#  The Effect of Robot Skill Level and Communication in Rapid, Proximate
Human-Robot Collaboration

Kin Man Lee<EMAIL_ADDRESS>Georgia Institute of
TechnologyAtlantaGeorgiaUSA ,  Arjun Krishna<EMAIL_ADDRESS>Georgia
Institute of TechnologyAtlantaGeorgiaUSA ,  Zulfiqar Zaidi<EMAIL_ADDRESS>Georgia Institute of TechnologyAtlantaGeorgiaUSA ,  Rohan Paleja
<EMAIL_ADDRESS>Georgia Institute of TechnologyAtlantaGeorgiaUSA ,  Letian
Chen<EMAIL_ADDRESS>Georgia Institute of TechnologyAtlantaGeorgiaUSA
,  Erin Hedlund-Botti<EMAIL_ADDRESS>Georgia Institute of
TechnologyAtlantaGeorgiaUSA ,  Mariah Schrum<EMAIL_ADDRESS>Georgia
Institute of TechnologyAtlantaGeorgiaUSA and  Matthew Gombolay
<EMAIL_ADDRESS>Georgia Institute of
TechnologyAtlantaGeorgiaUSA

(2023)

###### Abstract.

As high-speed, agile robots become more commonplace, these robots will have
the potential to better aid and collaborate with humans. However, due to the
increased agility and functionality of these robots, close collaboration with
humans can create safety concerns that alter team dynamics and degrade task
performance. In this work, we aim to enable the deployment of safe and
trustworthy agile robots that operate in proximity with humans. We do so by 1)
Proposing a novel human-robot doubles table tennis scenario to serve as a
testbed for studying agile, proximate human-robot collaboration and 2)
Conducting a user-study to understand how attributes of the robot (e.g., robot
competency or capacity to communicate) impact team dynamics, perceived safety,
and perceived trust, and how these latent factors affect human-robot
collaboration (HRC) performance. We find that robot competency significantly
increases perceived trust ($p<.001$), extending skill-to-trust assessments in
prior studies to agile, proximate HRC. Furthermore, interestingly, we find
that when the robot vocalizes its intention to perform a task, it results in a
significant decrease in team performance ($p=.037$) and perceived safety of
the system ($p=.009$).

collaborative robot, agile task, voice communication, varied skill, trust,
perceived safety

††journalyear: 2023††copyright: rightsretained††conference: Proceedings of the
2023 ACM/IEEE International Conference on Human-Robot Interaction; March
13–16, 2023; Stockholm, Sweden††booktitle: Proceedings of the 2023 ACM/IEEE
International Conference on Human-Robot Interaction (HRI ’23), March 13–16,
2023, Stockholm, Sweden††doi: 10.1145/3568162.3577002††isbn:
978-1-4503-9964-7/23/03††price: 15.00††ccs: Human-centered computing Empirical
studies in collaborative and social computing††ccs: Computer systems
organization Robotics

##  1\. Introduction

The deployment of collaborative robots (i.e., “cobots”) holds the promise of
increasing productivity and enhancing safety. These robots are expected to
collaborate in proximity with human workers as opposed to the caged robots
typically encountered within today’s manufacturing industry (Villani et al.,
2018). This paradigm shift allows for cobots to work with human workers in new
ways, such as mixed-initiative teaming (Mingyue Ma et al., 2018; Gombolay et
al., 2017; Jiang and Arkin, 2015), to increase productivity. In mixed-
initiative settings, human workers and robots are co-located in a shared
workspace and must dynamically decide who should accomplish shared tasks. This
online reasoning in human-robot collaboration provides a more natural and
fluent interaction scheme than a strict, pre-planned allocation of work
(Angleraud et al., 2021).

However, achieving fluency in mixed-initiative teaming is challenging as
identifying precisely who should accomplish a specific task is computationally
challenging for human and robot alike (Ravichandar et al., 2020), and
inferring human intent online from dynamic motion remains an open research
problem requiring domain expertise for algorithm design (Ravichandar and Dani,
2017). Enabling robots to convey intent is likewise an unsolved design problem
(Dragan et al., 2015; Paleja et al., 2022). Furthermore, as robots gain
functionality and increased agility, close collaboration can create safety
concerns that may alter the team dynamic, affecting human behavior and
performance due to latent variables, such as perceived safety and trust
(Lasota et al., 2017, 2014). In this work, we are interested in understanding
how attributes of the robot (e.g., skill-level or capacity to communicate)
impact team dynamics, perceived safety, and perceived trust, and how these
latent factors affect human-robot collaboration (HRC) performance. Insight
into these relationships will inform how to best design robots that work in
proximity to humans.

We are specifically interested in the development of agile robotics for human-
robot collaboration. Agile robots are those that are, by definition, capable
of demonstrating agile behaviors in dynamic settings (Dong et al., 2020; Yang
et al., 2022). These robots are designed to closely emulate human-level
abilities across tasks such as sports (Yang et al., 2022; Büchler et al.,
2022), cooking (Kormushev et al., 2010; Sugiura et al., 2010; Fonseca et al.,
2019), and mobile manipulation (Peng et al., 2020; Kulić et al., 2016). Agile
robotics holds the promise of low-latency decision-making and high-speed robot
maneuvers. Collaboration in proximity to humans presents a novel challenge for
agile robotics, as we would like to take advantage of high-speed manipulation
for effective human-robot collaboration.

