∎

11institutetext:  1 Technical University of Darmstadt
Hochschulstr. 1
64289 Darmstadt, Germany

2 Max Planck Institute for Intelligent Systems, Tübingen, Germany

Contact Author: Vignesh Prasad
Tel.: +49 6151 16-24475
Fax: +49 6151 16-24460
11email<EMAIL_ADDRESS>
# Human-Robot Handshaking: A Review

Vignesh Prasad1 Ruth Stock-Homburg1 Jan Peters1,2

###### Abstract

For some years now, the use of social, anthropomorphic robots in various
situations has been on the rise. These are robots developed to interact with
humans and are equipped with corresponding extremities. They already support
human users in various industries, such as retail, gastronomy, hotels,
education and healthcare. During such Human-Robot Interaction (HRI) scenarios,
physical touch plays a central role in the various applications of social
robots as interactive non-verbal behaviour is a key factor in making the
interaction more natural. Shaking hands is a simple, natural interaction used
commonly in many social contexts and is seen as a symbol of greeting, farewell
and congratulations. In this paper, we take a look at the existing state of
Human-Robot Handshaking research, categorise the works based on their focus
areas, draw out the major findings of these areas while analysing their
pitfalls. We mainly see that some form of synchronisation exists during the
different phases of the interaction. In addition to this, we also find that
additional factors like gaze, voice facial expressions etc. can affect the
perception of a robotic handshake and that internal factors like personality
and mood can affect the way in which handshaking behaviours are executed by
humans. Based on the findings and insights, we finally discuss possible ways
forward for research on such physically interactive behaviours.

###### Keywords:

Handshaking Physical HRI Social Robotics

##  1 Introduction

In the context of Social Robots and Human-Robot Interaction (HRI), there has
been an increase in the use of anthropomorphic robots in social settings, and
they already support human users in various industries, such as retail,
gastronomy, hotels, education, and healthcare services feil2005defining ;
han2012robotic ; jeffrey2016meet ; zhang2008service . In HRI, physical
presence plays an important role as it can influence the image of a robot as
compared to a virtual presence li2015benefit . According to the Computer-As-
Social-Actor (CASA) paradigm nass2000machines , physical presence is also an
important predictor of the mindless social response to robots by which humans
put a robot in the same category as humans. These responses are supposedly
triggered by social human-like cues as well, such as voice nass1997machines ,
face nass2001truth , and language style nass1995can . Additionally, the
appearance of the robot, as opposed to just the physical presence, can also
have an impact on the perception of robots. As the Uncanny Valley
mori1970uncanny  hypothesises, uncanny feelings are triggered by highly human-
like robots (androids) but not as realistic as a human being as compared to
robots that are less human-like in appearance (humanoids). These uncanny
feelings are hypothesised to get exaggerated even more in the case of
movements. The affinity, of both appearance and movement, rises again only
when the human-likeness becomes very close to that of a human being.

Figure 1: Conceptual Framework for categorising works on Human-Robot
Handshaking

In the context of HRI, physical contact plays a central role in the various
applications of social robots. One of the key reasons for this being that non-
verbal behaviour, especially touch, can be used to convey information about
the emotional state of a person hertenstein2006communicative ;
ogden2000robotic ; yohanan2012role . This enables a special kind of emotional
connection to human users during the interaction han2012robotic ; stock-
homburg2018negative . If, for example, one considers a future scenario in
which an accompanying robot shares the habitat with humans, a key requirement
for the robot would be its ability to physically interact with humans
wang2018predicting . In such a case, it would be advantageous that humans feel
more welcoming and be willing to interact and help a social robot with its
task like they would help other humans. Among such interactions, handshaking
is a common natural physical interaction and an important social behaviour
between two people schiffrin1974handwork , that is used in different social
contexts chaplin2000handshaking ; stewart2008exploring ; hall1983handshake .
The importance of touch in HRI and the prolonged nature of the contact
additionally make handshaking a more important interaction as compared to
other interactions, like high fives or other Asian greeting behaviours which
do not involve physical contact. Handshaking can, therefore, represent an
important social cue according to the CASA paradigm for several reasons:

  * •

It is one of the first and foremost non-verbal interactions which takes place
and should, therefore, be part of the repertoire of a social robot.

  * •

It plays an important role in shaping the impressions of others
chaplin2000handshaking ; stewart2008exploring ; bernieri2011influence , which
is used to develop an initial personality judgement of a person
aastrom1996greeting ; aastrom1994introductory .

  * •

The shaking of hands is seen as a symbol of greeting, farewell, agreement, or
congratulation. Socially, it symbolises acceptance and respect for another
person papageorgiou2015kinematic . The most common of these settings is
”greeting” in which it is usually the first non-verbal interaction taking
place in a social context.

  * •

The shaking of hands may help set the tone of any interaction, especially
since the sense of touch can convey distinct emotions
hertenstein2006communicative .

  * •

A good robot handshake may lead to future cooperation and coexistence
papageorgiou2015kinematic .

This can also be further enriched, such as in the possible scenario wherein a
robot can monitor the biological attributes of a person (such as stress levels
form blood flow) and thereby infer social information about a person from just
a single handshake duffy2003anthropomorphism . Having human-like body
movements plays an important role in the acceptance of HRI wherein humans tend
to look at robots more as social interaction partners kupferberg2011biological
. In the case of humanoid robots, having realistic motions enable similar
responses as humans chaminade2005motor . Thus, having a good handshake can not
only widen the expressive abilities of a social robot but also provide a
strong first impression for further interactions to take place. Robot
handshaking can additionally help improve the perception of the robot and
enable humans to be more willing to help a robot avelino2018power  allowing
for better integration of the robot into human spaces. To perform proper
handshaking motions, a social robot should be able to detect and predict
movements of the human and react naturally. Therefore, for better acceptance
and improved expressiveness, effective handshaking behaviours need to be
present to make social robots feel more acceptable. This importance can be
seen in Fig. 2, in the rising trend of works on human-robot handshaking.

1996-20002001-20052006-20102011-20152016-2020$0$$10$$20$$30$$1$$1$$12$$20$$27$YearsNumber
of PublicationPublications on Human-Robot Handshaking Figure 2: Publications
on Human-Robot Handshaking

Now that the importance of handshaking from a robotic standpoint has been
established, we propose the following framework for categorising the different
works, shown in Fig. 1. We first discuss works that aim to model handshakes
from human-human interactions. Along with that, we discuss the evaluation
criteria used for analysing participants’ feedback in experiments with humans
interacting with the robot. This can be seen in Sec. 2. Following this, we go
through each of the stages of handshaking, as depicted in melnyk2014analysis ;
vinayavekhin2017human , namely reaching (Sec. 3), grasping (Sec. 4) and
shaking (Sec. 5). We then explore more into the responses of participants to
different handshaking methods along with various factors, such as their
acceptability, their degree of preference, how external factors like voice and
gaze affect the perception of robot handshakes and so on in Sec. 6. Finally,
we discuss some of the shortcomings of existing works and propose some areas
for future research in Sec. 7 and present our concluding remarks in Sec. 8.
This was done as a broad categorisation of the works obtained after a search
of digital libraries, Google Scholar, IEEE Xplore and ACM Digital Library
using keywords that included ”human-robot handshaking”, ”robotic handshaking”
and ”handshaking AND human-robot interaction / HRI” followed by a depth-first
search-styled approach among the references and citations of the papers found.
Of these, we included all the articles that were published in conferences or
journals. We build upon our previous work prasad2020advances  where we talked
about the different works that model human handshaking interactions, and the
works in the different stages of the handshaking exchange and some social
responses of participants. In this paper, we dig deeper into these aspects and
provide key findings and insights of the different areas of work in regards to
Human-Robot Handshaking. In the end, we propose some ways forward based on
aspects that still need to be worked on in order to realise a social robot
that can truly capture the intricacies of such a physically interactive
action.

