Italy

Human-robot interaction (HRI) requires quantitative models of proxemics and trust for robots to use in negotiating with people for space. Hall’s theory of proxemics has been used for decades to describe social interaction distances
but has lacked detailed quantitative models and generative explanations to apply to these cases. In the limited case of autonomous vehicle interactions with pedestrians crossing a road, a recent model has explained the quantitative sizes of Hall’s distances to 4% error and their links to the concept of trust in human interactions. The present study extends this model by generalising several of its assumptions to cover further cases including human-human and human-robot interactions.
It tightens the explanations of Hall zones from 4% to 1% error and fits several more recent empirical HRI results. This may help to further unify these disparate fields and quantify them to a level which enables real-world operational HRI applications.

IEEE RO-MAN 2022

Extending Quantitative Proxemics and Trust to HRI

**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.
 
 [![](https://assets.underline.io/uploads/markdown_image/1/image/af259abe105b3791c498ae1552d75710.png)](https://drive.google.com/file/d/18w73BZPB_JVOgcrcdZDqYpEupwVt4CnU/view?usp=sharing)

A registration is required to access this event page. If you have not already done so, please use the below link to register for the event. 

[Register Here](http://www.smile.unina.it/ro-man2022/welcome-to-ieee-ro-man-2022/)

Registration Is Required

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.

technical paper

Novel human-robot collaboration (HRC) methods need careful validation. This is often achieved with user studies in laboratory environments, which mostly rely on highly individual, complex prototype setups in early design stages. Lately, the lack of replicability imposed by such experiments has vitally been discussed. In this paper, we contribute a benchmark toolkit to compose scalable, synthetic tasks as a unified basis for future efforts towards more replicable research. To this end, we propose modular task boards which cover different domains and HRC scenarios. The design of benchmark tasks with these task boards is supported by a software application which generates structural task models and materials for benchmark problem reproduction by 3D printing. Our experiments show that these tasks can be carried out robustly by robots, hence preventing unintended robot failure and providing a controllable, reliably reproducible setting for user studies.

Effects of Robot Clothing on First Impressions, Gender, Human-Likeness, and Suitability of a Robot for Occupations

Collaborative robots have been designed to perform tasks where human cooperation may occur. Additionally, undesired collisions can happen in the robot's environment. A contact classifier may be needed if robot trajectory recalculation is to be activated depending on the source of robot-environment contact. For this reason, we have evaluated a fast contact detection and classification method and we propose necessary modifications and extensions so that it is able to detect a contact in any direction and distinguish if it has been caused by voluntary human cooperation or by accidental collision with a static obstacle for kinesthetic teaching applications. Robot compliance control is used for trajectory following as an active strategy to ensure safety of the robot and its environment. Only sensor data that are conventionally available in commercial collaborative robots, such as joint-torque sensors and joint-position encoders/resolvers,
are used in our method. Moreover, fast contact detection is ensured by using the frequency content of the estimated
external forces, whereas external force direction and sense relative to the robot's motion is used to classify its
source. Our method has been experimentally proven to be successful in a collaborative assembly task for a number of different experimentally recorded trajectories and with the intervention of different operators.

Affective Robot Behavior Improves Learning in a Sorting Game

Fast changing tasks in unpredictable, collaborative environments are typical for medium-small companies, where robotised applications are increasing. Thus, robot programs should be generated in short time with small effort, and the robot able to react dynamically to the environment. To address this we propose a method that combines context awareness and planning to learn Behavior Trees (BTs), a reactive policy representation that is becoming more popular in robotics and has been used successfully in many collaborative scenarios. Context awareness allows for inferring from the demonstration the frames in which actions are executed and to capture relevant aspects of the task, while a planner is used to automatically generate the BT from the sequence of actions from the demonstration. The learned BT is shown to solve non-trivial manipulation tasks where learning the context is fundamental to achieve the goal. Moreover, we collected non-expert demonstrations to study the performances of the algorithm in industrial scenarios.

