United States

This paper proposes a novel dynamic method based on Behavior Trees that integrates planning and allocation of tasks in mixed human robot teams, suitable for manufacturing environments. The Behavior Tree formulation allows encoding a single job as a compound of different tasks with temporal constraints. The role allocation problem is solved with multiple simplified online optimization sub-problems. These sub-problems are defined as Mixed-Integer Linear Programs, that, according to the worker-actions related costs and the workers&#39; availability, allocate the yet-to-execute tasks among the available workers.

IEEE RO-MAN 2021

Older Adults’ Opinion on Social Robot as Companion

cooperation and collaboration in human-robot teams; hri and collaboration in manufacturing environments

[![](https://assets.underline.io/uploads/markdown_image/1/image/21d13e03491d7e9acf6b8c46c7979c25.png)](https://docs.google.com/spreadsheets/d/1SCDDfkQk7es4J6RULKHVx1RzbB0Mutla--Sle5SVfb0/edit?usp=sharing)

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**RoboCup Symposium**

The 24th RoboCup International Symposium will be held in conjunction with RoboCup 2021 in a purely online setting. Scientific papers will be presented reporting innovative, original research with relevance to robotics and artificial intelligence. Within the described scope of topics we also encouraged submissions of high-quality overview articles, papers describing real-world research, and papers reporting theoretical results. In addition to the main track with regular research papers, there will be a special track focused on systems, data, and benchmarks featuring papers describing novel, open-source hardware and software systems, as well as datasets and benchmarks relevant to the community. In addition to research paper presentations, the RoboCup Symposium 2021 includes keynote talks by Dieter Fox, Jean-Paul Laumond, and Stefanie Tellex, and several interactive sessions.

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RoboCup 2021

The 24th RoboCup International Symposium will be held in conjunction with RoboCup 2021 in a purely online setting. Scientific papers will be presented reporting innovative, original research with relevance to robotics and artificial intelligence. Within the described scope of topics we also encouraged submissions of high-quality overview articles, papers describing real-world research, and papers reporting theoretical results. In addition to the main track with regular research papers, there will be a special track focused on systems, data, and benchmarks featuring papers describing novel, open-source hardware and software systems, as well as datasets and benchmarks relevant to the community. In addition to research paper presentations, the RoboCup Symposium 2021 includes keynote talks by Dieter Fox, Jean-Paul Laumond, and Stefanie Tellex, and several interactive sessions.

This paper proposes a novel dynamic method based on Behavior Trees that integrates planning and allocation of tasks in mixed human robot teams, suitable for manufacturing environments. The Behavior Tree formulation allows encoding a single job as a compound of different tasks with temporal constraints. The role allocation problem is solved with multiple simplified online optimization sub-problems. These sub-problems are defined as Mixed-Integer Linear Programs, that, according to the worker-actions related costs and the workers' availability, allocate the yet-to-execute tasks among the available workers.

panel

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

With social robots entering the consumer market, there is a growing need to study
children's intention to adopt social robots in a domestic environment. Based on a
content analysis of data from 87 children, we found that hedonic beliefs, and
especially companionship from the robot, were the most mentioned beliefs for
domestic adoption of a social robot. Social beliefs were rarely mentioned. If children
mentioned beliefs for rejecting the robot, they often referred to family members and
family composition.

Exploring Children's Beliefs for Adoption or Rejection of Domestic Social Robots

Childhood ambulatory disorders affect physical, cognitive, and social development. We propose using a custom assistive robot for engaging children in early mobility interventions. Our first study showed that during driving interventions, children looked at the robot more while it was active, but other results were mixed. In our second study on ambulatory free play, children looked at and engaged with the robot more when it was active. This research is a first step in developing robots for early mobility interventions.

Design of an Assistive Robot for Infant Mobility Interventions

As autonomous collaborative robots are more widely used in work environments alongside humans it is of great importance to facilitate the communication between people and robotic systems, in a way that promotes safety and productivity. To this end, we propose an Augmented Reality (AR) based system that allows workers in a human-robot collaborative environment to interact with a robot while also receiving information regarding the robot state and plans that relate to the human’s safety and trust.