##  2 Insights and Evaluation of Handshakes

Before going into robotic handshakes, we first discuss a few works that draw
insights from human handshakes and some evaluation mechanisms used to measure
the parameters related to human acceptance of robotic handshakes. Regarding
the insights drawn, the main parameters looked at include trajectory profiles
(mainly acceleration, velocity, contact forces) between participants when they
shake hands and the mutual synchronisation of their movements while doing so.
Regarding the evaluation criteria, they mainly relate to how human-like the
handshake is and how the handshakes are perceived by humans who interact with
the robot.

###  2.1 Insights from Human-Human Handshake Interactions

A group of researchers from Okayama Prefectural University, Japan conducted a
series of studies jindai2015handshake ; jindai2012small ;
jindai2007development ; jindai2008handshake ; jindai2010small ;
jindai2011development ; jindai2006development ; yamato2008development ;
ota2014handshake ; ota2015handshake  to study handshaking interactions between
humans and analyse how participants respond to different behaviours applied on
a custom robotic arm. They use a VICON motion capture system to track markers
placed on both shoulders, right elbow wrists and hands of both participants.
Initially, Jindai et al. jindai2006development  observed that the motion of
the responder was seen to be similar to that of the requester with a lag
between their velocity profiles, which was found to be similar to a minimum
jerk trajectory profile. Hence they applied a lag-based transfer function for
generating the robot’s reaching trajectory based on the human’s reaching
motion. It was previously shown that this type of ”motion transfer” was
emotionally acceptable to humans in an object-handover scenario
yamamoto2005application . Subsequently, the oscillatory motion profiles of the
observed shaking behaviours were modelled as a spring and damper system
jindai2008handshake ; yamato2008development . Following this, the
interactiveness was studied by modelling the requesterjindai2011development ;
jindai2015handshake  and responderota2014handshake ; ota2015handshake
behaviours with respect to delays in the motions and auxiliary behaviours like
gaze and speech. The result of applying the key findings from the above works
on their robot is explored in Sec. 6. Their main findings of the modelling can
be summarised as follows.

  * •

The reaching trajectories of the requestor and responder are similar in their
trajectory profiles, which follow a minimum jerk trajectory model in
jindai2011development , and the motion of one can be used to mimic the other,
such as by using a transfer function jindai2006development .

  * •

As expected, the shaking behaviour and the transition from reaching to shaking
is modelled as a spring-damper system, given the oscillatory nature of the
behaviour yamato2008development .

  * •

Leading handshake behaviours were preferred over a non-leading one
yamato2008development  and a small delay (0.2s - 0.4s) between responding to a
handshake request was better perceived.

  * •

In terms of auxiliary behaviours, using voice with a small or no delay was
preferred and having the gaze shift steadily from the hand to the face was
well perceived.

A group of researchers from the University of Lorraine, France explicitly
study the mutual synchronisation (MS in short) between participants while
shaking hands along with the forces exerted on the palms melnyk2014analysis ;
melnyk2019physical ; melnyk2014sensor ; melnyk2015analysis ;
tagne2016measurement . They firstly study the hand motions by having the
participants wear a glove with an Intertial Measurement Unit (IMU) and 6 force
sensors placed around the palm melnyk2014sensor . Tagne et al.
tagne2016measurement  further investigate the joint motions as well (elbow and
shoulder) with IMUs placed at each joint. The influences of a few different
social settings, such as greeting, congratulating or sympathy, are then
explored as well melnyk2019physical ; tagne2016measurement . The MS between
participants is analysed using the Fourier analysis of the input signals
(mainly accelerations). However, wavelet transforms are shown to qualitatively
estimate the different stages of a handshake interaction as well
melnyk2015analysis .

Initially, the MS between participants was explored in a context-less setting
along with the contact strength of the interaction melnyk2014analysis . The
mean duration of a handshake was around 2.67 $\pm$ 0.87 seconds during which
the average duration of grasping was similar across pairs ( 0.5 s) whereas the
duration of shaking had a larger variation, from just below 1 s to almost 2.5
s. The frequency of shaking during MS peaked around 4Hz. The average strength
of the contact, which is the average of the forces measured by the sensors was
2.5N (no standard deviation was reported). This framework was extended to
analyse differences in a few social contexts melnyk2019physical ;
tagne2016measurement  and gender-based differencesmelnyk2019physical . Tagne
et al. tagne2016measurement  observe the differences between 3 scenarios
namely greeting, sympathy and congratulating. Melnyk and Hénaff
melnyk2019physical  analyse similar social settings, namely greeting and
consolation, and additionally analyse the trends across different gender-based
pairings.

Study | Setting | Duration | Wrist Frequency | Grip Strength
---|---|---|---|---
(s) | during MS (Hz) | (N)
Melnyk et al. melnyk2014analysis  | None | $2.67\pm 0.86$ | $4.20\pm 1.00$ | $2.50$
Tagne et al. tagne2016measurement  | Greeting | $0.90\pm 0.26$ | $2.43\pm 0.62$ | $4.37\pm 2.4$
2-6 | Sympathy | $1.30\pm 0.49$ | $2.44\pm 0.69$ | $3.12\pm 2.1$
2-6 | Congratulation | $1.24\pm 0.40$ | $2.66\pm 0.72$ | $5.88\pm 3.2$
Melnyk and Hénaff melnyk2019physical  | Greeting | MM | $0.73\pm 0.08$ | 3.6 (median 3.5) | $6.02\pm 0.99$
3-4 6-6 | MF | $1.48\pm 0.40$ | $5.68\pm 0.8$
3-4 6-6 | FF | $1.95\pm 0.26$ | $5.53\pm 1.17$
2-6 | Consolation | MM | $2.40\pm 0.23$ | 3.7 (median 3.5) | $6.08\pm 1.04$
3-4 6-6 | MF | $2.54\pm 0.42$ | $6.18\pm 0.97$
3-4 6-6 | FF | $4.05\pm 0.53$ | $6.29\pm 0.85$

Table 1: Summary of Results of melnyk2014analysis ; melnyk2019physical ;
tagne2016measurement  (M - Male, F - Female. Values are reported as mean $\pm$
standard deviation and only mean is reported when standard deviation is not
available.)

