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Multimodal Sensing for Human-Robot Interaction
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Multimodal Sensing for Human-Robot Interaction

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (10 October 2021) | Viewed by 3372

Special Issue Editors

Prof. Dr. Antonio Sgorbissa

E-Mail Website
Guest Editor
University of Genoa,16145 Genoa, Italy
Interests: cognitive robotics; knowledge representation; social and culture-aware interaction; wearable and Ubiquitous Robotics
Prof. Dr. Nak Young Chong

E-Mail Website
Guest Editor
School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa 923-1292, Japan
Interests: human-robot interaction; mobile robots; learning; humanoid robots; emotion recognition
Dr. Carmine Tommaso Recchiuto

E-Mail Website
Guest Editor
University of Genoa,16145 Genoa, Italy
Interests: social robotics, autonomous conversation, human-robot interaction, humanoid robotics, UAVs

Special Issue Information

Dear Colleagues,

The development of robots and artificial agents conceived for being part of our everyday life is a matter of fact. Even if almost all robots and devices are equipped with multiple sensors, many limitations of these autonomous systems are still evident: social robots have difficulty understanding human emotions and intentions, and thus they may fail to reply appropriately; autonomous mobile robots struggle to have full knowledge of the surrounding environment to make the right choice at the right moment; industrial robots have difficulty understanding and learning the needs of their human partners.

For these reasons, we need to pursue a more integrated perspective, one which involves a strict connection between multimodal sensing and actuation, in order to develop intelligent machines able to understand human behavior and act accordingly.

The purpose of this Special Issue is, therefore, to gather the latest research in the field of human-robot interaction, focusing on the integration of multimodal sensing approaches with the understanding, planning, and acting strategies of autonomous robots.

We strongly encourage the submission of papers focusing on the keywords below, but works on related topics will also be considered.

Prof. Dr. Antonio Sgorbissa
Prof. Dr. Nak Young Chong
Dr. Carmine Tommaso Recchiuto
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Autonomous Vehicle Navigation
  • Activiy recognition
  • Behaviour-Based Systems
  • Cognitive Control Architectures
  • Cognitive Human-Robot Interaction
  • Haptics and Haptic Interfaces
  • Human-Centered Robotics
  • Intention Recognition
  • Multi-Modal Perception for HRI
  • Physical Human-Robot Interaction
  • Reactive and Sensor-Based Planning
  • Robust/Adaptive Control of Robotic Systems
  • Sensors for social robotics
  • Sensors for human-robot interaction
  • Sensing, perceiving and acting
  • Sensing and perception for grasping and manipulation
  • Sensor Fusion
  • Sensor-based control
  • Semantic Scene Understanding
  • Social HRI

Published Papers (1 paper)

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Research

27 pages, 3056 KiB  
Article
Subsentence Extraction from Text Using Coverage-Based Deep Learning Language Models
by JongYoon Lim, Inkyu Sa, Ho Seok Ahn, Norina Gasteiger, Sanghyub John Lee and Bruce MacDonald
Sensors 2021, 21(8), 2712; https://doi.org/10.3390/s21082712 - 12 Apr 2021
Cited by 8 | Viewed by 2749
Abstract
Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging [...] Read more.
Sentiment prediction remains a challenging and unresolved task in various research fields, including psychology, neuroscience, and computer science. This stems from its high degree of subjectivity and limited input sources that can effectively capture the actual sentiment. This can be even more challenging with only text-based input. Meanwhile, the rise of deep learning and an unprecedented large volume of data have paved the way for artificial intelligence to perform impressively accurate predictions or even human-level reasoning. Drawing inspiration from this, we propose a coverage-based sentiment and subsentence extraction system that estimates a span of input text and recursively feeds this information back to the networks. The predicted subsentence consists of auxiliary information expressing a sentiment. This is an important building block for enabling vivid and epic sentiment delivery (within the scope of this paper) and for other natural language processing tasks such as text summarisation and Q&A. Our approach outperforms the state-of-the-art approaches by a large margin in subsentence prediction (i.e., Average Jaccard scores from 0.72 to 0.89). For the evaluation, we designed rigorous experiments consisting of 24 ablation studies. Finally, our learned lessons are returned to the community by sharing software packages and a public dataset that can reproduce the results presented in this paper. Full article
(This article belongs to the Special Issue Multimodal Sensing for Human-Robot Interaction)
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