Italy

The ankle plays a very large role as an end effector in gait and leg erection. As the number of people with reduced mobility in the ankle joint due to aging and nerve damage increases, rehabilitation and related research are steadily increasing. However, most studies overlook the eversion action that plays an important role in stability. In this study, an intention detection algorithm including the eversion motion was developed, and a multi-channel EMG sensor module was developed and utilized. By moving the ankle in a specific direction, 36 channels of EMG signals were measured to determine the correlation between ankle motion and EMG signals. CNN and ADAM were used for algorithm production, and ankle motion was estimated with high accuracy.

IEEE RO-MAN 2022

Explainability in Collaborative Robotics: The Effect of Informing the User on Task Performance and Trust

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technical paper

**31st IEEE International Conference on Robot & Human Interactive Communication**
 This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.
 
 The conference covers a wide range of topics related to Robot and Human Interactive Communication, involving theories, methodologies, technologies, empirical and experimental studies. Papers related to the study of robotic technology, psychology, cognitive science, artificial intelligence, human factors, ethics and policies, interaction-based robot design and other topics related to human-robot interaction are welcome.
 
 **CONFERENCE THEME:**
 Social, asocial, and antisocial robots
 
 Robots are starting to populate our lives: at work, in our homes, hotels, shops. However, not everybody is comfortable with robots finding a place in society. For example, people may be afraid that robots will replace humans in their jobs. This belief is exacerbated by the media that, on one side, raise expectations of robots’ capabilities, while amplifying fears and worries on the other side.
 
 In the next years, due to the increasing request for interaction with people in social environments, academic research and market analyses predict that robots will become more and more “social”. But, at the same time, robots may incur the risk of being perceived as “asocial” as their pervasiveness in everyday environments may become an obstacle to social interaction between humans. Or they may even be rejected as “antisocial” if researchers do not take care to develop robots that behave according to the customs and habits of the communities where they are expected to operate.
 
 To prevent this from happening, robotic researchers can no longer take the luxury of being mere technicians. They must be capable of addressing all the challenges raised by future robot-populated communities in a multidisciplinary way. They need to share their knowledge with the public to make people aware of what robots really are, what expectations we can have from them, the risks we may incur, and the possible mitigation plans.
 
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31st IEEE International Conference on Robot & Human Interactive Communication
This conference is a leading forum where state-of-the-art innovative results, the latest developments as well as future perspectives relating to robot and human interactive communication are presented and discussed.

In ongoing and consecutive conversations with persons, a social robot has to determine which aspects to
remember and how to address them in the conversation. In the health domain, important aspects concern the health-related goals, the experienced progress (expressed sentiment) and the
ongoing motivation to pursue them. Despite the progress in speech technology and conversational agents, most social robots lack a memory for such experience sharing. This paper presents the design and evaluation of a conversational memory for personalized behavior change support conversations on healthy nutrition via memory-based motivational rephrasing. The main hypothesis is that referring to previous sessions improves motivation and goal attainment, particularly when references vary. In addition, the paper explores how far motivational
rephrasing affects user’s perception of the conversational agent (the virtual Furhat). An experiment with 79 participants was conducted via Zoom, consisting of three conversation sessions. The results showed a significant increase in participants’ change in motivation when multiple references to previous sessions were
provided.

Combining Context Awareness and Planning to Learn Behavior Trees from Demonstration

Wizard of Oz, a very commonly employed technique in human-robot interaction, faces the criticism of being
deceptive as the humans interacting with the robot are told, if at all, only at the end of their interaction that there was in fact a human behind the robot. What if the robot reveals the wizard behind itself very early in the interaction? We built a deep wizard of Oz setup to allow for a robot to play together with a human against a computer AI in the context of Connect 4 game. This cooperative game interaction against a common opponent is then followed by a conversation between the human and the robot. We conducted an exploratory user study with 29 adults with three conditions where the robot reveals the wizard, lies about the wizard, and does not say
anything, respectively. We also split the data based on how the participants perceive the robot in terms of autonomy. Using different metrics, we evaluate how the users interact with and perceive the robot in both the experimental and perceived conditions. We find that while there is indeed a significant difference in the participants willingness to follow robots suggestions between the experimental conditions as well as in the effort they put to prove themselves as humans (reverse Turing test), there isn’t any significant difference in their robot perception. Additionally, how humans perceive whether the robot is tele-operated or autonomous seems to be indifferent to
the robot revealing its identity, i.e., the pre-conceived notions may be uninfluenced even if the robot explicitly states otherwise. Lastly, interestingly in the perception based conditions, absence of statistical significance may suggest that, in certain contexts, wizard of oz may not require hiding the wizard after all.

Nonverbal cues expressing robot personality - A Movement Analysts Perspective

While Wizard-of-Oz (WOz) techniques are frequently used to supplement a machine's abilities, extending this approach to human entities can increase experimental control in studies comparing evaluations of humans and machines in the same role. This article describes the design, implementation, and use of a WOz system for facilitating controlled verbal interactions between children and robot or human interviewers. A collaborative interface allows multiple remote wizards to combine participant responses with interaction-specific goals in order to direct a robot or human interviewer's behavior in a consistent manner. While robot interviewers are controlled directly, human interviewers receive direction through a tablet device or via a projection system concealed from participants. In addition to the system's technical design, we describe the division of responsibilities between wizards and insights from using the system across three extensive interview studies to facilitate a total of 217 interactions with children.