As seen in Table 1, a shorter duration was observed in greeting contexts. The
duration in cases of sympathy and congratulations were similar. The grip
strength shows contradictory results. Tagne et al. tagne2016measurement  saw
the lowest grip strength in case of sympathy, followed by greeting and then,
congratulations. Melnyk and Hénaff melnyk2019physical  found that it was
slightly higher in consolation case although not significantly. In terms of
gender-based pairings, it is seen that MM pairs shook for a lesser duration as
compared to mixed pairings. Female pairs shook hands the longest. This is
consistent with another study orefice2016let  as well. No conclusive
correlations were found between gender and grip strength, contrary to previous
studieschaplin2000handshaking ; orefice2016let .

Unlike the above works that explicitly measure the stiffness and forces of the
interactions, Dai et al. dai2019research  indirectly model the stiffness of
the elbow joint as a spring-damper system, like few other works described in
Sec. 5. They measure the expansion/contractions of the muscles in the upper
arm and forearm using EMG signals and thereby estimate the stiffness of the
elbow using the biceps and triceps and use the forearm muscle measurements to
observe indications of the grasping forces. Data was collected from 10
handshakes of which 5 were weak and 5 were strong. It was very evidently seen
that muscle activation in the case of the strong handshake was higher than the
weak condition.

The above works mainly study human-human handshaking to gain insights into the
motion and forces involved in handshaking. However, works looking into how
well robotic interfaces are suited for such haptic-heavy interactions are
limited in number. In this regard, Knoop et al. knoop2017handshakiness
perform experiments to understand the contact area, contact pressure and
grasping forces exerted by participants during handshaking and test out how a
few robotic hands and custom finger designs comply with their observations
from human-human handshaking interactions. Participants were asked to perform
3 handshakes of different strengths, namely, weak, normal and strong. A large
variation was seen in the final contact positions of the fingers at the back
of the hand. In the front, there is little variation across different
handshakes as almost the whole palm is held during the contact. This would
imply that there is possibly no fixed grasping location for a handshake, and
that the palms should be sufficiently covered. during the interaction. A
positive correlation was found between contact pressure and grasping force,
which is a straightforward implication since the contact area doesn’t vary
during the interaction. They test out how a few robotic hands and custom
finger designs compare with a human hand and argue that this study is useful
for optimising robotic hand designs at a coarse level.

Major Findings. Handshaking is inherently a synchronous process, which is
observed in the reaching motions by jindai2006development ;
jindai2007development  and the shaking by melnyk2014analysis ;
melnyk2019physical ; tagne2016measurement . This would imply that both parties
involved in a handshake try to achieve a common motion during the action. This
kind of inherent similarity in the motions and the subsequent synchronisation
can therefore be treated as an important aspect of making a handshaking
behaviour more acceptable. The context of a handshake along with additional
factors, like speech and gaze, play a role in the interaction as well. The
factors studied and the measurements obtained (in terms of duration,
frequency, relative grip strength etc.) can further be used to explicitly
model robotic handshake behaviours to give them a ”personality” of sorts or
provide appropriate responses based on detected interaction contexts improving
the social understanding of the robot.

Evaluation Method |  Works |  Evaluation Parameters
---|---|---
Bradley Terry Model |  Jindai et al.jindai2015handshake ; jindai2012small ; jindai2007development ; jindai2008handshake ; jindai2010small ; jindai2011development ; jindai2006development , Ota et al. ota2014handshake ; ota2015handshake , Yamato et al. yamato2008development  |  Participant’s preferences of handshakes
Kasuga and Hashimoto kasuga2005human  |  Flexibility, naturalness, kindness, affinity
Seven point scale |  Jindai et al.jindai2015handshake ; jindai2012small ; jindai2007development ; jindai2008handshake ; jindai2010small ; jindai2011development ; jindai2006development , Ota et al. ota2014handshake ; ota2015handshake , Yamato et al. yamato2008development  |  Handshake motion, Security, Velocity/comfort, politeness/Vitality
Avelino et al. avelino2018power  |  RoSaS (Warmth, competence, discomfort), Godspeed (anthropomorphism, animacy, likeability), closeness, willingness to help robot
Mura et al. mura2020role , Vigni et al. vigni2019role  |  Quality, human-likeness, responsiveness, perceived leader, personality
Five point scale |  Ammi et al. ammi2015haptic , Tsamalal et al. tsalamlal2015affective  |  Valence, Arousal, Dominance of Visual, haptic and visuohaptic interactions
Arns et al. arns2017design  |  Compliance, force feedback, overall haptics
Christen et al. christen2019guided  |  Naturalness of video of different simulated interactions
Score (out of 10) |  Wang et al. wang2008modelling ; wang2009hmm , Giannopoulos et al. giannopoulos2011comparison  |  Human likeness rating of Robot handshakes
Dai et al. dai2019research  |  Naturalness
Model Human Likeness Grade |  Avraham et al. avraham2012toward , Karniel et al. karniel2010turing , Nisky et al.nisky2012three  |  Human likeness of proposed handshake models
Table 2: Methods and Parameters used by different works to evaluate robotic handshaking. |  With force feedback |  Without force feedback
---|---|---
Human-like hand (4/5 finger model) |  Inactive |  Dai et al.dai2019variable  |  Campbell et al. campbell2019learning , Jindai et al.jindai2015handshake ; jindai2012small ; jindai2007development ; jindai2008handshake ; jindai2010small ; jindai2011development ; jindai2006development , Kasuga and Hasimoto kasuga2005human , Knoop et al. knoop2017handshakiness , Melnyk and Henaff melnyk2016bio , Nakanishi et al. nakanishi2014remote , Orefice et al. orefice2018pressure , Ota et al. ota2014handshake ; ota2015handshake , Stock-Homburg et al. stock2020evaluation , Vanello vanello2010neural , Vinayavekhin et al. vinayavekhin2017human , Yamato et al. yamato2008development
|  Passively controlled |  Arns et al. arns2017design , Beaudoin et al. beaudoin2019haptic , Ouchi and Hashimoto ouchi1997handshake , Pedemonte et al. pedemonte2016design ; pedemonte2017haptic  |
|  Actively controlled |  Avelino et al. avelino2018power ; avelino2018towards , Ammi et al. ammi2015haptic , Christen et al.christen2019guided *, Mura et al.mura2020role , Tsamalal et al.tsalamlal2015affective , Vigni et al.vigni2019role  |
Gripper |  |  Bevan and Fraser bevan2015shaking , Falahi et al. falahi2014adaptive , Jouaiti et al.jouaiti2018hebbian *, Sato et al.sato2007synchronization *
Rod-like end-effector |  |  Avraham et al.avraham2012toward , Giannopoulos et al.giannopoulos2011comparison , Karniel et al.karniel2010turing , Nisky et al.nisky2012three , Papageorgiou and Doulgeripapageorgiou2015kinematic , Wang et al.wang2009hmm ; wang2011handshake
Table 3: Different types of Robot End Effectors used for Human-Robot
Handshaking. *-simulated robot

###  2.2 Handshake Evaluation Methods

Given the differences in hardware and the evaluation criteria used by
different works, it is difficult to converge on a single metric or scale for
the task at hand. Moreover, different works evaluate different aspects of
handshaking, using different robots. It is therefore difficult to come up with
a common comparison baseline, although some studies evaluate their methods
similarly. To this end, we collate some of the common evaluation metrics and
methods used among different works in Table 2 and broadly categorise some of
the different robotic interfaces in Table 3. Some common aspects are the aim
to rate the acceptability of the handshaking interactions and the human-
likeness or the naturalness of the handshaking. Moreover, most of the works
that use a human-like end effector mainly have an inactive one, which could
cause the interaction to seem more unnatural. For example, in the works of
Wang et al. wang2011handshake  and Giannopoulos et
al.giannopoulos2011comparison  who use a rod-like end-effector, the case when
the robot arm is operated by a human, the handshake gets an average human-
likeness rating of only 6.8/10 which is far from the maximum score.