Fast moving collaborative robots in proximity to humans could potentially
induce fear, anxiety, and stress because the robot’s intention may be unclear,
the behaviors exhibited by the robot could be unpredictable, and the risk of
the human coming to harm is objectively higher (Arai et al., 2010; Salvini et
al., 2022; Murashov et al., 2016; Beauchamp et al., 1991). Additionally,
humans have less time to react, exacerbating the concerns of safety and trust
(Arai et al., 2010; Story et al., 2022; Butler and Agah, 2001; Shibata and
Inooka, 1998).

In this work, we choose the domain of table-tennis doubles for our human-
subjects experiment in order to gain insights into the design of agile,
collaborative robots. Here, we do not follow the typical table-tennis doubles
rule, where agents must alternate hitting the ball; instead, we frame our
domain as a mixed-initiative human-robot teaming task (i.e., either player can
return the ball). Our modified game introduces the opportunity for players to
reason about who should hit the ball, whether and how to communicate intent,
and a possible physical collision if the human and robot are not in sync. Our
task meets the following criterion: (1) Low-reaction time decisions from
humans and robots, and (2) Shared collaboration space where either agent can
successfully accomplish the task. In this environment, we evaluate how an
agile robot, varying in skill level and endowed with a deliberative voice-
communication channel is able to team with human end-users. We place specific
emphasis on understanding how the robot’s competence, voice profile, and the
capacity to communicate affect perceived safety, and trust in agile, proximate
human-robot collaboration. We note that we include as an independent variable
the sex of the voice (i.e., male vs. female) due to (conflicting) prior work
in human factors on sex-binary voices for cockpit automation indicating that
the sound of female vs. male voice can have differing impacts on audibility,
perceived assertiveness, and reaction time and quality (Edworthy et al., 2003;
Arrabito, 2009).

While we study these factors in an _ad hoc_ table-tennis doubles task, the
outcomes of this study are potentially applicable to settings where humans and
agile-robots have to work together in a shared space. Applications include
collaborative manufacturing robots, assistive home robots, athletic robots for
sports and recreation, etc. Our work is the first to explore these factors in
an athletic collaborative human-robot task and we hope this work inspires more
research to enable the deployment of safe and trustworthy agile robots that
operate in proximity with humans. We summarize our key contributions as
follows:

  1. (1)

We develop a novel table tennis system for safe and dynamic HRC by extending
probabilistic movement primitives (Gomez-Gonzalez et al., 2016) for motor
control and incorporating a safety subsystem that uses unintrusive, camera-
based human pose tracking.

  2. (2)

We conduct a user study and find that fast, verbal communication to convey
intent reduces overall team performance ($p=.037$) and perceived safety of the
robot ($p=.009$).

  3. (3)

We find that participants rarely utilized verbal signals to convey intent to
the robot. Based on a small sample size of user feedback, this suggests that
most felt more comfortable communicating to the robot implicitly with body
motion in high-speed teaming tasks.

Figure 1. Setup of the collaborative table tennis system.

##  2\. Related Work

Proximity in HRC – Cobots are envisioned to be in “cage-free” environments,
sharing a workspace and collaborating closely with human workers (el Zaatari
et al., 2019). As these cage-free systems can prove detrimental due to the
potential for collision, several works have attempted to create safe human-
robot collaboration via algorithmic advancements (Peternel et al., 2017; Luca
et al., 2006; Kulić and Croft, 2007), designing different manipulation
techniques to stop the robot from causing bodily harm in alignment with
industry guidelines (Romanov, 2019). Another approach has been to design
compliant robots to ensure safety in proximate HRC settings (She et al., 2017;
Haddadin et al., 2010; Zinn et al., 2004). Other work has looked into human-
in-the-loop approaches that attempt to understand human intent for safe,
proximate human-robot collaboration (Gabler et al., 2017). However, while such
techniques can produce safe behavior and address planning within a mixed-
initiative teaming paradigm, these approaches do not address the perceived
safety or trust of end-users, which are important factors in achieving high-
performance HRC (Busch et al., 2017). Works in human factors have attempted to
measure perceived safety through physiological signals that can indicate
stress, fear, or anxiety (Pollak et al., 2020; Yamada et al., 1999; Dehais et
al., 2011). Some of this work has been applied to industrial manipulators
(Kulić and Croft, 2007; Arai et al., 2010), though none have been applied in a
fast-paced HRC setting to the best of our knowledge.

Figure 2. Top-down view of the task setup. Balls land in three zones: Robot
(Zone R), Shared (Zone S), and Human (Zone H). The scoreboard on the right is
updated in real-time.

