United States

Imbuing furniture with robot properties reduces the physical labor and time needed for arranging spaces, such as homes, classrooms, and offices. Outsourcing labor tasks to robot furniture requires users&#39; involvement with functional user interfaces. We performed a user study on multi-robot furniture and added additional features based on the study results. The study involved 12 participants rearranging multiple non-robotic and robotic chairs (ChairBots). Results from the video and interview analysis revealed five high-level features missing in the original ChairBot: dual screen-based user interface, the ability to save and to set arrangements, the ability to move in multi-robot formations, the ability to snap to angles/gridlines, and higher movement precision. The improved system allows users to control multiple furniture robots, both locally and remotely. Such improvement sets the baseline functionalities of robot furniture arrangement systems while extending the potential utilization of established robotic chairs.

IEEE RO-MAN 2021

Towards User-Centric Robot Furniture Arrangement

user studies

human-robot interaction

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. 

Imbuing furniture with robot properties reduces the physical labor and time needed for arranging spaces, such as homes, classrooms, and offices. Outsourcing labor tasks to robot furniture requires users' involvement with functional user interfaces. We performed a user study on multi-robot furniture and added additional features based on the study results. The study involved 12 participants rearranging multiple non-robotic and robotic chairs (ChairBots). Results from the video and interview analysis revealed five high-level features missing in the original ChairBot: dual screen-based user interface, the ability to save and to set arrangements, the ability to move in multi-robot formations, the ability to snap to angles/gridlines, and higher movement precision. The improved system allows users to control multiple furniture robots, both locally and remotely. Such improvement sets the baseline functionalities of robot furniture arrangement systems while extending the potential utilization of established robotic chairs.

panel

How team members are treated influences their performance in the team and their desire to be a part of the team in the future. Fairness metrics that properly capture people's perception of fairness in human-robot teaming remains a research gap. We present work on assessing how well objective metrics capture people's perception of fairness. We show that these metrics align with perceived fairness. Importantly, we discover that there are bleed-over effects in people's assessment of fairness.

Unfair! Perceptions of Fairness in Human-Robot Teams

Almost everyone reading these lines will have a small computer either in their pocket or within easy reach and it is our smartphone. In this work, we take advance of their proliferation and we explore the possibility of using them as Human-Robot Interaction interfaces not only as a teleoperation tool but also as a knowledge-sharing interface. We also test how acceptable is for the user to use them and different methods to engage the user with the task at hand.

User-Friendly Smartphone Interface to Share Knowledge in Human-Robot Collaborative Search Tasks

Like most machines, robots may sometimes behave in an erroneous manner. Nevertheless, when a robot has been taught a task by humans, we test if the teachers ignore small mistakes made by the robot if it shows significant improvements while practising the task. We first conducted a study in which the perceived severity of different robot errors in a household chore was investigated. We then used the results to create teaching scenarios of different levels of error severity and conducted a second study. Even small errors significantly affected trust and big errors had a more adverse impact.

Context Dependent Trajectory Generation using Sequence-to-Sequence Models for Robotic Toilet Cleaning

We developed POP Cart which is a shopping cart type agent system and recommend products. The system chats with the user to create a good relationship while shopping for recommendations. To evaluate the system, we conducted an experiment with 49 people in a supermarket. The results of the experiment showed that POP cart recommendations were more acceptable to users than simple recommendations and no recommendations.

POP Cart: Product Recommendation System by an Agent on a Shopping Cart

Current study developed robot version of widely adopted test of affective ToM, Reading the Mind in the Eyes Test (RMET) using a social robot, Vector. In this preliminary study, the performance toward the expressions of same ethnicity (RMET-K) predicts the performance on the robot expression (RMET-R).

A Preliminary Study on Reading the Mind in the Eyes of the Robot

the goal of this study is to investigate the feasibility of integrating wearable sensors and machine learning techniques to detect in real-time the occurrence of challenging behaviours during child-robot interaction.
Our contributions are as follow:
1) Defining an effective annotation to distinguish challenge-
ing behaviours.
2) Investigating the influence of different physiological
signals in the prediction of challenging behaviours.
3) Development of an assistive annotation tool to monitor
the physiological signs, and to visualize the predictions of the developed machine learning algorithms.

Effect of Robot Agents on Teaching against Pseudoscience

We address a task allocation problem for human multi-robot settings. Given a set of tasks to perform, we formulate a general Mixed-Integer Linear Programming problem aiming at minimizing the overall execution time while optimizing the quality of the executed tasks as well as human and robotic workload. Different skills of the agents are taken into account and human operators are enabled to either directly execute tasks or play supervisory roles; moreover, multiple manipulators can tightly collaborate if required to carry out a task. Finally, as realistic in human contexts, human parameters are assumed to vary over time, e.g., due to increasing human level of fatigue. Therefore, online monitoring is required and re-allocation is performed if needed.

A Mixed-Integer Linear Programming Formulation for Human Multi-Robot Task Allocation

Speaking and appearance modalities might give characters to the AI agent. We designed a 2(speech types: assistant-like vs. companion-like) × 2(flexibility types: flexible vs. hard) × 2(roughness types: rough vs. smooth) mixed-participant experiment to explore the users’ perception of the AI agent on usefulness, sociability, and service evaluation. The results showed that the texture affected the users’ perception of the AI agent and the users’ preference for a specific texture according to the speech type. Based on the results, we discussed the appropriate texture setting and speech type in specific context.

Effect of AI Agent's Speech and Tactility Types on Users' Perception

We proposed that the empathic ability of a social robot can be improved if the social robot can create facial expressions based on the emotional state of a user. Moreover, we suggested a framework of facial expressions for robots. These facial expressions can be repeatedly used in various social robot platforms to achieve such a strategy.

Robot Facial Expression Framework for Enhancing Empathy in Human-Robot Interaction

The key contribution of this work is to incorporate natural human motion for controlling Soft Robotic Manipulators (SRMs). We accomplish this via user input modalities that translate motion in the human coordinate system to the SRM end-effector. For that, we present two Human-Robot Interfaces (HRI) and conduct user studies on an SRM in object handling tasks and assess the performance of teleoperators using a spatial input device (Geomagic Touch) and an augmented reality-based input device (HTC Vive). 


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