The common metrics used, like in most psychological and human studies, are the
seven-point or the five-point Likert scales, which is a bipolar scale that has
a negative valued sentiment on one end and a positive valued one on the other,
which allows for a nice representation, especially when averaging over the
data. For example, an overall negative average indicates an inclination
towards the negative sentiment and a positive average indicates an inclination
towards the positive sentiment. This, in contrast to comparing an absolute
score (rating out of ten for example), can help indicate the sentiments of the
participants better.

To use a more traditional test of computational intelligence, Karniel et al.
karniel2010turing  propose a Turing test for motor intelligence and come up
with a metric called as the Modern Human Likeness Grade (MHLG) which is used
to indicate the human-likeness of different shaking behaviours in this kind of
a mechanical Turing Test. This is based on the perceived probability by a
participant of the model being a human shaking the stylus or the algorithm.
Nisky et al. nisky2012three  propose different ways to perform this Turing
test for motor intelligence. There are described in further detail in Sec.
5.3.

##  3 Reaching phase of Handshaking

We have already described the work of Jindai et al. jindai2015handshake ;
jindai2011development ; jindai2006development  and Ota et al. ota2014handshake
; ota2015handshake  above. To the best of our knowledge, these were the first
works to model the hand reaching aspect and deploy it on a robot. As mentioned
above they propose two models. One with a transfer function based on the human
hand’s trajectory with a lag element and the other is a minimum jerk
trajectory model, which fits the velocity profiles and provides smooth
trajectories by definition. These modelling choices imply that a smooth motion
similar to the interaction partner is preferred with a small amount of delay
between them. However, they do not have any study showing how these two models
compare with each other.

More recent works model reaching using machine learning. Campbell et al.
campbell2019learning  use imitation learning to learn a joint distribution
over the actions of the human and the robot. During testing time, the
posterior distribution is inferred from the human’s initial motion from which
the robot’s trajectory is sampled. Their framework estimates the speed of the
interaction as well, to match the speed of the human. Christen et al.
christen2019guided  use Deep Reinforcement Learning (RL) to learn physical
interactions from human-human interactions. They use an imitation reward which
helps in learning the intricacies of the interaction. Falahi et al.
falahi2014adaptive  use one-shot imitation learning to kinesthetically teach
reaching and shaking behaviours based on gender and familiarity detected using
facial recognition. However, it cannot be generalised due to the extremely low
sample size. Vinayavekhin et al. vinayavekhin2017human  model hand reaching
with an LSTM trained using skeleton data. They predict the human hand’s final
pose and devise a simple controller for the robot arm to reach the predicted
location. In terms of smoothness, timeliness and efficiency, their method
performs better than following the intermediate hand locations. However, it
performs worse than using the true final pose due to inaccuracies in the
prediction.

Major Findings. Modelling of reaching behaviours draws heavily on learning
from human interactions, unlike other robotic grasping/manipulation tasks,
where a lot of it can be learnt from scratch. This provides a strong prior to
help make the motions more human-like and can also be used to initialise
campbell2019learning  or guide christen2019guided  the learning.

##  4 Controlling Hand Grasps in Handshaking

One of the first remote handshaking systems, proposed by Ouchi and Hashimoto
ouchi1997handshake , was aimed at two people performing a handshake while on a
telephone call with each other using a custom-made silicone-rubber based
robotic soft hand. They measured the pressure exerted on the hand using a
pneumatic force sensor which relays the force information over to the other
user, who has a robotic hand as well. With this type of active haptic
mechanism, they show that users better perceive the partner’s existence during
the call and that they were able to shake hands without feeling any
transmission delay. This shows the effect that such haptic interactions have
on the perception of the interaction partner.

Pedemonte et al. pedemonte2016design  design an anthropomorphic haptic
interface for handshaking. It is an under-actuated robot hand with a passive
thumb that is controlled based on the amount of force that is applied on it.
It is a sensor-less model with a deformable palm that controls the closure of
the fingers. A variable admittance controller is used to set the reference
position for fingers based on the degree deformation of the palm. Therefore
the amount of force exerted by the robot hand on the human hand depends on the
force exerted by the human, leading to a partial synchronisation in the
grasping. It takes approximately 0.6s to close the fingers. Arns et al.
arns2017design  build upon this design using lower gear ratios and more
powerful actuators to obtain a stronger grasping force and a faster
interaction speed. They argue that the use of impedance control as opposed to
admittance control helps improve responsiveness as well. A similar
synchronisation is observed as in the previous work as the mechanisms are the
same, in theory. The main difference is the speed of the interaction which is
almost instantaneous in this case (less than 0.05s), making the interaction
more realtime and natural.

Avelino et al. avelino2017human ; avelino2018towards  propose two models to
develop a pleasant grasp for handshaking. This is extended to three different
grasping models with different degrees of hand closure, corresponding to a
strong, medium strength and a weak handshake avelino2017human . Force sensors
present on the robots finger joints measure the interaction forces during
grasping. It was found that female participants mainly preferred strong
handshakes (85.7%). There was a larger variability among male participants.
Since a simple position based control is employed, the force perceived depends
on the hand sizes of the participants, which could be the cause of the
variability. This is addressed in avelino2018towards , where an initial study
is carried out where participants have to adjust their hand and the robot’s
grip until a preferable grasp is reached. This is done to find a suitable
reference force distribution among the sensors on the robot hand. The finger
joint positions are recorded as well. With this distribution, they compare a
fixed handshake to a force control method. The force control is done with a
PID controller whose set points are the average of the forces per sensor on
each finger obtained from the previous data. Moreover, they combine this with
a shaking motion presented in wang2008modelling  that is described in Sec. 5.
Participants had to rate the two handshakes based on various factors like
scariness, arousal (boring/interesting), meaningfulness, excitement,
strength/firmness, and the perceived enjoyment and safety, all on 7-point
scales for each variable. Although both handshakes were evaluated positively
overall, no significant differences were observed between them.

Vigni et al. vigni2019role  model the force exerted by the robot hand during
handshaking based on the force exerted by the human, measured using force-
sensitive resistors on the robot hand. The robot force is approximated from
the degree of hand closure using a calibration experiment where participants
are asked to mimic the force felt on their hand by a few open-loop handshakes
of the robot. The human force is estimated by fitting a cubic polynomial to
the sum of forces applied on the individual sensors. This is also done with a
calibration experiment where participants were made to grasp a sensorised palm
fitted with a load cell to measure the exerted force. They compare three
different controllers based on the relationships between the exerted forces of
the human and the robot namely linear, constant and combined
(constant+linear). The latter two are used with two values of the constant
force, weak and strong. Since humans have a small delay in reaction time, a
controller delay of 120ms was observed to be more natural and was added to the
behaviour.

