Hello, my name is Benjamin Camblor and I am going to present the work conducted with my
colleagues Nassim Benhabib, David Daney, Vincent Padois and Jean-Marc Salotti on Task-Consistent
Signaling Motions for Improved Understanding in Human-Robot Interaction and Workspace Sharing.
The integration of cobots into industrial environments heavily depends on their acceptance
by operators and the potential for symbiotic work between these two partners.
Currently, most works in cobotics has focused on robots and their behavior while interacting
with an operator to ensure safety.
However, it has also been shown that humans can be a source of danger to themselves.
Actually, task complexity and variability may lead to hazardous situations for the operator.
Previous work has focused on the different means of communication that can be used to
secure interaction in industrial robotics.
The use of interfaces, light signals and alarms are among the most commonly used solution.
In this work, it is proposed to use the cobot's motion at a lower level to communicate subconsciously
with the operator and increase his or her performance and safety.
The use of cobots motion must be done taking into account that in the industrial environments
motions are already mobilized to perform the tasks assigned to the cobot.
In most cases, not all degrees of freedom of the later are mobilized to perform the
main task.
It is proposed here to superimpose signaling motions on the main, task-oriented, motion
using the unused degrees of freedom to generate informative motion to be shared with the operator.
In this work, two types of signaling motions are presented: first, an explicit signaling
motion.
It should be related to the task and not interfere with it, and it must also keep the human involved
in the task and never put him out of the loop.
Finally, this motion should support the operators decision making and action taking.
The second signaling motion is a diffuse one.
It should be application independent, and is expected to cover a wide range of contexts
and user expertise.
Finally, it must be explicit and universal to avoid any ambiguity between users.
To assess the effect of signal emotion on both performance and security, a double within-subject
study was conducted.
This study was based on an experiment involving both the use of an explicit and a diffuse
signaling motion implemented on two different Franka Emika Panda robots.
The experiment consisted of a simulated wood cutting task, which requires a joint effort
between a cobot named Cobot 1 and an operator.
In addition, the behavior of the person was also studied when he or she was moving near
a second robot named Cobot 2, which shared the same workspace of the participant.
For this purpose, we have implemented two signaling motion one explicit and the other
diffuse.
The first motion is named postural encoding motion.
It is implemented at the same time as the main task of the robot, which is to simulate
wood resistances.
It tilts more or less depending on whether the resistance is high or low.
The second motion is named breathing motion, it is implemented while the robot is in an
idle state.
When performing this behavior, the robots makes slight movements from right to left
to indicate its activity.
The experiment required participants to perform 90 trials divided into 30 trials per modality
of the woodcutting simulation task with Cobot 1 producing 3 types of resistance, low, medium,
or high in a controlled random order.
During the first 30 trials, the Cobot 1 did not perform any postural encoding motion.
For the next set of trials, the robot performed its signaling motion while assisting the human
and the experimenter did not explain the significance of the cobot's behavior beforehand.
Finally for the last modality, the cobot still perform the explicit signaling motion, and
the meaning of the motion is explained to the participant.
Between each set of 30 trials, the participant has to go and answer the SART questionnaire
which asked about his or her subjective situation awareness to evaluate his own perceived safety.
Furthermore, every 10 trials, he must answer a diversionary question about his perceived
effort.
Answering these questions means that he has to move around the room and necessarily pass
near the Cobot 2 performing its breathing motion.
Overall each participant passed near the Cobot 2 21 times.
Several objective and subjective measures were collected during this experiment like
time, forces and distance to Cobot 2, but also the SART questionnaire score.
Statistical analysis were carried out according to the modalities of each of the signaling
motion, and also of the resistances produced during the experiment.
Regarding trial competition times, the participants needed less time to complete the trials when
the robot was implementing the postural encoding motion regardless of the resistance.
Then, for the force, the applied forces tend to decrease from one modality to the other
for the low and mean resistances.
The opposite effect occurs for high resistances.
In other words, their strengths increase from one modality to the next.
This results could be explained by the fact that participants were able to estimate better
their starting force required for a trial.
About the distance to Cobot 2, participants walked closer to it when it was motionless
rather than when it was active or breathing.
On the other hand, there were no significant differences between the active and breathing
behaviors.
This results suggests that participants were equally wary of Cobot 2 whether it was active
or breathing.
Finally, an improvement in situation awareness was measured when the robots applied the signaling
motions.
Participants rated themselves as having better situation awareness when Cobot 1 was performing
the signaling motion.
This may reflect the fact that even if the experimenter did not explain the meaning,
the participant guessed it on his own and felt safe.
In conclusion, our experiment has shown a positive effect of both signaling motion on
performance and safety.
Further analysis revealed that this result were not only the result of a learning effect
during the experiment, but also of signaling motions.
Participant scores improved on all measure when the robots were performing their motions.
In particular, they would have fed the participants mental models in order to either perform the
task better, that means to be more efficient, and also to act more safely towards the cobot.
The continuation of this work would be to generalize this approach and to systematize
the means of implementing new type of signaling motion.
Finally, I would like to thank my colleagues for participating in this work.
Thank you for your attention, and I will be happy to answer any question about this work.