Italy

The use of hand-based gestures has been proposed as an intuitive way for people to communicate with robots. Typically the set of gestures is defined by the experimenter. However, existing works do not necessarily focus on gestures that are communicative, and it is unclear whether the selected gesture are actually intuitive to users. This paper investigates whether different people inherently use similar gestures to convey the same commands to robots, and how teaching of gestures when collecting demonstrations for training recognizers can improve resulting accuracy. We conducted this work in two stages. In Stage 1, we conducted an online user study (n=190) to investigate if people use similar gestures to communicate the same set of given commands to a robot when no guidance or training was given. Results revealed large variations in the gestures used among individuals With the absences of training. Training a gesture recognizer using this dataset resulted in an accuracy of around 20%. In response to this, Stage 2 involved proposing a common set of gestures for the commands. We taught these gestures through demonstrations and collected ~7500 videos of gestures from study participants to train another gesture recognition model. Initial results showed improved accuracy but a number of gestures had high confusion rates. Refining our gesture set and recognition model by removing those gestures, We achieved an final accuracy of 84.1±2.4%. We integrated the gesture recognition model into the ROS framework and demonstrated a use case, where a person commands a robot to perform a pick and place task using the gesture set.

IEEE RO-MAN 2022

A Microsociological Approach to Understanding the Boundary Between Robot Cooperativeness and Uncooperativeness in Human-Robot Collaboration

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### THE CONFERENCE WE KNOW AND WE WANT.
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First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

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.
 
 While being open to any kind of contribution related to Human-Robot Interactive Communication, the RO-MAN 2022 conference will emphasize these aspects of robotic research. How can we create robots capable of being welcomed as our assistants and companions in society while, at the same time, not being perceived as “asocial” or “antisocial?” How can we create robots perceived as facilitators of the natural development of human beings and communities, rather than obstacles?
 
 Contributions from different disciplines, not exclusively related to technological development, will be welcomed as crucial to pave the way to global acceptance of future robots with an informed attitude.
 
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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 this work, the utilization of Dirichlet (periodic
sinc) base functions in DMPs for encoding periodic motions is
proposed. By utilizing such kernels, we are able to analytically
compute the minimum required number of kernels based only
on the predefined accuracy, which is a hyperparameter that
can be intuitively selected. The computation of the minimum
required number of kernels is based on the frequency content
of the demonstrated motion. The learning procedure essentially
consists of the sampling of the demonstrated trajectory. The
approach is validated through simulations and experiments
with the KUKA LWR4+ robot, which show that utilizing the
automatically calculated number of basis functions, the predefined
accuracy is achieved by the proposed DMP model.

Dirichlet-based Dynamic Movement Primitives for encoding periodic motions with predefined accuracy

Data sonification enhance and enrich information understanding with an additional sensory dimension. Sonifica- tion also opens the way to more creative applications, joining arts and sciences. In this study, we present sequences of chords obtained as auditory feedback from the trajectories of a robotic swarm. The swarm behavior is an emerging effect from simple local interactions and autonomous decisions of each robot. The swarm effect can be identified through sonification outcomes in terms of voice leading patterns. Thus, chord patterns represent behavior patterns. The convergence to the target is represented by the convergence to a specific pitch. The swarm decision process is based upon quantum computing. We first present logic gates and their implementations as quantum circuits, describing examples with 2- and 3-dimensional motion of a 3-robot toy swarm. The considered scenarios are ant foraging (2D) and underwater search and rescue (3D). Then, we provide and discuss some examples of the harmonic sequences that can be obtained as feedback from robotic motion.

Virtual Barriers in Augmented Reality for Safe and Effective Human-Robot Cooperation in Manufacturing

Autonomous navigation robots have a considerable potential to offer a new form of mobility aid to people with visual impairments. However, to deploy such robots in public buildings, it is imperative to receive acceptance from not only robot users but also people that use the buildings and managers of those facilities. Therefore, we conducted three studies to investigate the acceptance and concerns of our prototype robot, which looks like a regular suitcase. First, an online survey revealed that people could accept the robot navigating blind users. Second, in the interviews with facility managers, they were cautious about the robot's camera and the privacy of their customers. Finally, focus group sessions with legally blind participants who experienced the robot navigation revealed that the robot may cause trouble when it collides with those who may not be aware of the user's blindness. Still, many participants liked the design of the robot which assimilated into the surroundings.