Communication in HRC – Prior work displayed that verbal communication in HRC
affects collaboration as well as users’ perception of the robot (Grigore et
al., 2016; Mavridis, 2015; Thomaz et al., 2016). Studies have shown that
verbal communication between the robot and the user improves subjective and
objective metrics on team performance and users prefer a speaking robot over a
non-speaking one (Nikolaidis et al., 2018; St. Clair and Mataric, 2015a;
Lallée et al., 2013; St. Clair and Mataric, 2015b). Furthermore, (Delft, 2012)
found that autonomous agents who exchanged information regarding their
intention (i.e., deliberative communication (Butchibabu et al., 2016))
collaborated with humans better than those sharing information in regard to
the world state. It has also been suggested that a lack of truthfulness in a
robot’s statement can negatively affect a human’s evaluation of a robot’s
abilities (Nikolaidis et al., 2018). Although these studies produced
interesting findings about robot communication, the corresponding tasks did
not have time pressure, deviating from real-time decision-making.

In our experiment, the robot asserts its intention via a short verbal
statement in a male/female voice to inform the participant that it will
attempt to hit an incoming table tennis ball. The verbal communication is done
in real-time by an agile robot conducting a high-speed striking maneuver.
Deviating from prior literature, we additionally allow the human to
communicate intent with the robot (bidirectional communication), creating a
realistic, rapid, proximate HRC scenario where the human must reason about
received communication, whether to communicate intent, which agent should hit
the ball, and the ball trajectory, all within a short duration.

Agile Robot Manipulation – Agile robotic systems that work alongside human
beings is a long-standing goal in manufacturing, where high-speed robotic
maneuvers are safely conducted with humans collaborating in proximity with the
robot. In this way, manufacturing is able to take advantage of a human’s
ability to deal with uncertainty and variability while utilizing automation to
its fullest (el Zaatari et al., 2019). However, prior work has noted that
stress, anxiety, and fear levels of a human user around a fast-moving robot
with the capability of causing physical harm are high (Arai et al., 2010;
Salvini et al., 2022; Murashov et al., 2016; Beauchamp et al., 1991; Story et
al., 2022; Butler and Agah, 2001; Shibata and Inooka, 1998), presenting
interesting challenges in producing high-performance HRC. Furthermore, as
producing high-speed robotic maneuvers is a difficult challenge in itself,
poor performance of the robot can adversely affect user trust (Hancock et al.,
2011; Robinette et al., 2017; Ye et al., 2019; Desai et al., 2012). As such,
to address these open problems in studying agile cobots, we study both the
effects of varying skill level and communication on collaboration performance
between humans and agile robot.

We utilize table tennis as a testbed for HRC, as this is a sport requiring
fast reflexes and decision-making (Castellar et al., 2019). In prior work,
robotic systems have been developed to play singles table tennis (Gomez-
Gonzalez et al., 2016; Büchler et al., 2022; Chen et al., 2020b, a, 2022; Ding
et al., 2022; Gao et al., 2020). However, to the best of our knowledge,
systems for doubles table tennis have not been explored.

##  3\. Methodology

###  3.1. Task Description

The participant’s task is to team-up with a 7 degree-of-freedom Barrett WAM
arm to return table tennis balls launched from a Butterfly Amicus ball
launcher, as shown in Figures 2 and 1. To investigate the collaborative
behavior between the human and the robot, we program the launcher to project
balls to three regions: Zone R (the robot zone), Zone S (the shared zone), and
Zone H (the human zone). The robot has the capability to return balls landing
in Zone R and Zone S, and does not attempt to return the balls landing in Zone
H. The human is free to return balls in any region, but those in Zone R are
more difficult as those balls are farther from the subject’s starting position
(displayed in Figures 1 and 2).

The task of returning the balls in Zone S is designed to be a _shared task_ to
evaluate the collaboration between the human and the robot. In this region,
both agents (robot and participant) can comfortably attempt the task. Note, we
modify the standard doubles table tennis rules for our experiment such that
the two players (i.e., the participant and the robot) do not have to
alternate, and the human can decide which balls to attempt. The participants
must start in the “subject home position” (shown as shaded region in Figures 2
and 1) before the ball is launched to ensure consistent starting configuration
and difficulty to reach Zone R.

The setup described meets the two criteria we set: 1) Low-reaction time in
which the human has $\approx 0.6$ seconds to make a decision on whether to
attempt to return a ball or yield it to the robot. 2) Shared Human-Robot
Collaboration space following the definition of mixed-initiative teaming,
where the interaction strategy is flexible, dynamic, and based on agents
contributing to the task that they can do best (Allen et al., 1999). In our
scenario, the subject is incentivized to rely on the robot for balls in Zone R
due to the physical distance and share the Zone S with the robot. The
participant score is displayed on a real-time scoreboard (Figure 2); further
details are in Section 3.5.