The mean duration of handshakes was 2.2s with 24.8N as the mean sum of forces
measured on the robot hand exerted by the humans. The participants (n=15)
filled out a survey after interacting, rating the quality, human-likeness,
responsiveness, perceived leader, and the perceived personality of the robot
on a 7 point scale. The combined controllers were perceived better than the
constant ones in terms of quality, human-likeness and responsiveness, with a
significant difference between the weak variants. There was no significant
effect in terms of who the perceived leader or follower was. However, it was
observed that in the constant force cases, humans would adjust their force
based on the robot’s, showing that humans tend to follow the force exerted on
their hand. These findings further emphasise the effect of mutual
synchronisation in handshaking. In terms of personality, the stronger variants
of the constant and combined controllers were perceived as more
confident/extroverted, with a significant effect seen between the variants of
the constant controller.

Major Findings. The main commonality among the above-mentioned works is that a
force feedback mechanism is necessary to ensure good grasping since it enables
a mutual synchronisation between the participants. To this end, although there
is no force sensing mechanism as such in the hand designed by Pedemonte et al.
pedemonte2016design  and Arns et al. arns2017design , they still passively
control the closure of the hand based on the deformation, thereby producing a
similar synchronous behaviour. Vigni et al. vigni2019role  observed that the
grip strength had an effect on the perception of the robot’s personality,
which is consistent with the findings of Orefice et al. orefice2016let , from
human-human handshakes. This can help in crafting behaviours to explicitly
yield a personality to the robot, rather than observing such a personality
passively. Additionally, encoding different types of such explicit behaviours
can help the robot switch to adapt to the human interaction partner if
necessary.

##  5 Shaking Motions and Synchronisation between Partners

In terms of shaking, one can easily say that there is a synchronisation which
takes place between the participants while shaking. This is observed by the
studies mentioned above in Sec. 2 as well and is one of the aims of most works
that study the shaking aspect. They also look at reducing the interaction
forces between the robot end-effector and the human hand, which is modelled
using impedance/admittance control by some works.

We divide the works into 3 main categories: Central Pattern Generator (CPG)
and Related Models, Harmonic Oscillator Systems and Miscellaneous Shaking
Systems, as shown below in Table 4.

Central Pattern Generators and Related Models | Harmonic Oscillator Systems | Miscellaneous Shaking Systems
---|---|---
| Beaudoin et al.beaudoin2019haptic  |
Jouaiti et al.jouaiti2018hebbian  | Chua et al.chua2010human  | Avraham et al. avraham2012toward
Kasuga and Hashimotokasuga2005human  | Dai et al. dai2019research  | Karniel et al.karniel2010turing
Melnyk et al.artem2013physical , Melnyk and Henaffmelnyk2016bio  | Mura et al.mura2020role  | Nisky et al.nisky2012three
Papageorgio and Doulgeri papageorgiou2015kinematic  | Wang et al.wang2008modelling ; wang2009hmm  | Pedemonte et al.pedemonte2017haptic
Sato et al. sato2007synchronization  | Yamato et al. yamato2008development  |
| Zeng et al.zeng2012human  |
Table 4: Division of works studying the shaking motion.

###  5.1 Central Pattern Generators (CPGs) and Related Models

Central Pattern Generators (CPGs) hooper2001central  are biologically inspired
neuronal circuits that generate rhythmic output signals. One of the first
works to develop an algorithm for the shaking phase proposed the idea of using
a CPG-like neural oscillator to model the motion of the shoulder and elbow
joints of a robot. They use the torque exerted on the joints as input and
generate an oscillatory trajectory, that can be tuned by adjusting gains to
amplify the input signal to go from active (high gain) to passive (low gain)
kasuga2005human .

One drawback of this method, as pointed out by Sato et al.
sato2007synchronization  is that there are quite a few hand-tuned parameters.
Therefore, they propose a polynomial approximation for the attractor model of
the CPG and subsequently, a model for updating these parameters in an online
fashion. A similar on-the-fly parameter update of the oscillator is done by
Papageorgiou and Doulgeri papageorgiou2015kinematic , who use an impedance
model to help tune the parameters of an internal motion generator modelled as
a Hopf Oscillator hopf1942abzweigung . Although this is not a CPG model, it
shows similar synchronisation properties to produce rhythmic outputs like a
CPG. The output of the impedance model and oscillators are used to update the
oscillator parameters using Direct Least Squares in each iteration using $n$
previous samples of the trajectory. This is unlike previous approaches where
the adaptability of the CPG was an inherent trait.

In contrast, some works directly learn the CPG frequencies to enable a more
online real-time approach. Melnyk et al. artem2013physical  build the CPG
around an online learning mechanism that helps it sync with the human’s
motions directly. Their method dynamically adjusts to changes in the human’s
shaking frequency and synchronise with the human. Like the above-mentioned
works, they too use the joint forces as an input to generate the motions.
However, they only work on controlling a single degree of freedom. A similar
model is proposed again by Melnyk and Henaff melnyk2016bio  using two
different modes, joint positions and accelerations respectively as the inputs.
Like their previous work, they control only one degree of freedom. Along
similar lines, Jouaiti et al. jouaiti2018hebbian  use a similar CPG model and
incorporate dynamic plasticity righetti2006dynamic  in it, making it easier to
synchronise with the handshaking frequency. Moreover, they also propose
learning the amplitude of the oscillations along with the frequency, thereby
being more adaptive than previous approaches.

Major Findings. Overall, CPGs and oscillatory mechanisms synchronise well with
the human’s motion especially those that are dynamically learnt. They can also
fare better than a conventional impedance control approach in terms of
flexibility, naturalness, affinity and kindness of the perceived handshake
kasuga2005human . Additionally, Jouaiti et al. jouaiti2018hebbian  observed
that incorporating plasticity in the CPG can help decrease the energy spent by
the robot as well. One major drawback is the ability of such oscillatory
mechanisms to converge quickly to the required frequency, taking more than a
few seconds even in the fastest cases. This would lead to unnatural
handshaking behaviours that take too long to synchronise. Further research is
required to increase the convergence speed of such mechanisms.

###  5.2 Harmonic Oscillator Systems

Harmonic oscillator models are those that employ harmonic systems, like
spring-damper systems dai2019variable ; mura2020role ; yamato2008development
or simpler sinusoidal motions beaudoin2019haptic ; wang2009hmm ;
wang2008modelling ; zeng2012human  to model the motion during shaking. Some
works use both types of harmonic oscillator models in a two-step predictive
and reactive system chua2010human ; zeng2012human . Most works that employ
harmonic oscillator models use them as reference motions for an impedance
controller used to control the joint motions.

Beaudoin et al. beaudoin2019haptic  incorporate an impedance controller with
different stiffness values using a sinusoidal reference trajectory with
different frequencies and amplitudes along with the grasping model proposed by
Arns et al. arns2017design . Dai et al. dai2019variable  develop a controller
for a custom-made hand that controls the stiffness, viscosity and joint angles
independently. Mura et al. mura2020role  explore different shaking strategies
w.r.t. robotic arm stiffness and their synchronisation with the human during
handshaking. The parameters of the oscillations are estimated quickly in an
online fashion using an Extended Kalman Filter from a fixed number of
preceding frames. They compare three models of varying stiffness, namely high,
low and variable based on the pressure exerted by the human, similar to Vigni
et al. vigni2019role .

