Robots operating in public spaces, such as autonomous vehicles, will necessarily collect images and other data concerning the people and vehicles in their vicinity, raising privacy concerns. Common conceptions of privacy in robotics do not include the challenges of many-to-many surveillance where fleets of many individual robots collect data on many people during operation. Technologists, legal scholars, and privacy researchers recommend such technologies fulfill the reasonable privacy expectations of society, but there is no standard method for measuring privacy expectations. We propose a method informed by Contextual Integrity Theory for identifying societal privacy expectations for autonomous vehicle-collected data and codifying the contextual expectations as norms. We present a study (n=600) that identifies twelve distinct norms, which are made up of contextual factors such as the subject of data collection and the data use. In a model for tolerance of autonomous vehicle data collection, we find that both contextual factors related to the data processing and factors related to the individual are significant predictors.

Physical Touch from a Robot Caregiver: Examining Factors that Shape Patient Experience

Handwriting learning is a long and complex process that takes about ten years to be fully mastered. Nearly one-third of all children aged 4-12 experiences handwriting difficulties and, sadly, most of them are left to fight them on their own, due to the scarcity of tools for the detection and remediation of such difficulties. Building on state-of-the-art digital solutions for automated handwriting assessment and the training of specific handwriting-related skills, in this article we discuss requirements, rationale, and architecture of a system for handwriting training, which relies on a social robot as a mediator agent, offering personalized training and suggestions. The system is envisioned to operate autonomously and to support long-term interactions via personalization. Preliminary validation of the system in an experiment with 31 children showed its potential not only for autonomously guiding handwriting training sessions, but also for its inclusion in the teachers' practice.

A game-based approach for evaluating and customizing handwriting training using an autonomous social robot

The supervision component is the binder of a robotic architecture. Without it, there is no task, no interaction happening, it conducts the other components of the
architecture towards the achievement of a goal, which means, in the context of a collaboration with a human, to bring changes in the physical environment and to update the human partner mental state. However, not so much work focus
on this component in charge of the robot decision-making and control, whereas this is the robot puppeteer. Most often, either tasks are simply scripted, or the supervisor
is built for a specific task. Thus, we propose JAHRVIS, a Joint Action-based Human-aware supeRVISor. It aims at being
task-independent while implementing a set of key joint action and collaboration mechanisms. With this contribution, we intend to move the deployment of autonomous collaborative robots forward, accompanying this paper with our open-source code.

Nothing About Us Without Us: a participatory design for an Inclusive Signing Tiago Robot

Robots are increasingly involved in tasks that require them to navigate social spaces shared with humans.
Following social norms is considered a key requirement for such robots to ensure their acceptance and long-term use. This paper focuses on delivery robots as these often encounter problems in their operational areas–in this case a busy university campus–
when they find their way blocked by people and they cannot move on towards their goal destination. We explored automated cue triggering to resolve this situation autonomously without the help of remote operators. Eighty-three pedestrians participated
in a real-world study using a delivery robot. Four different cues were tested for their perceived politeness and ambiguity. The four different cues differed in the presence or absence of an instruction and the presence or absence of a justification for the request to let the robot pass as well as source orientation within the justification, which was either internally (self-)
directed or externally (user-) directed. The results reveal a complex picture. Overall, a positive effect of verbal instructions in comparison to staying mute on social attributions to the
robot was found. Contrary to our expectations, there was no significant difference in politeness between the different requests. Participants’ willingness to let the robot pass was
positively correlated with perceived politeness, and negatively correlated with ambiguity of the requests.

Polite and Unambiguous Requests Facilitate Willingness to Help an Autonomous Delivery Robot and Favourable Social Attributions

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

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

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

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

Risk is traditionally described as the expected likelihood of an undesirable outcome, such as a collision for an autonomous vehicle. Accurately predicting risk or potentially risky situations is critical for the safe operation of an autonomous vehicle. 
This work combines use of a controller trained to navigate around individuals in a crowd and a risk-based decision-making framework for an autonomous vehicle that integrates high-level risk-based path planning with a reinforcement learning-based low-level control. We evaluated our method using a high-fidelity simulation environment. We show our method results in zero collisions with pedestrians and predicted the least risky path, time to travel, or day to travel in approximately 72\% of traversals. This work can improve safety by allowing an autonomous vehicle to one day avoid and react to risky situations.

Comparison of Vehicle-To-Bicyclist and Vehicle-To-Pedestrian Communication Feedback Module: A Study on Increasing Legibility, Public Acceptance and Trust

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.

Providers-Clients-Robots: Framework for spatial-semantic planning for shared understanding in human-robot interaction

Deploying robots with the ability to autonomously
navigate in the environment of a clinic necessitates carefully de-
signing their verbal and non-verbal behaviour in order to avoid
irritating, worrying or even endangering users, bystanders
and passersby. We describe a user-centred approach used to
design, implement and evaluate an interaction concept for
mobile robotic assistants for a clinic environment, resulting in
two robotic assistant prototypes and a generalized multimodal
interaction framework applicable to a variety of autonomously
navigating robotic agents. We present the steps of the design
process and intermediate findings, the final system components
and experiment results showing good acceptance of the inter-
action concept. A clip of the two robots in action can be found
under https://short.dfki.de/ro-man

Iterative User-Centric Development of Mobile Robotic Systems with Intuitive Multimodal Human-Robot Interaction in a Clinic Environment

Using robot manipulators in contexts where it is undesirable or impractical for humans to physically intervene is crucial for several applications, from manufacturing to extreme environments. However, robots require a high degree of intelligence to operate in those environments, especially if they are not fully structured. 
Teleoperation compensates for this limitation by connecting the human operator to the robot using human-robot interfaces. The remotely operated sessions can also be used as demonstrations to program more powerful autonomous agents.
In this article, we report a thorough user study to characterise the effect of simple vibrotactile feedback on the performance and cognitive load of the human user in performing teleoperated. grasping and manipulation tasks. The experiments are performed using a portable and affordable bilateral teleoperation system that we designed, composed of a Leap Motion sensor and a custom-designed vibrotactile haptic glove to operate a 4-fingered robot hand equipped with 3-axis force sensors on the fingertips; the software packages we developed are open-source and
publicly available. Our results show that vibrotactile feedback improves teleoperation and reduces cognitive load, especially for complex in-hand manipulation tasks.

Underline digital video library

Physical Touch from a Robot Caregiver: Examining Factors that Shape Patient Experience

A game-based approach for evaluating and customizing handwriting training using an autonomous social robot

Nothing About Us Without Us: a participatory design for an Inclusive Signing Tiago Robot

Polite and Unambiguous Requests Facilitate Willingness to Help an Autonomous Delivery Robot and Favourable Social Attributions

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

Nonverbal cues expressing robot personality - A Movement Analysts Perspective

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

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

Comparison of Vehicle-To-Bicyclist and Vehicle-To-Pedestrian Communication Feedback Module: A Study on Increasing Legibility, Public Acceptance and Trust

Providers-Clients-Robots: Framework for spatial-semantic planning for shared understanding in human-robot interaction

Iterative User-Centric Development of Mobile Robotic Systems with Intuitive Multimodal Human-Robot Interaction in a Clinic Environment

Creating Informative and Successful Human Robot Interaction in a Real-World Service Environment

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