[AISWorld] *New Issue* ACM Transactions on Human-Robot Interaction 13(3) September 2024

[young at cs.umanitoba.ca]

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ACM Transactions on Human-Robot Interaction
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We are pleased to announce the publication of Volume 13, Issue 3, September 2024, which includes our Special Issue on Artificial Intelligence for Human-Robot Interaction (AI-HRI).

https://dl.acm.org/toc/thri/2024/13/3


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Tutorials and Surveys
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Gaze-Based Intention Estimation: Principles, Methodologies, and Applications in HRI
Anna Belardinelli
Abstract: Intention prediction has become a relevant field of research in Human–Machine and Human–Robot Interaction. Indeed, any artificial system (co)-operating with and along humans, designed to assist and coordinate its actions with a human partner, would ...
DOI: https://doi.org/10.1145/3656376.

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Articles
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Learning Autonomous Viewpoint Adjustment from Human Demonstrations for Telemanipulation
Ruixing Jia, Lei Yang, Ying Cao, Calvin Kalun Or, Wenping Wang, Jia Pan
Abstract: Teleoperation systems find many applications from earlier search-and-rescue to more recent daily tasks. It is widely acknowledged that using external sensors can decouple the view of the remote scene from the motion of the robot arm during manipulation, ...
DOI: https://doi.org/10.1145/3660348.

OmniCharger: CNN-Based Hand Gesture Interface to Operate an Electric Car Charging Robot through Teleconference
Miguel Altamirano Cabrera, Viktor Rakhmatulin, Aleksey Fedoseev, Oleg Sautenkov, Oussama Alyounes, Andrei Puchkov, Dzmitry Tsetserukou
Abstract: The automation of the car charging process is motivated by the rapid development of technologies for self-driving cars and the increasing importance of ecological transportation units. Automation of this process requires the implementation of Computer ...
DOI: https://doi.org/10.1145/3659060.


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Special Issue on Artificial Intelligence for Human-Robot Interaction (AI-HRI)
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Introduction to the Special Issue on Artificial Intelligence for Human–Robot Interaction (AI-HRI)
Jivko Sinapov, Zhao Han, Shelly Bagchi, Muneeb Ahmad, Matteo Leonetti, Ross Mead, Reuth Mirsky, Emmanuel Senft
DOI: https://doi.org/10.1145/3672535.

Assistance in Teleoperation of Redundant Robots through Predictive Joint Maneuvering
Connor Brooks, Wyatt Rees, Daniel Szafir
Abstract: In teleoperation of redundant robotic manipulators, translating an operator’s end effector motion command to joint space can be a tool for maintaining feasible and precise robot motion. Through optimizing redundancy resolution, the control system can ...
DOI: https://doi.org/10.1145/3630265.

Experimental Assessment of Human–Robot Teaming for Multi-step Remote Manipulation with Expert Operators
Claudia Pérez-D’Arpino, Rebecca P. Khurshid, Julie A. Shah
Abstract: Remote robot manipulation with human control enables applications in which safety and environmental constraints are adverse to humans (e.g., underwater, space robotics and disaster response) or the complexity of the task demands human-level cognition and ...
DOI: https://doi.org/10.1145/3618258.

Stochastic-skill-level-based Shared Control for Human Training in Urban Air Mobility Scenario
Sooyung Byeon, Joonwon Choi, Yutong Zhang, Inseok Hwang
Abstract: This article proposes a novel stochastic-skill-level-based shared control framework to assist human novices to emulate human experts in complex dynamic control tasks. The proposed framework aims to infer stochastic-skill-levels (SSLs) of the human novices ...
DOI: https://doi.org/10.1145/3603194.