Wang et al. wang2008modelling  propose an impedance control mechanism to model
the handshaking mechanism and show how this can be learnt from human
handshakes using least-squares minimisation. The reference trajectory for the
model is generated using an amplitude of 10cm and mixed frequency components
from 0 - 25Hz. Based on the human’s response, the model parameters are fine-
tuned when the human is being passive. When the human is being active, the
model is used to estimate the interaction forces between the human and the
robot and carry out the handshake while being passive. This low-level
controller is expanded on in wang2009hmm  where a high-level controller is
used to generate reference trajectories for it. They first propose a new
method using recursive least squares for a fast online estimation of the
impedance parameters which are fed into an HMM that predicts the intention of
the human i.e. active or passive from haptic data. The impedance parameters
are used, rather than raw force inputs, since they convey the state of the
system.

Major Findings. The use of active impedance control was found to be more
compliant to human motions as compared to simple position-based control
wang2008modelling . Such active behaviours were rated better in terms of
responsiveness than passive ones which also had a significant effect on the
perceived synchronisation mura2020role . Additionally, it was observed that
the human partners would adapt their handshake to the robot’s behaviour, even
when a change was not explicitly mentioned dai2019variable . This further
shows the inherent synchronisation that takes place during handshaking, and
that we as humans infer it from the interaction itself. There are still no
studies that compare the perception of CPG-based shaking motions with harmonic
oscillator motions. For a fair comparison, user studies with the same
interface would be needed to analyse the perceptual differences between these
methods.

###  5.3 Miscellaneous Shaking Systems

Karniel et al. karniel2010turing  describe an experimental framework for a
Turing test of motor intelligence for shaking behaviours. They do so on a 1D
force-controlled haptic stylus, that is presented to a participant. In their
test, the forces driving the participant’s stylus is a linear combination of
forces exerted by an experimenter and different proposed models. They develop
a Model Human-Likeness Grade (MHLG) which measure how human-like the motions
are from the participants’ feedback. Nisky et al. nisky2012three  extend this
to three different versions of the test. The first is a computer vs human
test, where the participant is presented wither with a purely algorithmic
handshake or a purely human handshake. However, this was not sensitive enough
as the participants could almost always guess correctly when a human was
shaking their hand. The second version is where the participants have to
compare an algorithmic handshake with a noisy human one. The third is the
weighted linear combination test proposed by Karniel et al. karniel2010turing
. Unlike the ”pure” test, the authors claim that the latter two variants are
said to be better suited for this purpose.

Avraham et al. avraham2012toward  make use of this ”noise”-based Turing Test
to compare 3 different shaking behaviours. The first being a tit-for-tat model
that initially records the human’s motion passively and then keeps replaying
the same motion, assuming that the human’s motion stays the same again. The
second is a biologically inspired model that simulates a movement that could
be generated by extensor and flexor muscles to ensure a low amount of overall
interaction force. The final is a simple machine learning model that uses
linear regression to learn the parameters of a linear combination of state
variables with corresponding Gaussian kernels. It was found that the tit-for-
tat model and the machine learning model fare similar to each other. They both
fare much better than the biologically inspired model, which the authors argue
can be improved by tuning the hyperparameters. While the proposed Handshaking
Turing tests work for shaking a simple 1D stylus, it still needs to be seen
how well these tests would fare on more complex robotic hardware.

Pedemonte et al. pedemonte2017haptic  introduce a mechanism for remote
handshaking using the hand developed in pedemonte2016design . They develop a
vertical rail mechanism that the hand is mounted on to support a vertical
shaking motion that is passively controlled. The same mechanism is used by
both the participants. This shaking motion along with the forces exerted on
the hand is relayed to the opponent’s hand and rail mechanism to allow a
bilateral handshake to take place remotely. They show that their mechanism
allows for realistic haptic interaction to take place remotely where the
participants can adequately perceive each other’s motions and forces.

##  6 Human Responses to Social Aspects of Robotic Handshaking

In previous sections, we have already talked about how some of the different
works were perceived in HRI experiments. In this section, we expand further
along similar lines and discuss works whose main aim was analysing the
responses in such HRI experiments.

###  6.1 External Factors in Handshaking

While the sense of touch can convey emotional information, there are
additional factors that enhance the perception of these feelings and the
acceptance of the interaction. Below, we discuss some works that explore
different external factors and present their findings to show the importance
of fine-tuning these external factors, which although are subtle, have an
impact on the way a handshake is perceived.

Ammi et al. ammi2015haptic  and Tsalamlal et al. tsalamlal2015affective
performed studies to explore how touch influences the perception of facial
emotions. They used two haptic behaviours (strong and soft) combined with
three visual behaviours namely happy (smiling), neutral and sad (frowning)
which were displayed by the robot’s lips. They test the interactions in three
conditions, haptic-only, visual-only and visuo-haptic. The combination of
visual expressions with a strong handshake showed higher arousal and dominance
over all visual expressions, showing that a sense of touch can enhance robotic
expressions. The majority of the comparisons between visuo-haptic and haptic-
only cases were insignificant, which the authors argue could be due to the
simplistic nature of the facial expression rendering. Vanello et al.
vanello2010neural  explore similar correlations between participant’s
perceptions while shaking hands with an artificial hand made of a plastic
material while being presented with a visual stimulus of either a human or a
robot face. While their experimental design to use fMRI data to understand
such correlations is a useful one, their results cannot be deemed as
conclusive since only three participants take part in their study.

Nakanishi et al. nakanishi2014remote  explore social telepresence with a video
screen equipped with a robot hand below it. They try out different visibility
settings of the participant’s hand in the frame and a robotic hand and compare
a one-way and a two-way teleoperated handshake settings. They build a hand
that resembles a human hand using a soft sponge and gel-like covering and an
artificial skin layer to make the appearance more human-like. It is also
equipped with resistive wires that heat the fingers. The closure of the
fingers is controlled from an external motor with wires connected to the
fingertip that are pulled to extend or close the fingers. First, they look at
different hand settings of the presenter where their hand would either be
visible or out of the video frame (invisible) while shaking. They find that in
the invisible case, the interaction was perceived better. Participants not
only strongly felt that the presenter was in the same room but also had a
strong feeling that they were shaking hands with the presenter in real life.
The authors argue that though the visibility of synchronisation might be
perceived as better, the visibility of the presenter’s hand led to this effect
getting cancelled out, which some subjects reported was due to the duplication
of the hand i.e. seeing two hands at the same time, both the presenter’s and
the robot hand. After establishing the results of one-way handshaking, they
tested out how participants felt when the presented had a robot hand that was
controlled by the participants (two-way handshake). This was tested in two
settings where the presenter’s interaction was either visible or invisible to
the participants. It was seen that the same feelings of physical closeness to
the presenter and of shaking hands in real life were rated higher for the
invisible two-way case, where participants knew that their handshake was being
felt by the presenter. This could possibly be attributed to a perceived
synchronisation of sorts which arises from the participant knowing that their
actions are being perceived by their partner rather than the interaction being
just in a one-way direction.

