Italy

We present SLOT-V, a novel supervised learning framework that learns observer models (human preferences) from robot motion trajectories in a legibility context. Legibility measures how easily a (human) observer can infer the robot&#39;s goal from a robot motion trajectory. When generating such trajectories, existing planners often rely on an observer model that estimates the quality of trajectory candidates. These observer models are frequently hand-crafted or, occasionally, learned from demonstrations. Here, we propose to learn them in a supervised manner using the same data format that is frequently used during the evaluation of aforementioned approaches. We then demonstrate the generality of SLOT-V using a Franka Emika in a simulated manipulation environment. For this, we show that it can learn to closely predict various hand-crafted observer models, i.e., that SLOT-V&#39;s hypothesis space encompasses existing handcrafted models. Next, we showcase SLOT-V&#39;s ability to generalize by showing that a trained model continues to perform well in environments with unseen goal configurations and/or goal counts. Finally, we benchmark SLOT-V&#39;s sample efficiency (and performance) against an existing IRL approach and show that SLOT-V learns better observer models with less data. Combined, these results suggest that SLOT-V can learn viable observer models. Better observer models imply more legible trajectories, which may - in turn - lead to better and more transparent human-robot interaction.

IEEE RO-MAN 2022

SLOT-V: Supervised Learning of Observer Models for Legible Robot Motion Planning in Manipulation

**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.

We present SLOT-V, a novel supervised learning framework that learns observer models (human preferences) from robot motion trajectories in a legibility context. Legibility measures how easily a (human) observer can infer the robot's goal from a robot motion trajectory. When generating such trajectories, existing planners often rely on an observer model that estimates the quality of trajectory candidates. These observer models are frequently hand-crafted or, occasionally, learned from demonstrations. Here, we propose to learn them in a supervised manner using the same data format that is frequently used during the evaluation of aforementioned approaches. We then demonstrate the generality of SLOT-V using a Franka Emika in a simulated manipulation environment. For this, we show that it can learn to closely predict various hand-crafted observer models, i.e., that SLOT-V's hypothesis space encompasses existing handcrafted models. Next, we showcase SLOT-V's ability to generalize by showing that a trained model continues to perform well in environments with unseen goal configurations and/or goal counts. Finally, we benchmark SLOT-V's sample efficiency (and performance) against an existing IRL approach and show that SLOT-V learns better observer models with less data. Combined, these results suggest that SLOT-V can learn viable observer models. Better observer models imply more legible trajectories, which may - in turn - lead to better and more transparent human-robot interaction.

technical paper

Theory of Mind is often characterized as the ability to recognize desires, beliefs, and intentions of others. In this position paper, I look at the literature on modeling Theory of Mind in machines and find that, to date, intention is not usually a focus. I define what I mean by intention---choice with commitment---following prior work. Intention has a long history of research in some communities, and I offer one theoretical framework for modeling intention as a starting point. I take inspiration from how children learn intention through joint attention with others and how that leads to Theory of Mind. I argue that though models of machine Theory of Mind need not follow the same learning progression as children, intention is an aspect of Theory of Mind that should be more explicit.

AI bias in Human-Robot Interaction: An evaluation of the Risk in Gender Biased Robots

Social robots have been introduced in different fields such as retail, health care and education. Primary education in the Netherlands (and elsewhere) recently faced new challenges because of the COVID-19 pandemic, lockdowns and quarantines including students falling behind and teachers burdened with high workloads. Together with two Dutch municipalities and nine primary schools we are exploring the long-term use of social robots to study how social robots might support teachers in primary education, with a focus on mathematics education. This paper presents an explorative study to define requirements for a social robot math tutor.
Multiple focus groups were held with the two main stakeholders, namely teachers and students. During the focus groups the aim was 1) to understand the current situation of mathematics
education in the upper primary school level, 2) to identify the problems that teachers and students encounter in mathematics education, and 3) to identify opportunities for deploying a social robot math tutor in primary education from the perspective of both the teachers and students. The results inform the development of social robots and opportunities for pedagogical methods used in math teaching, child-robot interaction and potential support for teachers in the classroom.

Don't Get into Trouble! Risk-aware Decision-Making for Autonomous Vehicles

Haptics is an essential element of interaction between humans and socially assistive robots. However, it is often limited to movements or vibrations and misses key aspects such as temperature. This mixed-methods study explores the potential of enhancing human-robot interaction [HRI] through thermal stimulation to regulate affect during a stress-inducing task. Participants were exposed to thermal stimulation while completing the Mannheim-multicomponent-stress-task (MMST). Findings yielded that human-robot emotional touch may induce comfort and relaxation during the exposure to acute stressors. User affect may be further enhanced through thermal stimulation, which was experienced as comforting, de-stressing, and altered participants’ perception of the robot to be more life-like. Allowing participants to calibrate a temperature they perceived as calming provided novel insights into the temperature ranges suitable for interaction. While neutral temperatures were the most popular amongst participants, findings suggest that cool (4 – 29 ºC), neutral (30 – 32 ºC), and warm (33ºC -36 ºC) temperatures can all induce comforting effects during exposure to stress. The results highlight the potential of thermal HRI in general and, more specifically, the advantages of personalized temperature calibration.


Hot or not? Exploring User Perceptions of thermal Human-Robot Interaction

Constructs intended to capture social attitudes and behaviour towards social robots are incredibly varied, with little overlap or consistency in how they may be related. In this study we conduct exploratory analyses between participants' self-reported attitudes and behaviour towards a social robot. We designed an autonomous interaction where 102 participants interacted with a social robot (Pepper) in a hypothetical travel planning scenario, during which the robot displayed various multi-modal social behaviours. Several behavioural measures were embedded throughout the interaction, followed by a self-report questionnaire targeting participant's social attitudes towards the robot (social trust, liking, rapport, competency trust, technology acceptance, mind perception, social presence, and social information processing). Several relationships were identified between participant's behaviour and self-reported attitudes towards the robot. Implications for how to conceptualise and measure interactions with social robots are discussed.

