Wow it smells great but I hope it tastes good too
I hope so too. How many carrots was it again?
Sure I can. What kind of music would you like to listen to?
No! No, no music.
Voice controlled interfaces have changed profoundly the way we interact with
technology however they are bound to fail in
interactions with humans. These failures can be critical because they require
costly human intervention and can cause users to lose trust. Most research
approaches start with the assumption of flawless interactions
However as conversation is the main interface visual feedback may not always
be available. Users typically expect an interaction in an orderly fashion.
In this paper we use a set of failures informed by taxonomies of failures in
previous studies HRI. The induced failures represent typical robot
malfunctions that have been reported in human robot interactions. They were
either task oriented such as giving incorrect guidance or failures that
violate social protocols of interaction. All failures had the consequence of
delaying users in completing a task. The failures we used were the following.
In this failure the system simulates losing user engagement and restarts the
interaction. After the failure has occurred the robot becomes responsive
and continues the guidance. A sample user response to the failure follows
We have already started. We have already started
In this failure the robot times speech improperly by
producing an incomplete instruction and after a short delay continues its
utterance
Next take the... Take the what? ...mushroom and add it
In this failure the robot simulates lack of user speech input
by not responding for 20 seconds
What is next I'm done
I'm done what is next? This is not working! I'm done! Take one spoon of red cabbage and put it in the spring roll
In this failure the robot repeats a previous statement by asking
the user to perform again the previous instruction. And finally in this failure
the robot produces an erroneous instruction by asking the user to pick
up and non-existing object. Using the failures above
we asked human users in this corpus to engage in a referential communication
task. Users were instructed how to cook spring rolls by two robots. Spring roll
recipes were broken down to interaction segments and used for instructions. The
recipes were non-trivial so users had to interact with the robots
to successfully complete the task. Interactions were segmented into
dialogue acts and the robots would reply to clarification questions from human users
Users were not instructed on dialogue options but had to interact with the
agents to find out. Participants were led to believe that
robots were autonomous. However to dismiss technical problems we used a
human operator. There is a long-standing effort in bringing human likeness to
robots but how robot embodiment affects users behavioural responses to failures
remains largely unexplored. Smart speakers and human like robot
embodiments for example differ in interactional affordances. From
transactional to relational interactions. While robot embodiment has shown to
typically positively impact human robot interactions it remains to be explored
if this effect persists when the robot fails
The robots we used were the following. An Amazon echo device that used voice as
the only output modality and the furhat robotic head for the human-like robot
embodiment. Time critical situations are difficult to convincingly simulate in the lab
As such most studies have focused on robot failures where little is at stake
In this work we introduce time pressure to half of the participants
using a timer on the screen. We expected that under time pressure the same
failures would have a higher severity on the task. We therefore separated
failures to low severity failures little to no consequence of the failure versus
high severity more severe consequence of the failure respectively. In a user
study with 44 participants we investigated the impact of these two
different factors. One is robot embodiment and the other one is failure
severity
Using this video corpus we define two types of states in the interaction
The first is a no failure state where the user is working on the robot instruction
and the other state is a failure state in which the robot has either not
responded or provided an erroneous instruction. To assess behavioural
responses first we ask human annotators to estimate user affective states in
responses to failures and then we train a random forest classifier to detect
whether a robot failure has occurred
Human annotators estimated whether a conversational failure occurred looking
at short video segments of user responses. These were 2 in lab annotators
and 192 online annotators from a crowdsourcing platform. They watched the
videos from the perspective of robot and coded the following items. Does the person
look confused? Does the person look uncertain? Does the person look
distressed? And how engaged is the person with the robot. Then we attempt to
predict if a failure has occurred in the same video segments using multimodal
input from human users. Behaviour is represented in gaze and speech features
as well as user actions. For example more body movement shows subjects are working
on the instructed task where lack of movement shows hesitation. We found
statistical differences in user engagement with the robot. Specifically
participants appear to be more engaged with the robot when it failed. Failures
need to be resolved before moving on to the task
The main findings are the following. Annotators were better at detecting
states of no failures than failures. When no speech features are presented to
annotators it is much harder to detect states of confusion caused by robot
failures. This further indicates the importance of speech and context
features in failure detection. We also observed that the classifier performed
better at detecting no failures than failures. There was however no
significant difference among robot embodiment in how well the classifier
performed. However getting a closer look at the
models and what features contribute to failure detection we found the following
In most conditions head movement appears to be the most important feature
This we define as an embodied contribution to the ground and is expected to work
well in task oriented dialogues. We however observed that gaze features are
more prominent when the human-like robot is present
While there is a preference for speech features in interactions with the smart
speaker. We saw a clear separation of the failure classes in the case of the
human-like robot embodiment when looking at the participants' gaze
Similarly a clear separation appeared in the speech features across classes with
the smart speaker embodiment. These subtle differences in features indicate that
the models for each embodiment are inherently different. The models in each
embodiment work equally well however different features are activated when the
smart speaker or the human-like robot attempt to resolve failures
Why are these findings important? Robot embodiment affects how users will
interact with a robot and therefore also how they respond to failures. How the
robot looks like is important regarding what features it should pay
attention to when trying to resolve conversational failures with users. This
is important for failure detection mechanisms which may need to consider
the robot's physical design in their failure detection model