###  3.2. Experiment Design

In our study, we seek to explore the impact and interplay of voice
communication and robot skill level on team performance, human perceived
safety, and human trust towards the robot. As such, within our collaborative
task detailed in Section 3.1, we design a study with three factors: (1)
_Communication_ : enabled or disabled, (2) _Robot Voice Sex_ : male or female,
(3) _Robot Skill_ : low and high. We propose a mixed-factorial study with a
2x2x2 design, with Communication and Robot Skill as within-subject factors and
Robot Voice Sex as a between-subjects factor. While a complete within-subjects
design is ideal for exposing participants to all conditions, we chose to
investigate robot voice sex as a between-subjects factor to prevent
participants from being aware that we are investigating sex/gender bias. We
detail the three independent variables below.

##### Communication:

Drawing inspiration from how volleyball players communicate deliberative
intent, we enable the robot to vocally communicate assertive audio signals
such as “ _Mine_ ” or “ _Got it_ ” through a speaker mounted on the robot arm.
The utilization of short phrases is a requirement in agile robotics as intent
must be quickly conveyed due to the low-duration task. We define these Vocal,
Assertive, INtention communication signals as “VAIN” signals for clarity and
brevity on the form of communication used in our study. VAIN signals are
provided in real-time as soon as a launched ball is detected by the vision
system to indicate that the robot will attempt to return the ball. VAIN
communication, when enabled, is used for all balls landing in both Zone R and
Zone S.

##### Robot Voice Sex:

We use a voice generation tool to create audio files for the phrases used in
the communication factor with both a male and female tone to study whether/how
the communication voice impacts the human-robot team’s performance and the
human’s subjective perception of trust and safety. As noted, prior work has
suggested that the sound of female vs. male voice can have differing impacts
on audibility, perceived assertiveness, reaction time, and quality (Edworthy
et al., 2003; Arrabito, 2009).

##### Robot Skill:

We have two levels of robot performance - low and high. The high performance
model is comprised of a well-tuned stroke primitive (detailed in Section
3.3.2) to return balls. The low performance model replaces the well-tuned
primitive with a suboptimal primitive ($\sim$ 50% of the time), which either
misses the ball or does not successfully return the ball over the net. The
success rates of the robot policies are summarized in Table 1.

Prior work has shown that robot skill has a positive correlation with trust on
the robot (Hancock et al., 2011; Robinette et al., 2017; Ye et al., 2019;
Desai et al., 2012). However, most findings have come from cognitive or
physical tasks in virtual environments. Additionally, the impact of a robot’s
skill on perceived safety in HRC is under-explored. Exploring these topics in
a setting with a physical, agile robot on a fast-paced task will lead to
insights that can help in designing more effective cobots.

Below, we detail our research questions under the following themes: Robot
Skill and Deliberative Communication.

####  3.2.1. Robot Skill

As skill is a critical factor in establishing effective collaboration, we are
interested in answering the following questions pertaining to this factor
through our study:

  1. (RQ1)

How does the robot’s skill level impact team performance?

  2. (RQ2)

How does the robot’s skill level impact the perceived safety of human
collaborators?

  3. (RQ3)

How does the robot’s skill level impact human collaborators’ trust on the
robot?

  4. (RQ4)

How does the robot’s skill level impact human initiative in shared regions of
the task?

####  3.2.2. VAIN Communication

We are interested in evaluating how Vocal, Assertive, INtention (VAIN)
communication (i.e., a transparent robot that always announces its intention)
during robotic table tennis motions affect the dynamics of human-robot
collaboration. To this end we ask the following questions:

  1. (RQ5)

Does VAIN communication help increase HRC team performance?

  2. (RQ6)

Does VAIN communication increase perceived safety of the robot in HRC?

  3. (RQ7)

Does communication increase people’s trust on the robot in HRC?

  4. (RQ8)

Does the sex of the robot’s voice impact the perceived safety and trust of the
robot?

  5. (RQ9)

Does VAIN communication increase the robot’s chance of taking initiative
within the shared regions of task?

Figure 3. Overview of our collaborative table-tennis robot system.

(a) Zone R stroke

(b) Zone S stroke

Figure 4. A visualization of the two robot stroke primitives that hits the
balls that land in Zone R and Zone S.

Stroke Type | Skill Level | Hit Rate | Success Rate
---|---|---|---
Zone R | high | 94.36% | 86.40%
Zone R | low | 93.66% | 59.86%
Zone S | high | 86.54% | 64.63%
Zone S | low | 83.89% | 30.29%
Table 1. Performance of robot strokes on balls that land in Zone R and Zone S
for the different skill levels. “Hit” denotes the robot successfully hit the
ball with the paddle. “Success” indicates the ball is successfully returned
over the net.

###  3.3. System Design

Our robotic system has four main components to achieve various levels of robot
skills and ensure safety, as shown in Figure 4. First, Ball Detection enables
the robotic system to “see” and predict the ball trajectory in order to decide
when to hit. Second, if the robot is able to strike the incoming ball (i.e.,
the ball must be in Zone R or Zone S), the Ball Strike Controller utilizes
ProMP (Gomez-Gonzalez et al., 2016) policies to execute the corresponding pre-
trained strikes for the zone. Third, Human Pose Estimation detects the 3D pose
of the human. Lastly, Safe Execution determines if any human pose keypoints
have entered the robot safety region, which leads to an immediate termination
of the robot trajectory to avoid any possible collision between the robot and
the human.