Does what users say match what they do? Comparing self-reported attitudes and behaviours towards a social robot

Emotions have always played a crucial role in human evolution, improving not only social contact but also their ability to adapt and react to a changing environment. In the field of social robotics, providing robots with the ability to recognize human emotions through the interpretation of non-verbal signals may represent the key to more effective and engaging interaction. However, the problem of emotion recognition has usually been addressed in limited and static scenarios, by classifying emotions using sensory data such as facial expressions, body postures, and voice. This work proposes a novel emotion recognition framework, based on the appraisal theory of emotion. According to the theory, the
expected person’s appraisal of a given situation depending on their needs and goals (henceforth referred to as “appraisal information”) is combined with sensory data. A pilot experiment was designed and conducted: participants were involved in spontaneous verbal interaction with the humanoid robot Pepper, programmed to elicit different emotions in various
moments. Then, a Random Forest classifier was trained to classify positive and negative emotions using: (i) sensor data only; (ii) sensor data supplemented by appraisal information. Preliminary results confirm a performance improvement in emotion classification when appraisal information is considered.

Assessing Emotions in Human-Robot Interaction Based on the Appraisal Theory

As robotic systems become more interactive and complex, there is a need to standardise interfaces and simplify development processes. This is particularly pertinent in the field of manufacturing, where human-robot collaboration is on the increase, but where standards and proprietary software are key barriers to deployment and adoption. In this article we present the ROSIE Adapter, a generalpurpose, modular adapter developed in ROS designed to support the creation and connection of industry-ready digital twins. Together with our previous work on the modular CSI digital twin framework, we demonstrate how the ROSIE Adapter creates a versatile “plug-and-play” interface that simplifies the development of new robotic processes, and improves accessibility to novice users. Furthermore, the adaptor supports integration of intuitive interface devices, such as speech and augmented reality interfaces, which enable more natural collaboration. We describe the adaptor and its use in two real-world applications, demonstrate the ease of use via a three-day hackathon event, and provide results showing the faithfulness of the arising digital twins to their connected physical systems.

ROSIE: A ROS adapter for a Modular Digital Twinning Framework

To assist human users according to their individual preference in assembly tasks, robots typically require user demonstrations in the given task. However, providing demonstrations in actual assembly tasks can be tedious and time-consuming. Our thesis is that we can learn the preference of users in actual assembly tasks from their demonstrations in a representative canonical task. Inspired by prior work in economy of human movement, we propose to represent user preferences as a linear reward function over abstract task-agnostic features, such as movement and physical and mental effort required by the user. For each user, we learn the weights of the reward function from their demonstrations in a canonical task and use the learned weights to anticipate their actions in the actual assembly task; without any user demonstrations in the actual task. We evaluate our proposed method in a model-airplane assembly study and show that preferences can be effectively transferred from canonical to actual assembly tasks, enabling robots to anticipate user actions.

Human-Guided Goal Assignment to Effectively Manage Workload for a Smart Robotic Assistant

Managing robot workloads in human robot teams is critical for efficient team operation. If robots are overloaded with work, then they will miss deadlines and force humans to take on extra work. This paper presents a framework for a robot to assess its own workload based on an initial goal assignment. The robot does this by generating task and motion plans and computing the probability of missing deadlines due to the possibility of delays in task execution. A branch and bound based search is used to generate task and motion plans by minimizing task execution effort. The robot presents a diverse set of task and motion plans to the humans to offer multiple different options. Humans can either approve a plan or provide guidance to reduce the workload by either relaxing deadlines or removing goal(s) assigned to the robots.

Who’s on First? The Impact of the Proactive Interaction on User Acceptance of the Robotized Object

Due to advances in technology, various types of existing products can be robotized. The interaction design between the robotized objects which are the robotized products and users has become an emerging issue since how to interact affects user acceptance of the new products. Because the robots can perceive, recognize and assist the users, robotized objects can assist users by reading the context and guessing their intentions in advance, or can assist users in response to users’ clear requests. In order to explore the effective interaction strategy of robotized object, we conduct 2 × 2 mixed-participant experiment. As a result, participants were more surprised by and felt positive emotion toward robotized objects with proactive interaction than with reactive interaction. In addition, the robotized object with proactive interaction was evaluated as more intelligent and easier to use than that with reactive interaction. The influence of interaction strategies on satisfaction of robotized objects was mediated by surprise and positive emotions. Furthermore, the robotized object with proactive interaction was evaluated as more positive when the user’s intention was expressed only by action cue than when it was expressed by both verbal cue and action cue.

Timing-Specified Controllers with Feedback for Human-Robot Handovers

Vision plays an important role in motion planning for mobile robots which coexist with humans. Because a method predicting a pedestrian path with a camera has a trade-off relationship between the calculation speed and accuracy, such a path prediction method is not good at instantaneously detecting multiple people at a distance. In this study, we thus present a method with visual recognition and prediction of transition of human action states to assess the risk of collision for selecting the avoidance target. The proposed system calculates the risk assessment score based on recognition of human body direction, human walking patterns with an object, and face orientation as well as prediction of transition of human action states. First, we investigated the validation of each recognition model, and we confirmed that the proposed system can recognize and predict human actions with high accuracy ahead of 3 m. Then, we compared the risk assessment score with video interviews to ask a human whom a mobile robot should pay attention to, and we found that the proposed system could capture the features of human states that people pay attention to when avoiding collision with other people from vision.

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SLOT-V: Supervised Learning of Observer Models for Legible Robot Motion Planning in Manipulation

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