Active and efficient motor skill learning method used in a haptic teleoperated system
Abstract
This paper presents a new haptic training method for motor skill acquisition working in a teleoperated system. The method promotes a visuo-motor learning process base on the active attention and participation of the apprentice during training. Using a robot end-effector, the master describes a desired path which is followed by the apprentice using a haptic device. The master aids the apprentice by restricting his motion within upper and lower boundaries parallel to the path being created in real time. The boundaries are located at a given distance established by the master base on the apprentice learning improvement. Using this method, the master does not disturb or control the position and velocity of the apprentice directly which avoids apprentice dependence on the master. A training handdrawing scheme was designed and implemented to evaluate the visuo-haptic method; experimental results show faster and better apprentice's motor performance than using only visual information. These results suggest a possible implementation of the method in complex motors tasks such as calligraphy or surgery, to name a few.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In that teleoperated system, trajectory boundaries are obtained by interpolation from a human master's trajectory rather than by an automated system.
“However, the trajectory boundaries in this teleoperated system are obtained by interpolation from a human master's trajectory, not by an automated system.”
From Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2013) · cites this paper as Garcia-Hernandez & Parra-Vega, 2009
To date, only one approach has realized online adaptation of trajectory boundaries for visuomanual tracking, a planar teleoperated system.
“To our knowledge, to date, only once has such an approach been realized: a planar teleoperated system that adapted trajectory boundaries online for visuomanual tracking (Garcia-Hernandez & Parra-Vega, 2009).”
From Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2013) · cites this paper as Garcia-Hernandez & Parra-Vega, 2009
Visuohaptic feedback training was more effective than visual feedback only for teaching children handwriting.
“To teach children handwriting, visuohaptic feedback training was more effective than visual feedback only (Garcia-Hernandez & Parra-Vega, 2009).”
From Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2013) · cites this paper as Garcia-Hernandez & Parra-Vega, 2009
Cites
Haptic guidance: experimental evaluation of a haptic training method for a perceptual motor skill
Feygin, D., Keehner, M., & Tendick, R. (2003) · Proceedings 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. HAPTICS 2002 · doi:10.1109/haptic.2002.998939 · Cited by 394 · see citing works
Augmented Feedback Presented in a Virtual Environment Accelerates Learning of a Difficult Motor Task
Todorov, E., Shadmehr, R., & Bizzi, E. (1997) · Journal of Motor Behavior · doi:10.1080/00222899709600829 · Cited by 314 · see citing works
Haptic Guidance Improves the Visuo-Manual Tracking of Trajectories
Bluteau, J., Coquillart, S., Payan, Y., & Gentaz, E. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001775 · Cited by 153 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review
Sigrist, R., Rauter, G., Riener, R., & Wolf, P. (2013) · Psychonomic Bulletin & Review · doi:10.3758/s13423-012-0333-8 · Cited by 1,416 · see citing works
Guideband: Intuitive 3d multilevel force guidance on a wristband in virtual reality.
Tsai, H.-R., Chang, Y.-C., Wei, T.-Y., Tsao, C.-A., Koo, X. C.-Y., Wang, H.-C., & Chen, B.-Y. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445262 · Cited by 25 · see citing works
- Authors
- Garcia-Hernandez, N., & Parra-Vega, V.
- Affiliations
- Italian Institute of Technology (IT)
- Venue
- RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 915-920
- ISSN
- 1944-9437 (ISSN-L)
- OpenAlex
- W2158430974
- Cited by
- 9
- References
- 3
- Citations by year
- 2026: 1 · 2024: 1 · 2021: 2 · 2020: 1 · 2018: 1 · 2016: 1 · 2014: 1 · 2012: 1
- FWCI
- 0.96
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE RO-MAN (publishedVersion)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Teleoperation and Haptic Systems
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Keywords
- motor skill learning, haptic teleoperation, active attention, robot end-effector, imitation learning, calligraphy education, surgical training
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review