United States

robots

audio classification

speaker recognition

machine learning

dialogue system

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

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. 

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

AIAI 2020

We present a speaker-aware robotic system which recognizes users by voice in realistic, noisy conditions, highlighting the potential of speaker identification to enrich industrial and social HRI. We approach this as a CNN-based audio classification task, with the particular aim of producing fast, reliable, and explainable predictions. Our method is evaluated on a challenging 6-speaker dataset collected "in the wild" and showcased in a manufacturing scenario, where a collaborative robot personalizes its responses and prevents non-authorized users from executing commands.

Why Talk to People When You Can Talk to Robots? Far-Field Speaker Identification in the Wild

panel

Explainable Temperament Estimation of Toddlers by a Childcare Robot

Commanding a Whole-Arm Manipulation Grasp Configuration with One Click: Interaction Concept and Analytic IK Method

Towards Infant Kick Quality Detection to Support Physical Therapy and Early Detection of Cerebral Palsy: A Pilot Study

Affective, Cognitive and Behavioural Engagement Detection for Human-Robot Interaction in a Bartending Scenario

Deployment of a Social Robot into a Classroom of Remote Teaching by Elderly People to School Children: a Case Report

Affective Touch Robots with Changing Textures and Movements

Pandemic Effects on Social Companion Robot Use in Care Homes

Robot player adaptation to human opponents in physical, competitive robogames

Designing Proactive Objects with Artificial Eyes Based on Perceptual Crossing Paradigm

An Experimental Study of the Accuracy vs Inference Speed of RGB-D Object Recognition in Mobile Robotics

technical paper

https://d3smihljt9218e.cloudfront.net/transcript/20308/subtitle/7a61257e6c2de595b6b87cab51fb0dee.vtt

English (automatic)

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Why Talk to People When You Can Talk to Robots? Far-Field Speaker Identification in the Wild

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