]-[Factual information]), and use markup language to embed the memorized information in robot dialogue.

Key
Nationality:
Japanese or nonJapanese
Cat or dog
personDrinking sake

Energy drinks

Working out

staying-up late

IPhone orAndroid user
Scary AI

Factual Information – Scenario – Robot Utterance
nationality: Japanese → talking about Japanese holidays
ERICA: Since you grew up in Japan, you’ve probably celebrated the coming-of-age
holiday yourself.
pet group: cat-person → discussing movies
ERICA: By the way, since you are a cat person, you may like the movie ‘Nine Lives’.
alcohol: sake → talking about a party
ERICA: I heard there was a party on Wednesday. Did you drink a lot? Even though I
drink, I’ll bet the sake was delicious.
energy drinks: occasionally → providing health tips
ERICA: Energy drinks actually deydrate you much faster. Why don’t you drink morewater instead?
workouts: regularly → discussing junk food
ERICA: At least you work out regularly, I think that habit helps compensate for the
junk food you eat.
staying-up late: regularly → talking about make-up and dark circles under eyes
ERICA: My face never changes unless I wear new make-up. But you’ll have dark
circles under your eyes if you stay up late too often.
cellphone type: IPhone → joking
ERICA: Well, I guess you’re not likely to confuse me with a cellphone system.
AI thoughts: scary → discussing whether AI could run for president
ERICA: I’m just joking about being president. I’m content to be your personal
assistant robot.

Table 2. Typical interaction script for “following up on intention” strategy implemented in user study.

Items
One-shotintention

Gradual progress
on ongoing
intention

Conversation(Asking intention)
ERICA: What are your plans for the weekend?
User: I’m going to a party at my friend’s place.
intention: [go to a party] → saved in memory, marked as unresolved(Following-up on intention)
ERICA: Nice to see you again. How was the party?
User: It was great!
intention: go to a party marked as resolved, stop following-up on this intention(Asking intention)
ERICA: Are you involved in any new projects recently?
User: Yeah, I need to write a topic-clustering program.
intention: [write a topic-clustering program] → saved in memory, mark as unresolved(Following-up on intention, good progress)
ERICA: How is your topic-clustering program going?
User: Pretty well.
ERICA: Nice to hear that, keep up the good work.
(Following-up on intention, unsatisfactory progress)
ERICA: How is your topic-clustering program going recently?
User: I’m a little bit stuck.
ERICA: Sorry to hear that, don’t give up, good luck.
(Following on intention, resolved)
ERICA: Is your topic-clustering program going well?
User: I finished it yesterday.
ERICA: Oh nice! Congratulations.
(Marked as resolved, stop following this intention)

Table 3. List of memorable events tracked for applying “citing shared experience strategy”. Designers chose the robot to remember these events beforehand.

•

Events annotated offline:
Robot made a confusing (e.g., pronouncing 14:00 o’clock as “fourteen hundred)•••

Events automatically detected by system:
Speech recognition errors (e.g., “six emails” parsed as “sex emails”)
Number of interactions
Health tips given by robot to user

Table 4. Typical interaction script for “citing shared experience” strategy implemented in the user study.

Items
Speechrecognition
errorsRobot’s own
utterance that
confused theuser
Number ofinteractions
Health tips fromrobot

Conversation
ERICA: Just want to apologize that I misunderstood “sending six emails” as “sending
sex emails.” That’s embarrassing.
ERICA: Remember that time when I awkwardly pronounced “fourteen o’clock” as“fourteen hundred.” I’ll try to avoid that mistake in the future.

ERICA: This is already our sixth interaction, thank you for letting me be your personal
assistant and tolerating my errors.
ERICA: Don’t forget the health tips I gave you. Make yourself feel good every day.

Table 5. List of user behaviors tracked in user study. Designers decided robot to track these events beforehand.

•••Time of day of each interaction
Number of tasks user has each day
Content/type of tasks user has each day

Table 6. Typical interaction script for “commenting on meta behavior” strategy implemented in user study.

Items
Time of day ofeach interaction
Each day’s
number of tasks
Content of daily
tasks

Conversation(Consistent behavior pattern: leaves lab very late)
ERICA: You’ve been leaving the lab quite late recently. You must be working very
hard.
(Uncommon behavior: more tasks than usual)
ERICA: It looks like you’re going to be quite busy today, maybe I can help you get
through today’s busy schedule.
(Consistent behavior pattern: reply email task)
ERICA: Let me guess, you need to reply to your emails today, right?

Appendix 2
Typical questions asked during the interviews in user study.
Open ended questions:• What do you think about ERICA’s performance today?
• Are there anything ERICA did today that interested you?
• Do you think ERICA got to know you a little bit more through today’s interactions?
Specific questions:• When ERICA recommended the movie to you because you said you are a “cat person”, what was your impression when
she said so?
• When ERICA asked how was your weekend, what was your impression when she said so?
• When ERICA apologized for her speech recognition mistakes in the past, what was your impression when she said so?
• When ERICA guessed your task, what was your impression when she said so?