Multi-perspective human robot interaction through an augmented video interface supported by deep learning

As the world surpasses a billion cameras [1] and
their coverage of the public and private spaces increases, the
possibility of using their visual feed to not just observe, but
to command robots through their video becomes an ever more
interesting prospect. Our work deals with multi-perspective interaction, where a robot autonomously maps image pixels from
reachable cameras to positions on its global coordinate space.
This enables an operator to send the robot to specific positions
in the camera with no manual calibration. Furthermore, robot
information, such as planned paths, can be used to augment all
affected camera images with an overlayed projection of their
visual information. The robustness of this approach has been
validated in both simulated and real world experiments.


From Message to Expression: Exploring Non-Verbal Communication for Appearance-Constrained Robots

Augmented Reality (AR) provides a method to superimpose
real-time information on the physical world. AR is
well-suited for complex robotic systems to help users
understand robot behavior, status, and intent. This paper
presents an AR system, Augmented Robot Environment (AugRE),
that combines ROS-based robotic systems with Microsoft
HoloLens 2 AR headsets to form a scalable multi-agent
human-robot teaming system for indoor and outdoor
exploration. The system allows multiple users to
simultaneously localize, supervise, and receive labeled
images from robotic clients. An overview of AugRE and
details of the novel system architecture that allows for
large-scale human-robot teaming is presented below. Studies
showcasing system performance with multiple robotic clients
are presented. Results show that AugRE can scale to 50
robotic clients with minimal performance degradation, due
in part to key components that leverage a recent
advancement in robotic client-to-client communication
called Robofleet. Finally we discuss new capabilities
enabled by AugRE

Visualizing Robot Intent for Object Handovers with Augmented Reality

The development of artificial intelligence has led to significant progress in activity detection; however, a proper evaluation of the activity detection performance is not guaranteed and is still lacking in real-life environments. In this study, to verify the stability and usefulness of activity detection in a real-world setting, we analyzed the activity detection performance for 40 elderly people using a human care robot.

Knowing Where to Look: A Planning-based Architecture to Automate the Gaze Behavior of Social Robots

Learning in universities challenges students to engage in self-directed learning, which requires a high degree of self-motivation while individual support by teachers is limited. Research on social robots has already demonstrated their potential to support students in their learning process. In this paper, we focus on the benefits of adaptivity of a robotic tutor in a higher education scenario. To this end, we conducted a field study over three sessions over the course of a semester and implemented two conditions (adaptive and non-adaptive) of a robotic tutor to support students with exam preparation. After participant learned with both version in random order in the first two sessions, their preferred condition was used in the third session. Our results show that significantly more students preferred to learn with the adaptive robotic tutor. Additionally, participation resulted in significantly better exam performance compared to the average of the course. However, there was no significant difference in the learning experience such as motivation or need satisfaction between conditions.

Towards an automatic generation of natural gestures for a storyteller robot

Current research on the impact of gender appear-
ance in virtual agents and social robots highlights the danger
of transmitting and solidifying existing gender stereotypes. To
investigate gender-stereotyped interaction at public spaces in
dependency of virtual agents gender, we varied the gender of a
virtual agent at a metro station. We used an ethnographic study
approach, combining a two-day behavior observation with semi-
structured interviews with descriptive and qualitative system
log analysis of four weeks. Our results show that topics of
conversation differ in dependency of the virtual agents gender:
the male virtual agent was asked about topics such as brothels,
drugs and alcohol and insulted frequently, while the female one
was asked for relationship status or about flirting.

Familiar Acoustic Cues for Legible Service Robots

When service robots will be employed in private and public spaces, conflicts in human-robot interaction (HRI) might arise. To gain priority and continue its tasks, the robot would benefit from conflict resolution strategies (CRS) that are acceptable and effective. Previous studies have mainly investigated verbal or text-based CRS. As verbal interaction might not suit every application context or robot type, movement-based CRS were investigated. First, four possible implementations were pre-tested in an online study (N = 101). Then two CRS varying in modality (verbal vs. motoric) and assertiveness (submissive vs. dominant) were tested for acceptance and compliance in a lab study (N = 31) and compared for three robot types (humanoid, zoomorphic, and mechanoid). The verbal appeal was the most effective strategy to achieve user compliance. The motoric dominant strategy (moving back and forth) was perceived as most assertive and least polite if applied by the mechanoid cleaning robot but was not more effective than the verbal strategy. These studies provide insights into the influence of robot type on the acceptability and effectiveness of robot conflict resolution behavior depending on the modality.

Continuous and Incremental Learning in physical Human-Robot Cooperation using Probabilistic Movement Primitives (ProMPs)

Increasing demand for automation and recent intact trends have accelerated the employment of robots in manufacturing and service areas. Thus, there is a growing demand for easier and safer operation of robots, both by experts and non-professionals. This paper proposes a detachable smart
robot teaching device with collision prediction to meet such demands. The proposed device is composed of two modules. One is for easy teaching based on a 6D mouse, and the other is for safe operation by measuring the distance to the surrounding obstacles and predicting the collision situation. The teaching module is used only in a teaching phase and can be separated
afterward. A prototype is implemented, and the performance is verified through experiments with a collaborative robot.

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Combining Context Awareness and Planning to Learn Behavior Trees from Demonstration

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