IEEE Technology and Society Magazine - March 2018 - 26

The uncanny valley has been
attributed to a mismatch between
user expectations and robot actions.

Designing Robots to be Predictable for Humans
One might easily assume that for a smooth humanrobot interaction it is sufficient to have a smart robot,
programmed to understand and react to the needs of its
human partner in real time. This is certainly true in a situation in which all actions are defined a priori, for
example when a swivel-arm robot on an assembly line
perpetually repeats the very same movement. This is
not the case when robots interact with humans in more
dynamic, unconstrained situations that demand a mutual exchange of information and meaning-making, something that has long been investigated for interpersonal
communication in the field of Language and Social
Interaction [42]. For example: if a pedestrian wants to
cross the road, a self-driving robot car must be capable
of inferring the person's intent by analyzing body direction or gestures and thus stop automatically. But beyond
that, the car also has to send some message to inform
the pedestrian of its intention to stop or to continue if
stopping is considered more dangerous. As a result of
this implicit dialogue, the person can cross the road
safely - or wait.
The alternative to this situation is to adopt an ultrasafe strategy such as stopping whenever there is a
pedestrian in view, with disadvantageous outcomes in
terms of efficiency. In general situations however, especially with humans naïve to robot's functions or whenever there are more than only two potential outcomes of
an HRI (contrary to the binary decision stop/move on), it
is crucial for a robot to proactively communicate its
imminent actions in order to reduce uncertainty on the
part of its human. Designing robots to be considerate of
people therefore also means designing robots to satisfy
the basic human longing for clarity, control, and predictable events. To reach this goal, robots not only need to
be able to anticipate human intent, but at the same
time need to give people a chance to anticipate the
robot's behavior as well.
In the humanities and social sciences it is long
known that - with a few exceptions - events, tasks,
and agents that are characterized by high ambiguity and
unpredictability often are evaluated as particularly
uncomfortable and even may cause anxiety [17], [18],

26

sometimes intensified by an actual or perceived inability to adapt to the current situation [19]. In early experimental investigations, people have been found to show
a significant preference for predictability in situations of
potential physical threat, realized, for example, by
means of preliminary warning signals [20]. Low predictability and controllability of animal movements repeatedly have been shown to correlate with the widespread
fear of spiders [21], [22]. And even the much discussed
uncanny valley effect in robotics, a negatively valued
state of creepiness elicited by humanlike robots of
high realism [23], has been attributed to a mismatch
between user expectations and robot actions and thus
an inherent ambiguity and low perceived predictability
of android characters [24].
In the realm of HRI, a comparably new branch of
empirical studies has been dealing with legible and predictable motion design for physical interactions in collaborative workplace settings, with most results in
strong support of the importance of mutual anticipation
between humans and machines. To be clear about the
terms, predictability in this context is typically defined
as a targeted outcome state once a robot's goal is clear
to the interacting person, whereas legibility refers to a
robot's easily "readable" behavioral cues (e.g., its
motion trajectory) that allow the person to build an
expectation of a robot's intended goal in the first place,
thus making the robot predictable [25]-[28]. In several
studies in which industrial robots and test persons collaboratively performed pick and place tasks, Dragan
and colleagues [26], [27] showed that the better people
were able to predict a robot's next action, the more
comfortable they felt while interacting with the robot.
This is very much in line with other research that
revealed a positive relation between legibility of robot
motion and people's perceptions of safety [29] as well
as people's trust in autonomous agents [30].
Yet, a highly legible and predictable motion design
seems not only to influence how positively people evaluate an HRI, but also at the same time how efficiently a
task can be carried out by a human-robot team. People
and robots have been shown to need significantly less
time for joint task completion when the human partner
was able to predict the robot's imminent action early.
This held true even if the legible motion design actually
resulted in the robot taking longer to execute its part of
the task [26]. This somewhat counterintuitive efficiency
effect can be explained by the test persons' increased
ability to coordinate their own actions with the robot at
an earlier stage. In practice, designing a robot's motion
for optimal legibility means that the robot doesn't follow
the most direct path, e.g. for grasping an object or
approaching a person, but favors a curved trajectory by
which the direction of the target location in many cases

IEEE Technology and Society Magazine

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march 2018



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