Effects of haptic guidance and disturbance on motor learning: Potential advantage of haptic disturbance
Abstract
One of the primary goals of haptic guidance is to facilitate the learning of complex human motor skills by providing haptic cues that are helpful to induce desired movements. Nevertheless, a majority of previous studies have found that haptic guidance is ineffective, or sometimes even detrimental, to motor skill learning. In this paper, we propose the opposite concept, haptic disturbance, and evaluate its efficacy. In haptic disturbance, haptic cues that interfere with the movements of a learner are presented during training. We designed two methods of haptic disturbance using repulsive and noise-like forces, respectively. The effects of these methods were experimentally assessed, comparatively with the conventional methods of visual learning only and progressive haptic guidance. The motor task was to track a dot moving on a 2D plane with a haptic interface operated with one arm. We found that during training, the progressive haptic guidance showed the best tracking accuracy, but in immediate and delayed retention tests, the noise-like haptic disturbance led to the best performance. The results suggest high potentials for haptic disturbance to be a general strategy for expediting the motor learning process.
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Random noise-based perturbations were more effective than haptic guidance in a path-following task in healthy subjects.
“Random noise-based perturbations were shown to be more effective than haptic guidance in one experiment on a path-following task in healthy subjects (Lee & Choi, 2010).”
From Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2013) · cites this paper as Lee & Choi, 2010
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Gutierrez, S. F. C., Luces, J. V. S., & Hirata, Y. (2022) · Advanced Robotics · doi:10.1080/01691864.2022.2076569 · Cited by 8 · see citing works
Evaluation of driving skills using an HMM-based distance measure
Osgouei, R. H., & Choi, S. (2012) · IEEE International Workshop on Haptic Audio visual Environments and Games · doi:10.1109/have.2012.6374445 · Cited by 8 · see citing works
Self-citationThe effect of haptic cues on motor and perceptual based implicit sequence learning
Kim, D.-W., Johnson, B. J., Gillespie, R. B., & Seidler, R. D. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00130 · Cited by 8 · see citing works
Movement Strategy Discovery during Training via Haptic Guidance
Gibo, T. L., & Abbink, D. A. (2016) · IEEE Transactions on Haptics · doi:10.1109/toh.2016.2516984 · Cited by 8 · see citing works
Trajectory adjustment system for learning based on electrical stimulation
Tatsuno, S., Hayakawa, T., & Ishikawa, M. (2017) · Augmented Human International Conference · doi:10.1145/3041164.3041197 · Cited by 8 · see citing works
Investigating Haptic Feedback in Vision-Deficient Millirobot Telemanipulation
Riaziat, N. D., Erin, Ö., Krieger, A., & Brown, J. D. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3397529 · Cited by 7 · see citing works
A longitudinal study of haptic pitch correction guidance for string instrument players
Yoo, Y., & Choi, S. (2017) · IEEE World Haptics Conference (WHC) · doi:10.1109/whc.2017.7989897 · Cited by 7 · see citing works
Self-citationVibrotactile guidance for drumming learning: Method and perceptual assessment
Lee, I., & Choi, S. (2014) · IEEE Haptics Symposium · doi:10.1109/haptics.2014.6775447 · Cited by 7 · see citing works
Self-citationMotor Skill Learning by Virtual Co-embodiment with an AI Teacher Trained in Human Teaching Behavior
Takita, H., Kodama, D., Hatada, Y., Narumi, T., & Hirose, M. (2024) · ACM Symposium on Applied Perception · doi:10.1145/3675231.3675237 · Cited by 6 · see citing works
Fingertip Force Learning with Enhanced Haptic Sensation using Stochastic Resonance
Chamnongthai, K., Endo, T., Nisar, S., Matsuno, F., Fujimoto, K., & Kosaka, M. (2019) · IEEE World Haptics Conference (WHC) · doi:10.1109/whc.2019.8816114 · Cited by 6 · see citing works
Evaluating the Efficiency of Six-DoF Haptic Rendering-Based Virtual Assembly Training
Zheng, M., Zhao, D., & Barbič, J. (2020) · IEEE Transactions on Haptics · doi:10.1109/toh.2020.3008941 · Cited by 6 · see citing works
The Role of Haptic Interactions with Robots for Promoting Motor Learning
Beckers, N., & Marchal–Crespo, L. (2022) · Springer eBooks · doi:10.1007/978-3-031-08995-4_12 · Cited by 6 · see citing works
Continuous Closed-Loop 4-Degree-of-Freedom Holdable Haptic Guidance
Walker, J. M., & Okamura, A. M. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3020581 · Cited by 6 · see citing works
Guidance-based Two-Dimensional Haptic Contour Rendering for Accessible Photography
Lim, J., Yoo, Y., & Choi, S. (2019) · IEEE World Haptics Conference (WHC) · doi:10.1109/whc.2019.8816151 · Cited by 5 · see citing works
Self-citationReview and Analysis of Platform-Related Performance of Rehabilitation Lower Limb Exoskeletons
Karami, H., Maludrottu, S., Vassallo, C., Laffranchi, M., & Michieli, L. D. (2023) · Actuators · doi:10.3390/act12110406 · Cited by 5 · see citing works
Training strategies for learning a 3D trajectory with accuracy
Rodríguez-Arce, J., Gutiérrez, T., Casado, S., & Sánchez, E. (2010) · IEEE International Workshop on Haptic Audio visual Environments and Games · doi:10.1109/have.2010.5623979 · Cited by 5 · see citing works
Learning a Locomotor Task: With or without Errors?