Jindai et al. jindai2006development  analyse various handshake motions
generated by their model in two ways. They first fit a Bradley Terry Model
bradley1952rank  on paired comparisons of their interactions. Following that,
they use a 7 point bipolar scale to test the participant’s preferences w.r.t.
the handshaking motion, velocity, relief, easiness, politeness and security.
They additionally see how participants respond to voice jindai2007development
and gaze behaviours jindai2011development . The study showed that a delay of
0.1 seconds between the voice and handshake motion of the robot was found
acceptable. It was found that the most preferred behaviour was when the gaze
shifts steadily from the hand while reaching out to the face after contact is
established. The response models ota2014handshake ; ota2015handshake  ware
tested based on the delay between the request and response motions and it was
seen that starting the response a fraction of a second (0.2s to 0.4s) after
the response was preferred. However, a larger delay of 0.6s was less
preferable. The request model in jindai2015handshake  was additionally tried
out with a human approaching the robot from a distance. They experimented with
starting the request at different distances of the human from the robot. Apart
from this, both the request and response models were combined with a similar
transfer function as in jindai2006development  such that the robot requests a
motion if the human doesn’t. This type of behaviour was well perceived by
humans as well (positive feedback on a 7-point bipolar scale).

###  6.2 Influence on the Perceived Image of the Robot

As mentioned in the introduction (Sec. 1), handshaking can impact first
impressions. Therefore in the context of HRI, this can possibly help
strengthen the perception of a robot for further interactions that take place.
This is explored by the works described below, wherein the effect of robotic
handshaking is studied on the specific tasks that a robot has to accomplish.

Avelino et al. avelino2018power  use their previously proposed handshaking
model avelino2017human  to see how a handshake affects a subsequent
interaction wherein the robot needs to perform a navigation task during which,
it would need some assistance by the human. They found that participants who
shook hands with the robot found it to be warmer and more likeable and were
more willing to help the robot for its task. However, they argue that if the
robot had an extremely human-like handshake, participants would not anticipate
it to get stuck in a simple navigational task due to a mismatch between the
behaviour during the experiment and the perceived behaviour according to the
handshake.

Bevan and Fraser bevan2015shaking  perform an experiment to see the effect of
handshaking on negotiations between participants, where one participant
interacts with the other via telepresence on a Nao robot. It was seen that
handshaking improved mutual cooperation, leading to a more favourable
negotiation result for both parties. A haptic feedback for the telepresent
negotiator didn’t have a significant impact. They also found that handshaking
did not affect the degree to which negotiators considered their opponent
trustworthy, which they argue is possibly due to the childlike nature of the
Nao robot.

###  6.3 Distinguishing Ability of Handshakes

In Sec. 2, it was shown that there were observable differences of gender and
context on handshaking. In this section, we discuss some works aim to model
the certain aspects of the participant, like their gender, personality and
mood based on handshakes.

Orefice et al. orefice2016let  propose a model for making distinctions along
the lines of gender and personality (introversion/extroversion), using a set
of 20 parameters relating to acceleration, velocity, duration, pressure etc.
They find that in male-male pairs, more pressure is applied than in male-
female ones. Moreover, they found that female pairs have a longer duration and
a lower frequency but the maximum speed of the oscillations is higher. They
argue that some results could also be due to the hand sizes rather than gender
since most of the females in their study had smaller hands. Coming to
personality, they found that introverts reached a higher speed while shaking
hands and extroverts would exert more pressure. Though they performed similar
experiments in a human-robot handshaking scenario as well, the small number of
participants ($n=8$) makes their results in this aspect inconclusive. Garg et
al. garg2017classifying  similarly aimed to classify people’s personality into
weak and dominant. They use similar information extracted using a custom-made
glove to measure accelerations, Euler angles and polar orientations. The
features are ranked based on the Mutual Information followed by classification
using K Nearest Neighbours, achieving a 75% accuracy. Orefice et al.
orefice2018pressure  perform another longitudinal study with 11 participants
over 16 non-consecutive days, that looks at how pressure variations while
shaking hands reflect the mood of the participants. They use a custom-made
glove with various pressure sensors and an accelerometer worn by both the
participant and a Pepper robot. Before shaking hands, participants had to
declare which mood (Calm, Relaxed, Cheerful, Excited, Tense, Irritated, Sad,
and Bored) best described their current mood. A consistency in the mood was
seen when participants shook hands with a human subject and with pepper, which
was unexpected as one would expect an interaction with a robot to seem
unrealistic or not as human-like. Overall, no significant results were found
for most positive moods, except between ”Calm” and ”Cheerful” where the former
had less pressure observed. In the case of negative moods, ”Bored” handshakes
had lower pressure than ”Excited” and ”Tense”, which have more arousal than
”Bored”. In general, lower pressures were found with moods with lower arousal.
This shows how handshaking can be used as an affective interaction to further
increase the emotional understanding of robots.

###  6.4 Human-likeness of Robotic Handshakes

In the introduction (Sec. 1), we mentioned the importance of having human-like
body movements, which plays an important role in HRI acceptance
chaminade2005motor ; kupferberg2011biological ; mori1970uncanny . Therefore we
analyse works that look at the human-likeness of robotic handshakes and draw
insights that can help shape future experiments.

The social responses to the shaking models proposed by Wang et al.
wang2008modelling ; wang2009hmm  were analysed further in wang2011handshake ;
giannopoulos2011comparison . Both studies perform their experiment on a robot
with a rod as its end effector, in a bar like setting wherein participants
have noise-cancelling headphones playing bar music and having ambient
conversations. In both studies, participants had to perform around 6-7
handshakes in each of the three different handshake settings. First was the
basic algorithm proposed in wang2008modelling , the second was the interactive
model proposed in wang2009hmm  and third was a human operating the robot.
After the handshakes, participants had to rate the human-likeness of the
handshake from 1 (resembling a robotic handshake) and 10 (resembling a human
handshake). In neither of the studies did the participants see the robot. In
the study by Giannopoulos et al. giannopoulos2011comparison , participants
were first blindfolded and led to the robot whereas, in the study by Wang et
al. wang2011handshake , participants had a VR headset on which had a graphical
rendering of a bar with a human model rendered for the robot, who would walk
up and request for a handshake with the virtual hand in the same position as
the robot end effector in the real world. In both the studies, the human-
operated handshake was rated the highest (6.8/10 in both), followed by the
HMM-based handshake (5.9/10 in giannopoulos2011comparison  and 5.3/10 in
wang2008modelling ). The least was the basic handshake proposed in
wang2008modelling  which was rated much lower than the other 2 alternatives
(3.3/10 in giannopoulos2011comparison  and 3.0/10 in wang2008modelling ).
Although the HMM-based handshake was rated closely as the human-operated one,
they both were far from the maximum human-likeness score (10/10), which could
be due to the rod-like end effector used. Having a similar experiment with a
more sophisticated robot hand could yield the results to be more human-like.
However, their studies conclude that having an adaptive handshake that matches
the behaviour of the human ends up being perceived closer to a human
behaviour.