Fusion of depth, color and thermal images towards safe interaction between human and robot in an industrial environment towards Digital twins

Social agents have been mostly designed to engage with children one-on-one as tutors or learning peers. Besides this child-robot dyadic interaction paradigm, they have the potential to empower parents to more actively interact with their children. Robot-assisted parent-child interaction could be a sustainable future approach for promoting children’s in-home learning. Motivated by this new design direction, this work takes an iterative design approach to explore how we design triadic interactions through
“lived technology experiences” and interviews. For 3-6 weeks, we deployed and remotely teleoperated a social robot in the homes of 12 families with 3-7-year-old children to engage in a triadic story reading activity with both parent and child for six 25-min sessions. Before and after the deployment, we conducted a semi-structured interview with participants on their triadic interaction experience and desired robot design features. The results of our qualitative analysis show that social robots can improve various aspects of parent-child interaction. We propose design guidelines for robot-assisted parent-child interactions at home, the considerations of participants’ values around technology design, and promotion of their long-term lived technology experiences as critical sources for design knowledge.

Understanding Intention for Machine Theory of Mind: a Position Paper

The goal of this study is to develop an autonomous conversational robot that acquires knowledge about human society in the real world. In this study, we define experience data as image data obtained from a camera (visual information) and dialogue data obtained from a microphone (auditory information). From experience data, the robot acquires knowledge about the co-occurrence between humans and items, that is, whether it is usual or not for humans to have the items. Not only does the proposed system acquire knowledge, but also generates utterances based on acquired knowledge. We have implemented the proposed method on an android, a humanlike robot. We conducted an experiment in which the android was placed in a real-world environment (shopping mall) and interacted with visitors. The percentage of positive responses to the robot’s questions based on the acquired knowledge suggests that this system can acquire knowledge from experience data.

Theory of Mind and Delegation to Robotic Virtual Agents

Patients with neurological gait impairments fear to
fall while performing locomotor tasks like sit to stand, stand to
sit, and level-ground walking without assistance. This prevents
them from participating in daily life. Multi-directional BodyWeight Support (BWS) systems aim to assist training in a safe
environment to overcome this limitation. To ensure the safety
and comfort during training, BWS systems should effectively
and automatically assist patients with forces that support the
desired locomotor task.
Instead of manual switching of controllers through the therapist, we propose a machine learning-based motion onset
recognition model that aims at automatic controller switching
(4 gait related task classes in this paper). In addition to the data
provided by the gait rehabilitation robot FLOAT, data of three
Inertial Measurement Units (IMUs) attached to the sternum
and middle of the outer thighs on 6 healthy participants were
used to predict motion onset of these 4 gait-related tasks.
Four train data sets have been built up from the synchronously
obtained data from the IMUs and the BWS system by dividing
them into observation windows with sizes of 100 ms and 200 ms
and overlap factors of 50% and 90%. From each training
dataset, 108 time-domain features have been extracted, ranked,
and reduced using the Minimum Redundancy Maximum Relevance (MRMR) method from each train set before applying it to
the classifier. The dominant features were applied for training
and comparing four different classifiers. The performance of
the classifiers has been evaluated based on leave one participant
out cross-validation. The ensemble classifier obtained from the
train data set with a window size of 100 ms and 90% overlap
achieved the best performance with an F1 score of 83.7%.