Intuitive and Safe Interaction in Multi-User Human Robot Collaboration Environments through Augmented Reality Displays

In this work, we present a framework for human-robot collaboration allowing the human operator to alter the robot plan execution online. To achieve this goal, we introduce branched AND/OR graphs, an extension to AND/OR graphs, to manage flexible and adaptable human-robot collaboration. In our study, the operator can alter the plan execution using two implementations of Branched AND/OR graphs for learning by demonstration, using kinesthetic teaching, and task repetition. Finally, we demonstrated the effectiveness of our framework in a defect spotting scenario where the operator supervises robot operations and modifies online the plan when necessary.

Branched AND/OR Graphs: Toward Flexible and Adaptable Human-Robot Collaboration

Following the broad outline of Active Student Participation theory, we propose a pedagogical approach CAR targeted toward education of new researchers in the Autonomous Vehicles area. CAR combines Creativity theory, Applied demo-oriented learning, and Real world research context. Furthermore, we report on applying the
approach to stimulate learning and engagement in a master’s course, in which students freely created a demo with 10 small robots running ROS2 and Ubuntu on Raspberry Pis, in connection to an ongoing research project and a real current
problem (SafeSmart project and COVID-19). The results suggested the feasibility of the CAR approach for enabling learning, as well as mutual benefits for both the students and researchers involved. 


On the Causality between Affective Impact and Coordinated Human-Robot Reactions

We present an interactive robotic platform for teleoperated grasping as an educational tool. With this open- source robot application, we engage children and young adults with robotics. Our teleoperation method uses the Leap Motion optical gesture tracker to simultaneously control each of the four degrees-of- freedom (DOF) of a robotic hand and the six-DOF tool pose of a serial manipulator.

Gesture-Based Teleoperated Grasping for Educational Robotics

This study investigates if and how college students cheat while engaged in a collaborative sorting task with a robot. Our study finds that prior exposure to cheating on the task significantly increases the likelihood of cheating. Yet, the tendency to cheat is not impacted by the clarity of the task rules.

Impact of Human Gaze Behavior and Robot Appearance on Motion Uncertainty During Cooperative Hand Movement Tasks

The contributions, of our study are: a) the comparison of a human vs a robot-tutor in terms of the students’ level of understanding from the course (knowledge acquisition) and opinions about the use of robots in the educational field, b) an analysis of how the exposure of pre-service teachers in a robot-tutor and Computational thinking course affects their robot-related attitudes. c) We investigate the differences in attitudes and understanding from the course between students, after having ten weeks of basically Computational thinking lessons, who have one or two lectures with a robot-tutor.

Interactive Robotic Systems as Boundary-Crossing Robots – the User’s View

Gamified rehabilitation tackles the problem of keeping patients engaged in, and motivated to do physical rehabilitation to improve its efficacy. However, with respect to standard rehabilitation, patients are freer to move about and may compensate their motion difficulties with parasite movements, which would greatly reduce the efficacy of the rehabilitation. To identify and characterize compensatory motions, we collected and analyzed video data of people playing the“tangible Pacman” game (an upper-limb rehabilitation game in which a patient moves a semi-passive robot, the “Pacman”, on a map to collect 6 apples, while being chased by one or two autonomous robots, the “ghosts”). Participants include 10healthy elderly adults and 10 chronic stroke patients, who played multiple runs of the game, with different-sized maps and various game configurations. By analyzing the video recordings we successfully identified higher shoulder and torso lateral tilt compensation in stroke patients and developed a proof-of-concept compensatory motion detection system that relies on a wearable Inertial Measurement Unit and ROS to provide in-game, real-time visual feedback on compensation.

Detecting Compensatory Motions and Providing Informative Feedback During a Tangible Robot Assisted Game for Post-Stroke Rehabilitation

In this work, we designed a teachable robot that encourages a pair of students to discuss their thoughts and teaching decisions during the tutoring session. The robot adapts to the students' talking activity. The results of our user study (n = 68) showed that the adaptive robot was successful in creating more dialogue between dyad members and in increasing task engagement. Over time, the adaptive robot was also able to encourage both members to contribute more equally to the conversation.

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Next from IEEE RO-MAN 2021

Exploring Children's Beliefs for Adoption or Rejection of Domestic Social Robots

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Toward robust manipulation in complex environments

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