Human-Robot Interaction With Robust Prediction of Movement Intention Surpasses Manual Control
Physical human-robot interaction (pHRI) enables a user to interact with a physical robotic device to advance beyond the current capabilities of high-payload and high-precision industrial robots. This paradigm opens up novel applications where a the cognitive capability of a user is combined with the...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-09-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2021.695022/full |
| _version_ | 1830477405921214464 |
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| author | Sebastijan Veselic Sebastijan Veselic Sebastijan Veselic Claudio Zito Claudio Zito Dario Farina |
| author_facet | Sebastijan Veselic Sebastijan Veselic Sebastijan Veselic Claudio Zito Claudio Zito Dario Farina |
| author_sort | Sebastijan Veselic |
| collection | DOAJ |
| description | Physical human-robot interaction (pHRI) enables a user to interact with a physical robotic device to advance beyond the current capabilities of high-payload and high-precision industrial robots. This paradigm opens up novel applications where a the cognitive capability of a user is combined with the precision and strength of robots. Yet, current pHRI interfaces suffer from low take-up and a high cognitive burden for the user. We propose a novel framework that robustly and efficiently assists users by reacting proactively to their commands. The key insight is to include context- and user-awareness in the controller, improving decision-making on how to assist the user. Context-awareness is achieved by inferring the candidate objects to be grasped in a task or scene and automatically computing plans for reaching them. User-awareness is implemented by facilitating the motion toward the most likely object that the user wants to grasp, as well as dynamically recovering from incorrect predictions. Experimental results in a virtual environment of two degrees of freedom control show the capability of this approach to outperform manual control. By robustly predicting user intention, the proposed controller allows subjects to achieve superhuman performance in terms of accuracy and, thereby, usability. |
| first_indexed | 2024-12-21T16:13:42Z |
| format | Article |
| id | doaj.art-017e85f075fa4fac895b8e61ccf0b402 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-12-21T16:13:42Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-017e85f075fa4fac895b8e61ccf0b4022022-12-21T18:57:44ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182021-09-011510.3389/fnbot.2021.695022695022Human-Robot Interaction With Robust Prediction of Movement Intention Surpasses Manual ControlSebastijan Veselic0Sebastijan Veselic1Sebastijan Veselic2Claudio Zito3Claudio Zito4Dario Farina5Department of Clinical and Movement Neurosciences, University College London, London, United KingdomWellcome Centre for Human Neuroimaging, University College London, London, United KingdomSchool of Computer Science, University of Birmingham, Birmingham, United KingdomSchool of Computer Science, University of Birmingham, Birmingham, United KingdomAutonomous Robotics Research Centre, Technology Innovation Institute, Abu Dhabi, United Arab EmiratesDepartment of Bioengineering, Imperial College London, London, United KingdomPhysical human-robot interaction (pHRI) enables a user to interact with a physical robotic device to advance beyond the current capabilities of high-payload and high-precision industrial robots. This paradigm opens up novel applications where a the cognitive capability of a user is combined with the precision and strength of robots. Yet, current pHRI interfaces suffer from low take-up and a high cognitive burden for the user. We propose a novel framework that robustly and efficiently assists users by reacting proactively to their commands. The key insight is to include context- and user-awareness in the controller, improving decision-making on how to assist the user. Context-awareness is achieved by inferring the candidate objects to be grasped in a task or scene and automatically computing plans for reaching them. User-awareness is implemented by facilitating the motion toward the most likely object that the user wants to grasp, as well as dynamically recovering from incorrect predictions. Experimental results in a virtual environment of two degrees of freedom control show the capability of this approach to outperform manual control. By robustly predicting user intention, the proposed controller allows subjects to achieve superhuman performance in terms of accuracy and, thereby, usability.https://www.frontiersin.org/articles/10.3389/fnbot.2021.695022/fullphysical human robot interactionmotion intention estimationmotion predictionAI assistancereach and grasp |
| spellingShingle | Sebastijan Veselic Sebastijan Veselic Sebastijan Veselic Claudio Zito Claudio Zito Dario Farina Human-Robot Interaction With Robust Prediction of Movement Intention Surpasses Manual Control Frontiers in Neurorobotics physical human robot interaction motion intention estimation motion prediction AI assistance reach and grasp |
| title | Human-Robot Interaction With Robust Prediction of Movement Intention Surpasses Manual Control |
| title_full | Human-Robot Interaction With Robust Prediction of Movement Intention Surpasses Manual Control |
| title_fullStr | Human-Robot Interaction With Robust Prediction of Movement Intention Surpasses Manual Control |
| title_full_unstemmed | Human-Robot Interaction With Robust Prediction of Movement Intention Surpasses Manual Control |
| title_short | Human-Robot Interaction With Robust Prediction of Movement Intention Surpasses Manual Control |
| title_sort | human robot interaction with robust prediction of movement intention surpasses manual control |
| topic | physical human robot interaction motion intention estimation motion prediction AI assistance reach and grasp |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2021.695022/full |
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