THE LMA12-O Framework for Emotional Robot Eye Gestures

This paper presents a novel quality assessment approach for the prediction of the subjective haptic experience
in time-delayed teleoperation. With the rapid development of haptic technology in remote robot control and virtual reality, new control schemes and hardware systems are developed to
provide high-quality human-in-the-loop teleoperation service. Our subjective experiments indicate that the existing objective quality assessment metrics do not sufficiently correlate with the subjective haptic experience of the users. This gap requires expensive and time-consuming subjective experiments to be conducted. To avoid time-consuming experiments and provide
a fast and accurate subjective experience prediction, we make
an attempt to analyze and explain the mismatch between the subjective and objective haptic signal quality metrics. To this end, extensive subjective experiments and case studies have
been conducted for teleoperation with time delay and haptic data reduction. Based on our experimental results, we propose a quality assessment approach that predicts the subjective quality of experience using multiple objective metrics. For the one-dimensional spring model, the Spearman’s rank order,
Kendall’s rank-order and Pearson’s Linearity correlation coefficient (SROCC, KLOCC, and PLCC) between the predictions of our model and the results of subjective experiment show
remarkable improvement in the correlation between subjective and objective quality assessment.

Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback

Theory of Mind (ToM) refers to the ability to make inferences about other's mental states. Such ability is fundamental for human social activities such as empathy, teamwork, and communication. As intelligent agents come to be involved in diverse human-agent teams, they will also be expected to be socially intelligent in order to become effective teammates. In this paper, we describe a computational ToM model which observes team behaviors and infers their mental states in an urban search and rescue (US\&R) task. Our modular ToM model approximates human inference by explicitly representing beliefs, belief updates, and action prediction/generation using Deep Neural Networks (DNNs). To validate our model we compare its performance to the gold standard of human observers asked to make the same inferences. The ToM model proved superior to the average judgments of human observers on all four tests of inference and better than 90th percentile observers on three of the four. While the learning bias provided by modularizing belief and prediction proved sufficient for the simple inferences tested, substantial refinement will be needed to replicate the complex nuanced chains of inference observed in human social interaction.

Theory of Mind Modeling in Search and Rescue Teams

Humans have an immediate perception of shapes and surroundings through their senses and their cognitive capabilities. This innate ability enables the manual inspection of components in manufacturing environments. Trained inspectors combine their senses and handling skills with bespoke non-destructive testing instrumentation. However, manual inspections can be slow for large and/or complex geometries and prone to human factors. Automated non-destructive testing systems have emerged in recent years, to increase data acquisition speed, part coverage and inspection reliability. These tools work well when the robotic inspection takes place in a well-structured environment and an accurate part model is available. However, precisely registering the position of a part in the robot reference system makes the inspection setup very time-consuming. Furthermore, the geometry of a part may differ from its digital model, spoiling the inspection accuracy. This work introduces a new approach that mimics the human perception capability and gives full manipulation autonomy to robotic sensing applications. We use a single robotized sensor to introduce a fully autonomous single-pass geometric and volumetric inspection of complex parts. Our approach can be used to solve some key challenges in quality assurance for Industry 4.0 and can find applicability beyond robotic non-destructive testing.

Mimicking Human Autonomy in Industrial Robotic Enabled Sensing

Theory of Mind is the process of ascribing mental states to other individuals we interact with. It is used for sense-making of the observed actions and prediction of future actions. Previous studies revealed that humans mentalize about artificial agents, but it is not entirely clear how and to what extent. At the same time mentalizing about humans is often influenced by biases such as an egocentric bias. 
We present a study investigating differences in participants' ToM and their susceptibility to an egocentric bias when observing humans vs robots.
The participants observed an autonomous robot, a controlled robot, and a human in the same scenarios. The agents had to find an object in a laboratory. While watching the agents, participants had to make several action predictions as an implicit measure of ToM, potentially revealing an egocentric bias.
At the end, questions about the agent’s responsibility, awareness and strategy were asked. 
The results indicate that while participants generally performed ToM for all types of agents,
both the scenario as well as the agent type appear to influence participants' likelihood of exhibiting an egocentric bias.

Premium content

Extending Quantitative Proxemics and Trust to HRI

Downloads

Next from IEEE RO-MAN 2022

Effects of Robot Clothing on First Impressions, Gender, Human-Likeness, and Suitability of a Robot for Occupations

Similar lecture

Measuring Visual Social Engagement from Proxemics and Gaze