Learning to Control Complex Robots Using High-Dimensional Body-Machine Interfaces
Jongmin Lee, Temesgen Gebrekristos, Dalia De Santis, Mahdieh Nejati-Javaremi, Deepak Gopinath, Biraj Parikh, Ferdinando Mussa-Ivaldi, Brenna Argall
Abstract: When individuals are paralyzed from injury or damage to the brain, upper body movement and function can be compromised. While the use of body motions to interface with machines has shown to be an effective noninvasive strategy to provide movement ...
DOI: https://doi.org/10.1145/3630264.

Unified Learning from Demonstrations, Corrections, and Preferences during Physical Human–Robot Interaction
Shaunak A. Mehta, Dylan P. Losey
Abstract: Humans can leverage physical interaction to teach robot arms. This physical interaction takes multiple forms depending on the task, the user, and what the robot has learned so far. State-of-the-art approaches focus on learning from a single modality, or ...
DOI: https://doi.org/10.1145/3623384.

“Do This Instead”—Robots That Adequately Respond to Corrected Instructions
Christopher Thierauf, Ravenna Thielstrom, Bradley Oosterveld, Will Becker, Matthias Scheutz
Abstract: Natural language instructions are effective at tasking autonomous robots and for teaching them new knowledge quickly. Yet, human instructors are not perfect and are likely to make mistakes at times and will correct themselves when they notice errors in ...
DOI: https://doi.org/10.1145/3623385.

Augmented Reality Visualization of Autonomous Mobile Robot Change Detection in Uninstrumented Environments
Christopher Reardon, Jason M. Gregory, Kerstin S. Haring, Benjamin Dossett, Ori Miller, Aniekan Inyang
Abstract: The creation of information transparency solutions to enable humans to understand robot perception is a challenging requirement for autonomous and artificially intelligent robots to impact a multitude of domains. By taking advantage of comprehensive and ...
DOI: https://doi.org/10.1145/3611654.

A Meta-Analysis of Vulnerability and Trust in Human–Robot Interaction
Peter E. Mckenna, Muneeb I. Ahmad, Tafadzwa Maisva, Birthe Nesset, Katrin Lohan, Helen Hastie
Abstract: In human–robot interaction studies, trust is often defined as a process whereby a trustor makes themselves vulnerable to a trustee. The role of vulnerability however is often overlooked in this process but could play an important role in the gaining and ...
DOI: https://doi.org/10.1145/3658897.

IMPRINT: Interactional Dynamics-aware Motion Prediction in Teams using Multimodal Context
Mohammad Samin Yasar, Md Mofijul Islam, Tariq Iqbal
Abstract: Robots are moving from working in isolation to working with humans as a part of human-robot teams. In such situations, they are expected to work with multiple humans and need to understand and predict the team members’ actions. To address this challenge, ...
DOI: https://doi.org/10.1145/3626954.

UHTP: A User-Aware Hierarchical Task Planning Framework for Communication-Free, Mutually-Adaptive Human-Robot Collaboration
Kartik Ramachandruni, Cassandra Kent, Sonia Chernova
Abstract: Collaborative human-robot task execution approaches require mutual adaptation, allowing both the human and robot partners to take active roles in action selection and role assignment to achieve a single shared goal. Prior works have utilized a leader-...
DOI: https://doi.org/10.1145/3623387.

Face2Gesture: Translating Facial Expressions into Robot Movements through Shared Latent Space Neural Networks
Michael Suguitan, Nick Depalma, Guy Hoffman, Jessica Hodgins
Abstract: In this work, we present a method for personalizing human-robot interaction by using emotive facial expressions to generate affective robot movements. Movement is an important medium for robots to communicate affective states, but the expertise and time ...
DOI: https://doi.org/10.1145/3623386.

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ACM THRI welcomes contributions from across HRI and Robotics. For details on the journal, information for authors, and upcoming Special Issues, please visit the ACM THRI website: http://thri.acm.org

Iolanda Leite, KTH Royal Institute of Technology
James E. Young, University of Manitoba
Editors-in-Chief, ACM Transactions on Human-Robot Interaction