Italy

Modern robotics heavily relies on machine learning and has a growing need for training data. Advances and commercialization of virtual reality (VR) present an opportunity to use VR as a tool to gather such data for human-robot interactions. We present the Robot Interaction in VR simulator, which allows human participants to interact with simulated robots and environments in real-time. We are particularly interested in spoken interactions between the human and robot, which can be combined with the robot&#39;s sensory data for language grounding. To demonstrate the utility of the simulator, we describe a study which investigates whether a user&#39;s head pose can serve as a proxy for gaze in a VR object selection task. Participants were asked to describe a series of known objects, providing approximate labels for the focus of attention. We demonstrate that using a concept of gaze derived from head pose can be used to effectively narrow the set of objects that are the target of participants&#39; attention and linguistic descriptions.

IEEE RO-MAN 2022

Benefits of an Interactive Robot Character in Immersive Puzzle Games

**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.

Modern robotics heavily relies on machine learning and has a growing need for training data. Advances and commercialization of virtual reality (VR) present an opportunity to use VR as a tool to gather such data for human-robot interactions. We present the Robot Interaction in VR simulator, which allows human participants to interact with simulated robots and environments in real-time. We are particularly interested in spoken interactions between the human and robot, which can be combined with the robot's sensory data for language grounding. To demonstrate the utility of the simulator, we describe a study which investigates whether a user's head pose can serve as a proxy for gaze in a VR object selection task. Participants were asked to describe a series of known objects, providing approximate labels for the focus of attention. We demonstrate that using a concept of gaze derived from head pose can be used to effectively narrow the set of objects that are the target of participants' attention and linguistic descriptions.

technical paper

This paper proposes a novel framework for robotic
manipulation tasks, exploiting the Human-Robot
Collaboration (HRC) potential. The framework integrates two
adaptive controllers to i) modulate robot compliance in
contact with the environment along constrained directions,
and to ii) enable human guidance through touch when a
manual intervention is needed. To demonstrate the potential
of the proposed framework, we consider a collaborative
screwing task. In this example application, the operator is
in charge of placing the screws on the table and following
the instructions on a graphical user interface. The robot,
after identifying the position of the screws through an
online human pose-tracking system, performs the screwing
using the proposed controller. The human operator can
adjust the screwing position of the robot using the
adaptive interface at anytime if the position accuracy
through vision is insufficient. We first experimentally
evaluate the operation of the proposed controller and
demonstrate its performance in comparison to the classical
impedance control. Next, the overall system is evaluated in
a collaborative (human and robot) setting.

Task sharing with the humanoid robot iCub increases the likelihood of adopting the intentional stance

Existing approaches to direct robot-to-human handovers are typically implemented on fixed-base robot arms, or on mobile manipulators that come to a full stop before performing the handover. We propose "on-the-go" handovers which permit a moving mobile manipulator to hand over an object to a human without stopping. The on-the-go handover motion is generated with a reactive controller that allows simultaneous control of the base and the arm. In a user study, human receivers subjectively assessed on-the-go handovers to be more efficient, predictable, natural, better timed and safer than handovers that implemented a "stop-and-deliver" behavior.

I Can’t Believe That Happened! Exploring Expressivity in Collaborative Storytelling with the Tabletop Robot Ha

Children diagnosed with autism spectrum disorder (ASD) typically work towards acquiring skills to participate in a regular classroom setting, such as attending and appropriately responding to an instructor’s requests. Social robots have the potential to support children with ASD in learning group-interaction skills. However, the majority of studies that target children with ASD’s interactions with social robots have been limited to one-on-one interactions. Group interaction sessions present unique challenges, such as the unpredictable behaviors of the other children participating in the group intervention session and shared attention from the instructor. We present the design of a robot-mediated group interaction intervention for children with ASD to enable them to practice the skills required to participate in a classroom. We also present a study investigating differences in children's learning behaviors during robot-led and human-led group interventions over multiple intervention sessions. Results of this study suggest that children with ASD's learning behaviors are similar during human and robot instruction. Furthermore, preliminary results of this study suggest that a novelty effect was not observed when children interacted with the robot over multiple sessions.

A Social Robot for Anxiety Reduction via Deep Breathing

Learning to recognize new objects in real time in unconstrained environments presents significant challenges for robotic platforms. We present a meta-learning solution to this problem as well as a registered image and events dataset to facilitate work in this domain. Our solution uses interactive motion to isolate the object, and motion-based saliency (from events) to select relevant keypoints from a high-resolution RGB image. Salient keypoints are then passed to a meta-learner to classify the object type. We show that using our interactive isolation and keypoint selection approach, we outperform existing techniques by 6-20%.

Step-by-Step Task Plan Explanations Beyond Causal Links

Explainable robotics refers to the challenge of designing robots that can make their decisions transparent to humans. Recently, a number of approaches to task plan explanation have been proposed, which enable robots to explain each step in their plan to humans. These approaches have in common that they are based on the causal links in the plan. We discuss problems with using causal links for plan explanation. Particularly, their inability to distinguish enabling actions from requiring actions can lead to counter-intuitive explanations. We propose an extension that allows for making this relevant distinction and demonstrate how it can be applied to create a robot that explains its actions.