Stock-Homburg et al. stock2020evaluation  study whether a realistic android
robot, that is modelled after a human, with soft silicone skin and
pneumatically controlled joints can pass a hardware version of the Turing
test. They have 15 participants blindfolded who have to interact with a human
and the robot with their hand stretched out twice in a random order, leading
to a total of 4 trials each. Although the robot is built to be as realistic as
possible, majority of the humans (11/15) correctly guessed the hand in the
first attempt itself and by the last handshake, they were all able to guess
the hand correctly. However, they only test a static interaction with the
robot. For a better evaluation, comparing a handshake behaviour rather than
just a static interaction could show better insights into the modelling of a
human-like handshake.

##  7 Discussion

Overall, we have talked about the various works that look into human-robot
handshaking, however, due to the differences in hardware and metrics used
across different studies, it is difficult to come up with a common benchmark
to evaluate these studies. Having said that, some qualitative conclusions can
be drawn from analysing these studies. In general, over the different stages
of handshaking, an element of synchronisation is present. In the reaching
phase, this is seen in terms of the similarity of the requestor’s and
responder’s motions, which is why most works modelling the reaching behaviour
draw from human-human interactions. Although such behaviours can be learnt
using Reinforcement Learning, human trajectories provide a strong prior to
enable the learnt motions to be human-like. Following this, synchronisation in
the grasping phase can be observed by matching the strength of the partner and
can additionally affect the perceived personality of the robot, which
highlights the affective nature of the interaction. Finally, the shaking phase
is where the main element of synchronisation can be explicitly measured with
the interaction forces between the hands. This depends directly on how well
the shaking motion adapts to the that of the partner, leading to low levels of
interaction forces as the synchronisation gets better. Though synchronisation
is a major element of handshaking, in reality, it is difficult to be
completely in sync, due to differences in hand shape and size, mental states
etc. Therefore, a leader-follower situation can arise in the different stages
as well, which could reflect on various personal attributes of the interaction
partners.

Figure 3: Modified Conceptual Framework for Human-Robot Handshaking. (The
proposed suggestions are shaded in grey.)

Although there is a considerable amount of work on the topic, there are still
some gaps in the current state of Human-Robot Handshaking research. Based on
what we have already discussed above and their pitfalls, we propose the
following suggestions/open areas for further research on Human-Robot
handshaking.

Suggestion 1: From the perspective of a robotic handshake, making use of
contextual cues would be effective in having a successful impact of the
handshake. As described in Sec. 2 and 6.3, different social contexts and moods
have an effect on the handshake. Being able to detect such cues additionally
requires further research in other fields like emotion recognition, intent
recognition, etc. Some works try and estimate the mood/personality via the
handshake orefice2018pressure ; wang2009hmm , but there is no explicit context
detection in place, say for example by detecting it from facial expressions,
or possibly from physiological data, and correspondingly using the insights
from Sec. 2 for fine-tuning the handshake.

Suggestion 2: Developing better social robotic interfaces that have a force
sensing mechanism and performing closed-loop control can be more expressive,
as seen in ammi2015haptic ; tsalamlal2015affective ; vigni2019role . Currently
most works do not implement proper grasping control (as shown in Table 3),
which is key for capturing the expressive ability of handshakes to its
maximum. Additionally, the human-likeness of an interface is just as important
for it’s perception. This can be seen in giannopoulos2011comparison ;
wang2008modelling  where even though a human was controlling the robot having
a rod-like end effector, this mode of shaking only got a human-likeness score
of 6.8/10 by the participants. Even sophisticated mechanisms, like the Android
robot used in stock2020evaluation  which has a soft skin-like layer and heated
palms, are still easily distinguished from a human’s hand. One workaround
could be to use sensing gloves, like by Orefice et al. orefice2018pressure ,
which could help bridge the gap between a sophisticated interface and social
robots.

Suggestion 3: One important, yet relatively difficult task is that of
combining the different phases. For a more human-like perception, a proper
transitioning would be required between each of the different phases. Only a
few studies christen2019guided ; jindai2008handshake ; mura2020role ;
pedemonte2017haptic ; yamato2008development  look into combining pairs of
different phases but still do not implement an end-to-end behaviour. There is
still work that needs to be done to achieve a complete handshaking behaviour.
Along with this, the termination of a handshake is an equally important
criterion to make the interaction more socially acceptable. Current works
neither take a smooth separation into account nor do they analyse the effects
of it. A prolonged handshake or an untimely termination can possibly be
perceived as unnatural and can affect the subsequent interaction
nagy2020effects . Therefore an end-to-end handshaking behaviour should take
this into account as well.

Suggestion 4: Given that one of the main use-cases of handshakes in shaping
first impressions is in business cases, the effect of robotic handshaking in
such cases hasn’t been properly explored. This is especially important given
the use of social robots as front-line employees jeffrey2016meet . Bevan and
Fraser bevan2015shaking  study a part in a negotiation context. They look at
the impact of robotic handshaking on the impression of the negotiation partner
who teleoperated the robot. Moreover, they conduct their study with a Nao
robot, which can come across as very childlike and not be taken as seriously
in such settings.

While suggestions 2 and 4 are still subjective, suggestions 1 and 3 are areas
that can be objectively improved and incorporated to improve existing methods
not just for Human-Robot handshaking but would be applicable towards other
similar physically interactive behaviours as well. With these two suggestions,
a modified framework of Human-Robot handshaking is shown in Fig. 3 (the
suggested aspects are shaded in grey). Here the main importance is given to
contextual modelling, which influences parameters like strength, speed etc. by
adapting them accordingly.

##  8 Conclusion

Handshaking is a versatile non-verbal interaction which plays important roles
in social settings. In this paper, we first draw insights from human-human
handshaking regarding timing, trends in the grip strength and the
synchronisation of shaking. We then explore the different phases of
handshaking, namely reaching, grasping and shaking, while observing a common
aspect of synchronisation between the phases. We finally discuss how
handshaking affects the way the robot is perceived and propose some directions
for future work. However, one thing to keep in mind is that handshaking is
just one in so many physical interactions all of which vary over different
cultures, age groups, geographic locations, contextual settings etc. To this
end, learning different physically interactive behaviours, such as hand-
claps/high-fives, fist bumps, or a combination of different touch-based
interactions, would help improve the perception of the robot. Being able to
distinguish and learn such new physically interactive behaviours on the go,
building a skill library of sorts, rather than just a single one like
handshaking, could improve the sociability of a robot.

##  9 Acknowledgements

The authors would like to thank Dorothea Koert, Niyati Rawal and Xihao Wang
for their useful comments that helped make the paper better.

##  10 Compliance, Funding, and Conflict of Interest

Compliance with Ethical Standards: The authors declare that there are no
compliance issues with this research.

Funding: This research was funded by the Interdisciplinary Research Forum
(Forum Interdisziplinäre Forschung - FiF) at the Technical University of
Darmstadt, the Association of Supporters of Market-Oriented Management,
Marketing, and Human Resource Management (Förderverein für Marktorientierte
Unternehm-ensführung, Marketing und Personalmanagement e.V.), the Leap in Time
Foundation (Leap in Time Stiftung), and the Center for responsible
digitization (Zentrum Verantwortungsbewusste Digitalisierung - ZEVEDI).

Conflict of Interest: The authors declare that they have no conflict of
interest.

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