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Engaging with Robotic Swarms: Commands from Expressive Motion

Authors:

Ulysse Côté-Allard,

Benoit Gosselin,

Giovanni BeltrameAuthors Info & Claims

ACM Transactions on Human-Robot Interaction (THRI), Volume 8, Issue 2

Article No.: 11, Pages 1 - 26

https://doi.org/10.1145/3323213

Published: 08 June 2019 Publication History

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Abstract

In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier. To compensate for the low quality of the information of these sensors, and to allow a wider range of dynamic control, our approach relies on machine learning. We train our classifier directly on the user to recognize (within minutes) which physiological state his or her body motion expresses. We demonstrate that on top of guaranteeing faster field deployment, our algorithm performs better than all comparable algorithms, and we detail its configuration and the most significant features extracted. As the use of large groups of robots is growing, we postulate that their interaction with humans can be eased by our approach. We identified the key factors to stimulate engagement using our system on 27 participants, each creating his or her own set of expressive motions to control a swarm of desk robots. The resulting unique dataset is available online together with the classifier and the robot control scripts.

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Cited By

Capelli BSantos MSabattini L(2024)Towards the Legibility of Multi-robot SystemsACM Transactions on Human-Robot Interaction10.1145/364798413:2(1-32)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3647984
Brignol APaas ASotelo-Castro LSt-Onge DBeltrame GCoffey E(2024)Overcoming boundaries: Interdisciplinary challenges and opportunities in cognitive neuroscienceNeuropsychologia10.1016/j.neuropsychologia.2024.108903200(108903)Online publication date: Jul-2024
https://doi.org/10.1016/j.neuropsychologia.2024.108903
Zhang CLi H(2022)Adoption of Artificial Intelligence Along with Gesture Interactive Robot in Musical Perception Education Based on Deep Learning MethodInternational Journal of Humanoid Robotics10.1142/S021984362240008419:03Online publication date: 8-Jul-2022
https://doi.org/10.1142/S0219843622400084
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Index Terms

Engaging with Robotic Swarms: Commands from Expressive Motion
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
      1. External interfaces for robotics
      2. Robotic control
    2. Sensors and actuators

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Published In

cover image ACM Transactions on Human-Robot Interaction

ACM Transactions on Human-Robot Interaction Volume 8, Issue 2

June 2019

136 pages

EISSN:2573-9522

DOI:10.1145/3339062

Editors:
Odest Chadwicke Jenkins
University of Michigan, USA
,
Selma Sabanovic
Indiana University, USA

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Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Publication History

Published: 08 June 2019

Accepted: 01 March 2019

Revised: 01 January 2019

Received: 01 April 2018

Published in THRI Volume 8, Issue 2

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Cited By

Capelli BSantos MSabattini L(2024)Towards the Legibility of Multi-robot SystemsACM Transactions on Human-Robot Interaction10.1145/364798413:2(1-32)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3647984
Brignol APaas ASotelo-Castro LSt-Onge DBeltrame GCoffey E(2024)Overcoming boundaries: Interdisciplinary challenges and opportunities in cognitive neuroscienceNeuropsychologia10.1016/j.neuropsychologia.2024.108903200(108903)Online publication date: Jul-2024
https://doi.org/10.1016/j.neuropsychologia.2024.108903
Zhang CLi H(2022)Adoption of Artificial Intelligence Along with Gesture Interactive Robot in Musical Perception Education Based on Deep Learning MethodInternational Journal of Humanoid Robotics10.1142/S021984362240008419:03Online publication date: 8-Jul-2022
https://doi.org/10.1142/S0219843622400084
Li Y(2021)Dance Motion Capture Based on Data Fusion Algorithm and Wearable Sensor NetworkComplexity10.1155/2021/26562752021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/2656275
Pruszko LCoutrix CLaurillau YPiranda BBourgeois J(2021)Molecular HCIProceedings of the ACM on Human-Computer Interaction10.1145/34617335:EICS(1-33)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3461733
Cote-Allard UGagnon-Turcotte GPhinyomark AGlette KScheme ELaviolette FGosselin B(2021)A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture RecognitionIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2021.305974129(546-555)Online publication date: 2021
https://doi.org/10.1109/TNSRE.2021.3059741
Berger JBacula AKnight H(2021)Exploring Communicatory Gestures for Simple Multi-robot SystemsSocial Robotics10.1007/978-3-030-90525-5_78(819-823)Online publication date: 10-Nov-2021
https://dl.acm.org/doi/10.1007/978-3-030-90525-5_78
Côté-Allard UCampbell EPhinyomark ALaviolette FGosselin BScheme E(2020)Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted FeaturesFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2020.001588Online publication date: 3-Mar-2020
https://doi.org/10.3389/fbioe.2020.00158
Urrestilla NSt-Onge DTruong KHeylen DCzerwinski MBerthouze NChetouani MNakano M(2020)Measuring Cognitive Load: Heart-rate Variability and Pupillometry AssessmentCompanion Publication of the 2020 International Conference on Multimodal Interaction10.1145/3395035.3425203(405-410)Online publication date: 25-Oct-2020
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St-Onge DKaufmann MPanerati JRamtoula BCao YCoffey EBeltrame G(2020)Planetary Exploration With Robot Teams: Implementing Higher Autonomy With Swarm IntelligenceIEEE Robotics & Automation Magazine10.1109/MRA.2019.294041327:2(159-168)Online publication date: Jun-2020
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