####  3.3.1. Ball Tracking

To accurately detect the table tennis balls, we utilize a multi-camera vision
system consisting of three Stereolabs ZED2 cameras. Classical computer vision
techniques, including background subtraction and color thresholding (from
OpenCV (Bradski, 2000)), are applied to identify the ball pixels within the
image. With this, an estimate of the ball position in the world frame is
calculated based on each camera’s relative position to an AprilTag (Wang and
Olson, 2016) on the ping pong table. All three ball poses are merged by an
Extended Kalman Filter (EKF) (Kalman, 1960; Julier and Uhlmann, 1997) to
produce an accurate fused estimate of the ball. A dynamics model that factors
in drag, gravity, and the coefficient of restitution of bounce is used to
rollout the EKF’s expected future ball trajectory, which is then utilized by
the Ball Strike Controller to determine the right time to execute a stroke.

####  3.3.2. Ball Strike Controller

We trained two probabilistic movement primitives (ProMPs) (Gomez-Gonzalez et
al., 2016) – one for Zone R and Zone S – using ten kinesthetic demonstrations
of successful returns for each. We model the joint position trajectories of
the stroke as a weighted activation over 50 radial basis functions in the
phase space $[0,1]$ and learn the weight distribution that maximizes the
likelihood of the demonstrated trajectories through the Expectation-
Maximization (EM) procedure outlined in Gomez-Gonzalez et al. (2016). The
trained stroke primitives are visualized in Figure 4. While the learned
primitive weights can be conditioned to pass through waypoints based on the
ball trajectories, we execute the unconditioned mean of the learned primitive
due to the possible unsafe waypoints of a conditioned ProMP. Doing so
effectively ensures that the stroke performed by the robot for each zone is
pre-determined, thereby keeping the robot’s behavior predictable and safe for
the purposes of the study.

During our experimentation, the sequence of ball launch locations is
predetermined, providing the robot with access to information about which
region each ball will land. To avoid the participant from obtaining
information about incoming ball trajectories, for each strike, the robot
starts at a common reset pose and moves to the initial pose of the primitive
only once a ball is detected by the Ball Tracking component. The common reset
pose was determined to be a weighted average of the initial poses of the Zone
R and Zone S strokes. Once a ball is detected, the system utilizes the
anticipated ball trajectory to calculate the time of impact and the
approximate paddle position at the time of impact, which is used to trigger an
agile robot table tennis return.

The stroke parameters, such as duration and hit phase were tuned to return as
many balls in each zone as possible. These strokes serve as the _high_ skill
model of the robot. In order to create a _low_ skill model of the robot, we
introduce mistiming to the strokes 50% of the time. This offsetting during the
hit phase causes the robot to poorly return the ball or completely miss. We
report the performance of the robot using these two skill models over all the
ball sequences considered in the experiment trial in Table 1.

####  3.3.3. Human Pose Estimation

We utilize a ZED2 camera for tracking human pose. The ZED software development
kit provides access to a body tracking module that outputs skeleton-based
human keypoints. These keypoints indicate the locations of major joints within
the human body such as the head, shoulders, elbows, hands, knees, and feet. We
use the 18-point 3D pose format (OpenPose (Cao et al., 2019) pose format) in
our system to track the pose of participants. Each 3D keypoint consists of an
X, Y, and Z position of a human joint in the camera frame. These keypoints are
then transformed to world coordinates relative to an AprilTag (Wang and Olson,
2016) on the table, which is used in the safe execution module to avoid the
robot colliding with humans.

####  3.3.4. Safe Execution

All control requests to the robot are routed through the safe execution module
as shown in Figure 4. A fixed 3D unsafe region is created around the robot
based on the reachability range of the robot strokes calculated by the Ball
Strike Controller. The skeleton keypoints from the Human Pose Estimation
module are passed as input into the safe execution module to detect if any
part of the participant’s body enters the unsafe region. If so, any robot
motion in progress is immediately terminated by sending a stop command
directly to the low-level controller and all further control requests are
rejected. Once keypoints are no longer detected within the unsafe region
(i.e., the human is outside of the robot striking area), control requests are
accepted again and the robot is reset to its default joint positions.
Additionally, the maximum end-effector speed of any stroke is limited to 3.5
m/s to ensure safety even if the unlikely scenario of a collision were to
occur.

###  3.4. Metrics

####  3.4.1. Objective Metrics

The team performance metric is the sum of successful returns both the robot
and participant achieve together in a randomized sequence of 30 balls. This
randomized sequence consists of 8 balls in Zone H, 14 in Zone S, and 8 in Zone
R. In order to control the impact of the participants’ prior table tennis
skill to the overall performance score, we measure the participant’s skill
level in performing the described task in a calibration phase where the
participant returns a sequence of 30 balls on their own. We record for each
ball, the zone in which it landed, whether it was hit, and if the ball was
returned successfully. Similarly, in the main experiment, we record for each
ball the landing zone, who attempted to hit it, whether it was hit, and if the
return was successful.

