Italy

Understanding the decisions of AI-driven systems and the rationale behind such decisions is key to the success of the human-robot team. However, the complexity and the ``black-box&#39;&#39; nature of many AI algorithms create a barrier for establishing such understanding within their human counterparts. Reinforcement Learning (RL), a machine-learning algorithm based on the simple idea of action-reward mappings, has a rich quantitative representation and a complex iterative reasoning process that present a significant obstacle to human understanding of, for example, how value functions are constructed, how the algorithms update the value functions, and how such updates impact the action/policy chosen by the robot. In this paper, we discuss our work to address this challenge by developing a decision-tree based explainable model for RL to make a robot&#39;s decision-making process more transparent. Set in a human-robot virtual teaming testbed, we conducted a study to assess the impact of the explanations, generated using decision trees, on building transparency, calibrating trust, and improving the overall human-robot team&#39;s performance. We discuss the design of the explainable model and the positive impact of the explanations on outcome measures.

IEEE RO-MAN 2022

Explainable Reinforcement Learning in Human-Robot Teams: The Impact of Decision-Tree Explanations on Transparency

**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)

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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.

Understanding the decisions of AI-driven systems and the rationale behind such decisions is key to the success of the human-robot team. However, the complexity and the ``black-box'' nature of many AI algorithms create a barrier for establishing such understanding within their human counterparts. Reinforcement Learning (RL), a machine-learning algorithm based on the simple idea of action-reward mappings, has a rich quantitative representation and a complex iterative reasoning process that present a significant obstacle to human understanding of, for example, how value functions are constructed, how the algorithms update the value functions, and how such updates impact the action/policy chosen by the robot. In this paper, we discuss our work to address this challenge by developing a decision-tree based explainable model for RL to make a robot's decision-making process more transparent. Set in a human-robot virtual teaming testbed, we conducted a study to assess the impact of the explanations, generated using decision trees, on building transparency, calibrating trust, and improving the overall human-robot team's performance. We discuss the design of the explainable model and the positive impact of the explanations on outcome measures.

technical paper

Cohesive human-robot collaboration (HRC) for carrying out an industrial task requires an intelligent robot capable of functioning in uncertain and noisy environments. This can be achieved through seamless and natural communication between human and robot partners. Introducing naturalness in communication is highly complex due to both aleatoric variability and epistemic uncertainty originating from the components of the HRC system including the human, sensors, robot(s), and the environment. The presented work proposes the artificial intelligence (AI)-powered multimodal, robust fusion (AI-MRF) architecture that combines communication modalities from the human for a more natural communication. The proposed architecture utilizes fuzzy inferencing and Dempster-Shafer theory for deal with different manifestations of uncertainty. AI-MRF is scalable and modular. The evaluation of AI-MRF for safety and robustness under real-world mimicking case studies is showcased. While the architecture has been evaluated for HRC in industrial settings, it can be readily implemented into any human and machine communication scenarios.

Fuzzy Based Control of a Flexible Bevel-Tip Needle for Percutaneous Interventions

In the literature, perceptual deadband approach
based on the Weber’s law of perception has been employed
to reduce haptic data (i.e., force) rate for a typical teleoperation
application. The approach selects only those samples
for transmission which lie outside the perceptual deadband.
The existing structure of the deadband has linear decision
boundaries and assume that the just noticeable differences
(JNDs) for increasing and decreasing change in a reference force
stimulus are similar. This paper questions this assumption and
searches for an asymmetric perceptual deadband using a datadriven
approach. For the purpose, we design an experimental
setup for collecting human haptic responses (perceived and nonperceived)
of several users for a force range [3, 5] N. A machine
learning classifier inspired from the Weber’s law is trained to
predict the labels of the responses and define a generalized
linear perceptual deadband for each user. The results show
that the generalized deadband do provide different increasing
and decreasing JND (i.e., pointing towards asymmetry in force
perception), but fails to improve the data reduction significantly
as compared to the existing symmetric one

An affordable system for the teleoperation of dexterous robotic hands using Leap Motion hand tracking and vibrotactile feedback

