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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.

IEEE RO-MAN 2021

Effect of AI Agent&#39;s Speech and Tactility Types on Users&#39; Perception

speech type

tactility type

robot assistant

robot companion

ai agent

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

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

technical paper

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 increased interest in close-proximity human-robot collaboration in production settings it is important that we understand how robot behaviors and mistakes affect human-robot trust. We designed a system for real-time human-robot trust assessment using a top-down depth camera tracking setup with the goal of using signs of physical apprehension to infer decreases in trust toward the robot. In an experiment with 20 participants we evaluated the tracking system in a repetitive collaborative pick-and-place task with a Rethink Robotics Sawyer robot.

Interviewing Style for a Social Robot

This paper presents a new concept of
supplementary arms for the collaborative task with a worker
during a thin panel installation on the wall. Foldable robot arms
for holding and installation (FRAHI) using scissor structure
have been proposed as a new concept of the human-robot
collaboration. The foldable and wearable FRAHI arm has been
designed to influence cooperativeness and worker safety. FRAHI
arms are developed and integrated with a worker's safety suit,
and a soft and flexible waist belt is introduced for the arms and
worker interface, comfortable to the user while working. The
design principle of FRAHI is based on folding capability, gravity
compensation for the cooperative thin-panel installation with
workers. The experimental demonstration of a thin panel
installation on the wall has been performed for the feasibility of
the supplementary arm for worker assistance and
cooperativeness in construction works.

Exploring Human attitude during Human-Robot Interaction

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

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

For an effective deployment in manufacturing,
Collaborative Robots should be capable of adapting their
behavior to the state of the environment and to keep the user
safe and engaged during the interaction. Artificial Intelligence
(AI) enables robots to autonomously operate understanding
the environment, planning their tasks and acting to achieve
some given goals. However, the effective deployment of AI technologies
in real industrial environments is not straightforward.
There is a need for engineering tools facilitating communication
and interaction between AI engineers and Domain experts. This
paper proposes a novel software tool, called TENANT (Tool
fostEriNg Ai plaNning in roboTics) whose aim is to facilitate
the use of AI planning technologies by providing domain
experts like e.g., production engineers, with a graphical software
framework to synthesize AI planning models abstracting from
syntactic features of the underlying planning formalism.

Artistic Style in Robotic Painting: A Machine Learning Approach to Learning Brushstrokes from Human Artists

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

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). 


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

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