As an additional dependent variable, we create a bidirectional communication
channel by allowing the subject to speak the phrases (“Mine”, “Got it”) to
convey their intent of returning the ball. Here, we observe if robot skill
level or robot communication incentivized the human to communicate their
intent. Note we explicitly state that participant communication towards the
robot is optional and not required for the robot to stop its stroke. The robot
always yields given participant communication, responding with either “ _Okay_
” or “ _Alright_ ”. Due to the latency of speech recognition and the limited
timing allowed in our task, we choose a Wizard-of-Oz approach (Dahlbäck et
al., 1993) to provide a timely response to the participant’s declaration of
intent - a button is pressed by the experimenter to trigger immediate stopping
of the robot movement and provide voice responses.

####  3.4.2. Surveys

We conduct a pre-experiment questionnaire that includes self-reported
demographics - age, gender, and dominant hand in playing table-tennis, mini-
IPIP(Donnellan et al., [n. d.]) to measure the big-five personality factors,
and negative attitude towards robots (NARS)(Syrdal et al., [n. d.]) to measure
the subject’s predisposition towards robot interactions. Each of these serve
as potential confounds within our analysis. After each within-subject
condition (described in depth within Section 3.5), the subject is asked to
fill out a questionnaire assessing their interaction with the robot. This
questionnaire includes the Godspeed questionnaire (Bartneck et al., 2009),
measuring anthropomorphism, likeability, perceived intelligence, and perceived
safety, and includes the trust in automated systems questionnaire (Jian et
al., 2000) to measure perceived trust.

###  3.5. Study Procedure

To assign participants across all conditions uniformly, we sample sequences in
blocks of four samples with an even split of robot voice sex in a pseudo-
random fashion. The sequence order of conditions is fully randomized. The same
seed is used for consecutive blocks of samples where the last 11 samples are
manually assigned to ensure the gender of participants are balanced across
robot voice sex.

For each participant, we start by obtaining informed consent. In the consent
form, we note that our experiment may have deception approved by our IRB
protocol. This hidden deception helps incentivize participants to be more
involved and competitive in the task by deducting 10 cents from the study
compensation for each ball missed irrespective of whether it was the human or
the robot. We further utilize a digital scoreboard showing the current score
alongside the loss in compensation in the current experiment block (Figure 2)
to emphasize the importance of human-robot collaboration. Note, participants
were debriefed about the deception at the end of our study and were paid the
full amount of compensation.

(a) Effect of Robot Skill on the perceived
trust of the system.

(b) Impact of VAIN Communication on
the perceived safety of the system.

(c) Effect of robot voice sex on the perceived safety of the system.

Figure 5. Effect of robot skill, VAIN communication and robot voice on the
perceived trust and safety.

Upon completion of informed consent, we give the participant a pre-experiment
survey to assess self-reported demographics, personality factors, and negative
attitude towards robots. Once participants have completed the survey, we
perform skill calibration for a sequence of 30 balls to access the subject’s
performance on the task without the robot. The subject is allowed a two-minute
warm up period before the calibration task begins. After the calibration
phase, we move into the main study. We begin by providing a description of the
collaborative study and show a demonstration over a sequence of 6 balls with
the high-skill model of the robot. The demonstration provides the participant
with an idea of the robot capability and show that it is safe to step into the
robot’s configuration space to intercept a ball. We additionally demonstrate
the high-skill model of the robot with communication enabled and show how both
the robot and human can declare intent, and we inform the participant that
communicating with the robot is optional. The participant is allowed a warm-up
phase, actively collaborating with the robot under no communication. This
allows the participant to get used to the robot’s presence and minimize the
learning effect in the first sequence. During this warm-up, the ball sequence
order is fixed and evenly distributed across all zones.

For each condition of the 2x2 within-subject block design, we describe whether
the robot has communication enabled and stress that the performance is
independent of previous trials. Information about robot performance is kept
secret from the participant. Each participant will experience a total of four
trials varying across Robot Skill and Communication. The human-robot team will
play a 30 ball sequence for each condition, after which the subject is asked
to evaluate the interaction with the Godspeed (Bartneck et al., 2009) and
Trust in Automated Systems questionnaires (Jian et al., 2000). Upon completion
of the four trials, we debrief the subject about the experiment, detail the
deception/concealment, and obtain consent to use their data.

##  4\. Results

We recruited 46 participants for this in-person IRB-approved study. Each
participant was compensated $20 for completing our study. Four participants
were excluded from our analysis as we experienced issues with the cable
tension of the robot arm, resulting in visibly degraded robot performance
during the experiments. In our results, we report on 42 participants (sex: 20
Male, 20 Female, 2 Prefer not to say) and age ($M=25.43$, $SD=3.5$).