Human-robot communication is key to establishing transparency of robot states, and promoting psychological safety of the people interacting with a robot. Such communication is especially challenging for appearance-constrained robots, as they do not afford to employ commonly-used anthropomorphic modalities such as facial expressions. 
We explore how an appearance-constrained quadruped robot can use LEDs and gestures to generate affective expressions in order to non-verbally communicate. Traditionally, affect has been used as a signalling paradigm in Human-Robot Interaction where expressions are designed with the goal of communicating a particular emotion such as happiness. Instead, this work explores a generalizable mapping from informative messages (e.g., "I am listening") to abstract affective expressions. We modulate the expressions by a model of affect, taking inspirations from Affect Control Theory (ACT). To explore designing the expressions, we first conducted pilot semi-structured interviews consulting stakeholders who regularly operate appearance-constrained robots (N=12) to understand the communication requirements. Then, we designed a set of six affective expressions and evaluated them in a crowdsourced study (N=450). Findings suggest that the expressions can significantly improve effective communication of a robot's awareness and intent, and promote psychological safety of people interacting with it.

User Requirements for a Robot Teleoperation system for General Medical Examination

In the last few years, Extended reality (XR) has enabled novel forms of sensory experiences and social interplay, which can be hardly experienced in real life. 
However, the full potential of XR has not been exploited yet, since vision remains the main interaction modality, and the time- and space- modulation of the sense of self - which could open interesting perspectives in several scenarios - is still largely unexplored.
To pave the path to a multi-modal manipulation of the sense of time and space in immersive XR, in this work we discuss the preliminary outcomes of the first investigation in the visual-tactile domain of two well known perceptual illusions affecting spatial and temporal perception, i.e. Tau and Kappa effects, respectively. 
We compared the effects originated from unimodal stimulation (i.e., only visual or tactile) with the same effects induced by convergent bimodal stimulation (i.e., visual and tactile), delivered to the forearm. 
Results show that both Tau and Kappa effects are affected by the multi-modality of the stimulation, and that the perceptual bias differently affects time- or space- perception based on the modality used for stimulus delivery. Our results, although preliminary, seem to suggest that multimodal perceptual illusions could be a viable solution for time- and space- modulation of the sense of self in immersive XR and advanced social human-robot interaction.

From Task Analysis to Wireframe Design: An Approach to User-Centered Design of a GUI for Mobile HRI at Assembly Workplaces

Domestic robots and agents are widely sold to the grand public, leading us to ethical issues related to the data harvested by such machines. While users show a general acceptance of these robots, concerns remain when it comes to information security and privacy. Current research indicates that there’s a privacy-security trade-off for better use, but the anthropomorphic and social abilities of a robot are also known to modulate its acceptance and use. To explore and deepen what literature already brought on the subject we examined how users perceived their robot (Replika, Roomba©, Amazon Echo©, Google Home©, or Cozmo©/Vector©) through an online questionnaire exploring acceptance, perceived privacy and security, anthropomorphism, disclosure, perceived intimacy, and loneliness. The results supported the literature regarding the potential manipulative effects of robot’s anthropomorphism for acceptance but also information disclosure, perceived intimacy, security, and privacy.

Designing the mobile robot Kevin for a life science laboratory

Robot-initiated touch is a promising mode of expression that would allow robot caregivers to perform physical tasks (instrumental touch) and provide comfort (affective touch) in healthcare settings. To understand the factors that shape how people respond to touch from a robotic caregiver, we conducted a crowdsourced study (N=163) examining how robot-initiated touch (present or absent), the robot’s intent (instrumental or affective), robot appearance (Nao or Stretch), and robot tone (empathetic or serious) impact the perceived quality of care. Results show that participants prefer instrumental to affective touch, view the robot as having greater social attributes (higher warmth, higher competence, and lower discomfort) after robot-initiated touch, are more comfortable interacting with the human-like Nao than the more machine-like Stretch, and favor consistent robot tone and appearance. From these results, we derived three design guidelines for caregiving robots in healthcare settings.

Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study

Social robots are often critiqued as being too ‘robotic’ and unemotional. For affective human-robot interaction (HRI), robots must detect sentiment and express emotion and empathy in return. We explored the extent to which people can detect emotions, empathy and sentiment from speech expressed by a computer system, with a focus on changes in prosody (pitch, tone, volume) and how people identify sentiment from written text, compared to a sentiment analyzer. 89 participants identified empathy, emotion and sentiment from audio and text embedded in a survey. Empathy and sentiment were best expressed in the audio, while emotions were the most difficult detect (67%, 67% and 42% respectively). We found moderate agreement (70%) between the sentiment identified by the participants and the analyzer. There is potential for computer systems to express affect by using changes in prosody, as well as analyzing text to identify sentiment. This may help to further develop affective capabilities and appropriate responses in social robots, in order to avoid ‘robotic’ interactions. Future research should explore how to better express negative sentiment and emotions, while leveraging multi-modal approaches to HRI.

ClassMate Robot: A Robot to Support Teaching and Learning Activities in Schools

Social robots are increasingly used in different services such as education, hospitality, healthcare, and elderly care. These robots are often adopted to provide people with information and guidance, which sometimes can be quite different from people’s expectations. Therefore, it is important to understand whether similar or dissimilar opinions or attitudes of a focal subject held by the user and robot affects the user’s perception of the robot and future adoption intentions. We propose that robot design (positive facial expressions and verbal sentiments) may moderate the effect of attitude dissimilarity on robot perception. It is well-documented in the psychology literature that attitude similarity affects human-human relationships; however, no such study has been undertaken in human-robot interactions. Our results showed that when the robot expressed its views using a neutral facial expression and verbal sentiment, attitude similarity influenced robot perception, i.e., those participants who had similar (vs. dissimilar) attitudes as the robot perceived the robot to be positive (vs. negative). However, when the robot expressed its opinion with a positive facial expression and verbal sentiment, participants judged the robot as positive regardless of attitude similarity between the user and the robot. These results indicate that positive facial expressions and the verbal sentiment of a robot can nullify any negative effect when a user and a robot share very different opinions. 

Positive Facial and Verbal Sentiments of a Social Robot Mitigates Negative User Perceptions in Attitudinally Dissimilar Interactions

To assist and protect citizen communities and police officers, robots have been developed for situational responses (e.g., explosive ordinance disposal). However, the robots used by law enforcement are typically expensive, can be difficult to operate, and do not readily facilitate communication between individuals. Recent research by the authors examined how communication impacts trust between robots and humans in the context of law enforcement. Using a mobile communication robot, law enforcement officers (LEOs) reported high levels of trust because the robot provided near face-to-face interaction using screens, microphones, and speakers. This paper seeks to expand upon the previous findings by discussing public perception, privacy, safety, and ethical considerations as they pertain to communication robots utilized in law enforcement. In the following, the authors explain their primary research thrusts and provide their plan for expanded stakeholder involvement for future research. For the future work, the authors will work with stakeholders to develop ethically grounded communication robots and accompanying education programs that enhance communication, trust, transparency, and accessibility between LEOs and citizens communities.

Socially Assistive Robotics and Wearable Sensors for Intelligent User Dressing Assistance

Social robots that are able to express emotions can potentially improve human's well-being. Whether and how they can learn from interactions between them and human being in a natural way will be key to their success and acceptance by ordinary people. In this paper, we proposed to shape social robot Haru affective behaviors with predicted continuous rewards based on received implicit facial feedback via human-centered reinforcement learning. The implicit facial feedback was estimated with the valence and arousal of received implicit facial feedback using Russell’s circumplex model, which can provide a more accurate estimation of the subtle psychological changes of human user, resulting in more effective robot behavior learning. The whole experiment is conducted on the desktop robot Haru, which is primarily used to study emotional interactions with human in different scenarios. Our experimental results show that with our proposed method, Haru can obtain a similar performance to learning from explicit feedback, eliminating the need for human users to get familiar with training interface in advance and resulting in an unobtrusive learning process.

Explainable Reinforcement Learning in Human-Robot Teams: The Impact of Decision-Tree Explanations on Transparency

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Fuzzy Based Control of a Flexible Bevel-Tip Needle for Percutaneous Interventions

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