A Contact-Adaptive Control Framework for Co-Manipulation Tasks with Application to Collaborative Screwing

Clothing is often used as a way to communicate about one’s identity. It can provide information about
characteristics such as gender, age, occupation, and status as
well as more subjective attributes such as trustworthiness and
friendliness. In this paper we report on exploratory research
that we conducted by means of three studies to investigate the
effects of clothing on a Pepper robot. Three outfits that varied in
style and colour were compared with each other and the robot
without clothing. Findings from an online survey suggest that
gender perception but not human-likeness can be manipulated
by clothing. We also found that first impressions on expertise
and likeability may vary with clothing style and may induce
stereotypical job associations. In a second study, we interviewed
experts which were all familiar with the Pepper platform to
obtain a more qualitative perspective on robot clothing. The
interviews made clear that specific features of clothing may
trigger strong associations with, for example, occupations, and
that features such as headdress may make an outfit look
more like a uniform. Finally, we conducted a field experiment
comparing two clothing conditions for a receptionist robot in
a natural setting and found that a robot with uniform appears
to be more engaging. Our findings suggest that robot clothing
may have an effect on first impressions, gender perception, and
engagement in interactions with users in the wild.

Fast Contact Detection and Classification for Kinesthetic Teaching in Robots using only Embedded Sensors

This paper presents a qualitative study that uses video prototypes and interviews to explore older adults' acceptance, needs, and design requirements towards a social robotic application for physical rehabilitation. Our study identified the benefits of applying social robots (SR) in physical rehabilitation. Further, we discovered participants' preference for an anthropomorphic social robot design. The data revealed a desire for social interaction could increase motivation for older adults to engage in an active lifestyle and social robot acceptance. However, participants showed low motivation for technology adoption and negatively anthropomorphize the social robot, which lowers acceptance for their application. This work complements the current user-centered explorations with SR in rehabilitation, and provides considerations for SR design for rehabilitative applications.

Perceptual Deadband for Haptic Data Compression: Symmetric or Asymmetric?

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.

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

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

Agriculture is facing a labor crisis, leading to increased interest in fleets of small, under-canopy robots (agbots) that can perform precise, targeted actions (e.g., crop scouting, weeding, fertilization), while being supervised by human operators remotely. However, farmers are not necessarily experts in robotics technology and will not adopt technologies that add to their workload or do not provide an immediate payoff. In this work, we explore methods for communication between a remote human operator and multiple agbots and examine the impact of audio communication on the operator's preferences and productivity. We develop a simulation platform where agbots are deployed across a field, randomly encounter failures, and call for help from the operator. As the agbots report errors, various audio communication mechanisms are tested to convey which robot failed and what type of failure occurs. The human is tasked with verbally diagnosing the failure while completing a secondary task. A user study was conducted to test three audio communication methods: earcons, single-phrase commands, and full sentence communication. Each participant completed a survey to determine their preferences and each method's overall effectiveness. Our results suggest that the system using single phrases is the most positively perceived by participants and may allow for the human to complete the secondary task more efficiently. The code is available at: https://github.com/akamboj2/Agbot-Sim.

Spatio-Temporal Action Order Representationfor Mobile Manipulation Planning

Today, people are enjoying using teleconference systems
like Zoom or Skype for social communication or for having
drinks through a screen with others via the internet. In
addition, some people have started using embodied systems
called telepresence robots, such as the Beam robot. Some
schools have started using telepresence robots so that
students can attend school. However, in previous studies,
systems have not been compared in terms of social presence
and anthropomorphism, for example, robots compared with
humans. Therefore, we wondered how the presence and
anthropomorphism of such systems affect people. Therefore,
we carried out a web-based experiment and conducted a
one-way ANOVA (smartphone vs. telepresence robot with vs.
without motion). Some people feel that telepresence robots
bring a feeling of presence to remote places. Ironically,
from the results, a video teleconference system using a
smartphone and a telepresence robot did not create a
feeling of presence, but regarding anthropomorphism,
participants felt more of a human-likeness in the video
teleconference system.

Physical embodiment vs. smartphone: which influences presence and anthropomorphism most in telecommunication?

This paper develops a novel framework called Providers-Clients-Robots (PCR), applicable to socially assistive robots that support research on shared understanding in human-robot interactions. Providers, Clients, and Robots share an actionable and intuitive representation of the environment to create plans that best satisfy the combined needs of all parties. The plans are formed via interaction between the Client and the Robot based on a previously built multi-modal navigation graph. The explainable environmental representation in the form of a navigation graph is constructed collaboratively between Providers and Robots prior to interaction with Clients. We develop a realization of the proposed framework to create a spatial semantic representation of an indoor environment autonomously. Moreover, we develop a planner that takes in constraints from Providers and Clients of the establishment and dynamically plans a sequence of visits to each area of interest. Evaluations show that the proposed realization of the PCR framework can successfully make plans while satisfying the specified time budget and sequence constraints and outperforming the greedy baseline.

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Dirichlet-based Dynamic Movement Primitives for encoding periodic motions with predefined accuracy

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