For each measure of trust, perceived safety, team performance, and
anthropomorphism, we create a mixed-effects multiple regression model with
metrics of interest as the dependent variable. The independent variables are
skill, communication, and robot-voice, and we include covariates such as
gender, age, personality factors, calibration score, etc. We then applied an
Analysis of Variance (ANOVA) on each model to answer our research questions.
The mixed-effects model accounts for the repeated measures nature of each
subject completing the 2x2 within-subject blocks. For each model, we report
the transforms used, results from the Shapiro-Wilk test for normality, and the
Levene’s test for homoscedasticity for the independent variables in the
Appendix. We measure significance as $\alpha<0.05$. If the assumptions for
normality or homoscedasticity do not hold, we report a non-parametric
assessment using Friedman’s test followed by an all-pairs Nemenyi test
comparing the within-subject factors of robot-skill and communication, and do
not consider the influence of other covariates.

###  4.1. Perceived Trust

Across all conditions of gendered voice, skill, and VAIN communication, we
compared how participants rated their levels of trust after completing each
block comprising a sequence of 30 balls. We find that when the robot performed
poorly, the trust in the robot reduced significantly ($F(1,124)=18.687$,
$p<.001$), as shown in Figure 5(a). This finding concurs with many previous
human-robot interaction studies evaluating trust on the robot with varying
skill levels (Hancock et al., 2011) and addresses (RQ3). However, we find that
VAIN communication did not impact the level of trust on the robot
($F(1,124)=0.463$, $p=.497$), concluding for (RQ7) that communication does not
improve trust on the robot.

###  4.2. Perceived Safety

To investigate (RQ6), we analyze the relationship between VAIN communication
and perceived safety. We observe that this communication significantly
decreased perceived safety ($F(1,124)=7.070$, $p=.009$), as shown in Figure
5(b). We hypothesize that the constant announcement of the robot’s intention
induces more anxiety and agitation, which leads to lower scores within the
perceived safety sub-scale. Additionally, we observe that the sex of the robot
voice was a significant factor affecting participants’ perception of the
robot. On average, the robot equipped with a female voice was perceived safer
than the robot with a male voice ($F(1,38)=7.079$, $p=.011$), as shown in
Figure 5(c), addressing (RQ8). The participant’s gender also influences the
perceived safety score, with male participants rating the robot as safer than
female participants ($F(2,38)=4.709$, $p=.015$). In addressing (RQ2), we find
that the robot skill level is marginally significant for perceived safety,
with a robot with a higher skill level perceived to be safer
($F(1,124)=2.762$, $p=.099$).

###  4.3. Team Performance

For (RQ1), we find that a higher skill level of the robot significantly
improves the team performance ($F(1,123)=69.25$, $p<.001$). Additionally, the
skill of the human, measured by their score on the calibration task, improved
team performance ($F(1,34)=29.929$, $p<.001$). The gender of the participant
also significantly impacts team performance, with male participants having
higher team performance on the task ($F(2,34)=6.314$, $p=0.005$). In
addressing (RQ5), we find that VAIN communication degrades HRC team-
performance ($F(1,123)=4.514$, $p=.037$). We find an interaction effect
between communication and skill also being significant ($F(1,123)=4.0266$,
$p=0.047$), indicating that communication significantly hurts team-performance
when the robot’s skill is low, and affects team performance minimally when
robot skill is high, as visualized in Figure 6(a).

To answer (RQ4) and (RQ9), we perform a non-parametric all pairs Nemenyi test,
as some independent variables failed the Levene’s test. The Friedman’s test
reported that the condition: skill + communication significantly impacts the
number of shared tasks attempted by the robot. This metric indicates that the
participant yielded these tasks to the robot ($\chi^{2}(3)=18.071$, $p<.001$).
Comparing the different conditions with an all pairs Nemenyi test, we find
that when the communication is disabled, robot with higher skill was given
more chances to play the shared task ($p=.034$). For (RQ9) we find that
communication improves the robot’s odds of playing the shared task, as
depicted in Figure 6(b), but the difference is not statistically significant.

Our setup additionally allowed the participant to declare their intent to the
robot verbally, similar to what the robot does in the VAIN communication
condition. Participants rarely made use of this channel to declare their
intent, with a median of 3 declarations in a sequence of 30 balls, with 45.2%
of the participants not even using the channel. We note that this metric did
not correlate with other factors such as calibration score, personality
factors, etc.

(a) Average team score out of the 30 possible points.

(b) Percentage of shared tasks yielded to the robot on average.

Figure 6. Effect of robot skill and VAIN communication on the team
performance.

###  4.4. Anthropomorphism

We observe that the robot with high skill was rated higher in terms of
anthropomorphism ($F(1,120)=4.762$, $p=.031$). We hypothesize this is due to
the fluid nature and legible behavior of successful strokes compared to the
jerky movement exhibited when the robot fails to return balls. Furthermore, we
find VAIN communication was marginally significant in impacting perceived
anthropomorphism ($F(1,120)=3.047$, $p=.083$). A participant’s
conscientiousness score (i.e., their propensity towards organization and
attention to detail) positively impacted how much they anthropomorphized the
robot ($F(1,35)=9.257$, $p=.004$). Conversely, a participant’s rating on the
NARS subscale negatively influenced the participants perception of
anthropomorphism ($F(1,35)=7.084$, $p=.012$). We also see that participants
that are detail-oriented found the robot more anthropomorphic, while
participants that are worried about robots having emotions found the robot
less anthropomorphic.