Robot Vulnerability and the Elicitation of User Empathy

In this work, the issue of simplicity and low weight of a prosthetic hand versus its dexterity is addressed through a “minimal anthropomorphic” approach, which focusses on trying to replicate solely those features of the natural hand that contribute most to its dexterity, while sacrificing features that contribute less. In this regard, the design of a larger, heavier hand that was previously developed using this approach, but that was intended for teleoperation applications rather than as a prosthesis, is modified to satisfy the mechanical requirements for a prosthetic hand. The design, development, and evaluation of the first exploratory prototype prosthesis, designated UM-PRO-I, is described in detail. The new hand incorporates two fingers and a thumb, with eight joints and five independent degrees of freedom actuated using embedded linear DC motors. The main innovative features are the implementation of a substantial, coupled abduction / adduction motion of the index and middle fingers, and the use of an optimally inclined 90o hinge joint at the base of the thumb. The hand weighs less than 300 g, and has the capability to execute a wide variety of human-like grasps. The dexterity of the prototype is explored also through the conduction of a standard clinical dexterity test, carried out with the support of an input / output data glove developed specifically for the kinematic configuration of the prototype hand.

Prototype Development of a Prosthetic Derivative of the Minimal Anthropomorphic Artificial Hand

In this paper, we present a user-selectable control unit to make complex robot systems easier to operate. Search and rescue robots are primarily used for obtaining information and manipulating dangerous objects or supporting emergency forces in dealing with crisis situations. Based on hazardous mission scenarios in which mobile robot systems are confronted with complex manipulation and manoeuvring tasks (e.g., leakage of contaminants), new methods and concepts in the area of robot decision making, sensor data presentation and control concepts were developed to facilitate the handling and operation of assistance robots. The flexibility of the control concept approach is intended to increase the confidence of the emergency services and to provide intuitive operation of the assistance robots for every user. An intuitive control and stable communication for both control commands and feedback from the robot itself increase trust in the robotic system and its acceptance by the operators. These developments have been investigated in field trials with different types of robots and with network communication constraints.

Intuitive HRI Approach with reliable and resilient wireless communication for rescue robots and first responders

It is challenging to grasp numerous objects with varying sizes and shapes with a single robot hand. To address this, we propose a new robot hand called the “F3 hand” inspired by the complex movements of human index finger and thumb. The F3 hand attempts to realize complex human-like grasping movements by combining a parallel motion finger and a rotational motion finger with an adaptive function. In order to confirm the performance of our hand, we attached it to a mobile manipulator - the Toyota Human Support Robot (HSR) and conducted grasping experiments. In our results, we show that it is able to grasp all YCB objects (82 in total), including washers with outer diameters as small as 6.4 mm. We also built a system for intuitive operation with a 3D mouse and grasp an additional 24 objects, including small toothpicks and paper clips and large pitchers and cracker boxes. The F3 hand is able to achieve a 98% success rate in grasping even under imprecise control and positional offsets. Furthermore, owing to the finger’s adaptive function, we demonstrate characteristics of the F3 hand that facilitate the grasping of soft objects such as strawberries in a desirable posture.

F3 Hand: A Versatile Robot Hand Inspired by Human Thumb and Index Fingers

Behaviours of robot swarms often take inspiration from biological models, such as ant colonies and bee hives. Yet, understanding how these behaviours are actually perceived by human users has so far received limited attention. In this paper, we use animations to represent different kinds of possible swarm motions intended to communicate specific messages to a human. We explore how these animations relate to the perceived group cohesiveness of the swarm, comprised of five different parameters: synchronising, grouping, following, reacting, and shape forming. We conducted an online user study where 98 participants viewed nine animations of a swarm displaying different behaviours and rated them for perceived group cohesiveness. We found that the parameters of group cohesiveness correlated with the messages the swarm was perceived as communicating. In particular, the message of initiating communication was highly positively correlated with all group parameters, whereas broken communication was negatively correlated. In addition, the importance of specific group parameters differed within each animation. For example, the parameter of grouping was most associated with animations signalling an intervention is needed. These findings are discussed within the context of designing intuitive behaviour for robot swarms.

Bots of a Feather: Exploring User Perceptions of Group Cohesiveness for Application in Robotic Swarms

The success of human-robot interaction is strongly affected by the people's ability to infer others' intentions and behaviours, and the level of people's trust that others will abide by their same principles and social conventions to achieve a common goal. 
The ability of understanding and reasoning about other agents' mental states is known as Theory of Mind (ToM). 
ToM and trust, therefore, are key factors in the positive outcome of human-robot interaction. We believe that a robot endowed with a ToM is able to gain people's trust, even when this may occasionally make errors. 

In this work, we present a user study in the field in which participants (N=123) interacted with a robot that may or may not have a ToM, and may or may not exhibit erroneous behaviour. 
Our findings indicate that a robot with ToM is perceived as more reliable, and they trusted it more than a robot without a ToM even when the robot made errors. Finally, ToM results to be a key driver for tuning people's trust in the robot even when the initial condition of the interaction changed (i.e., loss and regain of trust in a longer relationship).


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