When communication is enabled, the male/female voice of the robot influences
the perception of anthropomorphism. The robot using a female voice is rated as
more anthropomorphic than the robot with the male voice ($F(1,120)=4.762$,
$p=.031$). The gender of the participant and its interaction with the robot
voice were not significant for anthropomorphism.

###  4.5. Likeability

We analyze the impact of the skill and the communication factor on this
dependent variable through non-parametric tests as this variable failed to
meet the assumption of normality. The Friedman’s test indicated that the
condition: skill+communication significantly impacts likeability
($\chi^{2}(3)=21.171$, $p<.001$). To further compare the different conditions,
we perform an all-pairs Nemenyi test and find that when communication is
disabled, higher robot skill increases likeability ($p=.043$), and with low
robot skill, communication tends towards significance in improving likeability
($p=.075$).

##  5\. Discussion

Existing work has shown that verbal communication increases team performance,
and that users prefer robots with vocal capabilities. Further, it has been
shown that cues which signal specific robot motions (e.g., emitting a sound
for a particular motion or non-verbal cues, such as eye contact, for handover
tasks) improve perceived safety (Arai et al., 2010). However, our results show
that in a dynamic, mixed-initiative setting with proximate collaboration, VAIN
communication negatively affects team performance and perceived safety. We
hypothesize that VAIN communication overloads the subject’s mental resources
given the short timeframe ($\sim$ 0.6 seconds) for task execution (Eppler and
Mengis, 2004). Within this time, participants must reason about the ball
trajectory to determine who should return the ball, process received
communication, and optionally communicate information. Extra information may
make planning more strenuous, resulting in lower team scores on average.

Moreover, VAIN communication, while transparent, provides a signal to the
human just before the robot makes a sudden and fast movement. Such explicit
signals could convey to the user that the robot could be capable of overriding
the person to take an unsafe action, contributing further to the ambience of
physical danger and a decrease in perceived safety. Based on our current
findings, we posit that VAIN communication should not be used in fast-paced
human-robot teaming systems; instead, robots should be primarily defensive and
avoid the appearance of conflicting with a human’s autonomy. However, prior
work (Zhang et al., 2021) studying sound caused by robots found that louder
robots are described as “scary” and “dangerous,” indicating that other sound
profiles of VAIN communication could potentially lead to improvement.

Additionally, we found that VAIN communication from human to the robot was
rarely utilized, suggesting that participants felt more comfortable
communicating to the robot implicitly with their body motion as opposed to
explicitly declaring their intent with voice in fast-paced tasks. While
providing general feedback, some participants noted that talking to the robot
was redundant, as intercepting the ball causes the robot to stop regardless.
As such, we recommend that agile collaborative systems be developed that can
infer human intent from physical motion rather than relying on explicit
communication. Nonetheless, it is possible other forms of voice framework and
design beside VAIN communication could show benefits to using voice
communication in agile collaboration. Humans in agile team sports (volleyball,
tennis doubles, etc.) all incorporate some form of VAIN communication to avoid
conflict, so further exploration is needed to understand how to achieve
effective communication with robots.

Limitations – As indicated in Table 1, the success rate of the high robot
skill level is not perfect. The number of failures vary across each trial,
where the timing of these failures can alter the subject’s perception of the
robot’s skill. While not common, some trials had a series of missed swings at
the beginning of a sequence on a robot with high skill. This can lead the
subject to believe the robot is poor performing, and thus receiving fewer
attempts at the ball for the rest of the sequence. Additionally, our
participants were largely students recruited from a university campus. The use
of deception in the study also creates a high-stakes environment which can
create more pronounced effects on our findings compared to a lax environment;
however, we believe that our current design more accurately represents real-
world HRC scenarios where failure results in negative consequences (e.g., a
physical collision).

Future Work – In future work, we would like to conduct an extension to this
study to identify agile robot system characteristics that lead to increased
perceived safety, perceived trust, and HRC performance. Our system was
designed to conservatively avoid any collision with the participants, and we
plan to improve the system with tighter safety boundaries to allow closer
interactions.

##  6\. Conclusion

In this work, we utilize a modified human-robot teaming, table-tennis doubles
task in a human-subject experiment to gain insights that can inform the design
of agile, collaborative robots. Specifically, we study a mixed-initiative
teaming scenario with quick decision-making from humans and robots in a shared
collaboration space where either agent can successfully accomplish the task.
We investigate how robot voice sex, robot skill level, and deliberative
communication impact perceived safety and trust in agile, proximate human-
robot collaboration. We conduct a human-subjects experiment with 42
participants and find robot skill is positively correlated with trust,
extending skill-trust assessment in prior studies to agile, proximate HRC.
Furthermore, interestingly, we find that VAIN communication degrades human-
robot teaming performance and decreases perceived safety, indicating the need
for more research enabling safe and trustworthy agile robots that operate in
proximity with humans.

###### Acknowledgements.

This research was supported by a gift award by Google to the Georgia Tech
Research Foundation.

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