Schneider, J., Jaeger, L., Hollnagel, C., Riener, R., & Marchal–Crespo, L. (2013) · Biosystems & biorobotics · doi:10.1007/978-3-642-34546-3_3 · Cited by 5 · see citing works
Augmentation of kinesthetic sensation by adding "rotary switch feeling" feedback
Kurihara, Y., Kuniyasu, Y., Hachisu, T., Sato, M., Fukushima, S., & Kajimoto, H. (2012) · Augmented Human International Conference · doi:10.1145/2160125.2160153 · Cited by 4 · see citing works
Haptic gait retraining: Evaluating learning environments for haptic motion training
Lurie, K. L., Santos, J. M., & Shull, P. B. (2010) · IEEE International Workshop on Haptic Audio visual Environments and Games · doi:10.1109/have.2010.5623967 · Cited by 4 · see citing works
Control of Dual-User Teleoperation Systems Design, Stability Analysis, and Performance Evaluation
Khademian, B. (2010) · QSpace (Queen's University Library) · Cited by 4 · see citing works
Zhang, Y., Wang, H., & Shi, B. E. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831286 · Cited by 3 · see citing works
Lee, I., & Choi, S. (2013) · World Haptics Conference · doi:10.1109/whc.2013.6548471 · Cited by 3 · see citing works
Self-citationImproving short-term retention after robotic training by leveraging fixed-gain controllers
Losey, D. P., Blumenschein, L. H., Clark, J. P., & O’Malley, M. K. (2019) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/2055668319866311 · Cited by 3 · see citing works
Miyazaki, T., Tomita, Y., & Kawashima, K. (2025) · Wearable Technologies · doi:10.1017/wtc.2024.30 · Cited by 3 · see citing works
Reshaping Movement Distributions With Limit-Push Robotic Training
Shah, A. K., Sharp, I., Hajissa, E., & Patton, J. L. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2839565 · Cited by 3 · see citing works
Comparative evaluation of performance measures for human driving skills
Osgouei, R. H., Lee, H., & Choi, S. (2013) · Intelligent Service Robotics · doi:10.1007/s11370-013-0134-6 · Cited by 3 · see citing works
Self-citationVisual and Haptic Error Modulating Controllers for Robotic Gait Training
Tsangaridis, P., Obwegeser, D., Maggioni, S., Riener, R., & Marchal–Crespo, L. (2018) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2018.8488011 · Cited by 3 · see citing works
Augmenting Fine Motor Skill Training with Haptic Error Amplification
Clamann, M. P., & KABER, D. B. (2018) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931218621350 · Cited by 3 · see citing works
To Guide or to Disturb - How to Teach Dexterous Skills Using AI?
Park, J., Kwak, D., Lim, J., Lee, T., Kim, S.-G., & Yoo, Y. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712097 · Cited by 2 · see citing works
Dissociating haptic feedback from physical assistance does not improve motor performance
Ivanova, E., Perez, N. P., Eden, J., Yip, Y., & Burdet, E. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340983 · Cited by 2 · see citing works
Haptic-enabled driving training system
Osgouei, R. H., Lee, H., & Choi, S. (2013) · IEEE RO-MAN · doi:10.1109/roman.2013.6628494 · Cited by 2 · see citing works
Self-citationEffect of local-adaptive haptic guidance on a path-following task
Moon, H.-S., & Seo, J. (2017) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas.2017.8204403 · Cited by 2 · see citing works
Haptic role allocation and intention negotiation in human-robot collaboration
Küçükyılmaz, A. (2013) · Repository@Nottingham (University of Nottingham) · doi:10.24385/lincoln.24325816 · Cited by 2 · see citing works
Park, J., Lim, J., Lee, T., & Yoo, Y. (2024) · ACM Symposium on Virtual Reality Software and Technology (VRST) · doi:10.1145/3641825.3689524 · Cited by 1 · see citing works
The interplay between haptic guidance and personality traits in robotic-assisted motor learning
Garzás-Villar, A., Boersma, C., Derumigny, A., Prendergast, J. M., Zgonnikov, A., Cramm, J. M., & Marchal–Crespo, L. (2025) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-025-01709-6 · Cited by 1 · see citing works
Hung, C.-W., Chen, E., Khan, H. S., Hsu, P.-Y., Chen, B., & Nishida, J. (2026) · ACM Symposium on Spatial User Interaction (SUI) · doi:10.1145/3822518.3830065 · Cited by 1 · see citing works
Haptic Augmentation in Soft Tissue Interaction
Jeon, S., Choi, S., & Harders, M. (2014) · Springer series on touch and haptic systems · doi:10.1007/978-1-4471-6533-0_12 · Cited by 1 · see citing works
Self-citationPerformance of simultaneous motion and respiration control under guidance of audio-haptic cues
Xu, M., Wang, D., Zhang, Y., Song, J. J., & Wu, D. (2015) · IEEE World Haptics Conference (WHC) · doi:10.1109/whc.2015.7177748 · Cited by 1 · see citing works
Building and Improving Tactical Agents in Real Time through a Haptic-Based Interface
Stein, G., & González, A. J. (2015) · Journal of Intelligent Systems · doi:10.1515/jisys-2014-0126 · Cited by 1 · see citing works
A novel haptic training method through skill decomposition
Liu, L., Liu, G., & Zhang, Y. (2013) · World Haptics Conference · doi:10.1109/whc.2013.6548480 · Cited by 1 · see citing works
- Authors
- Lee, J., & Choi, S.
- Affiliations
- Pohang University of Science and Technology (KR)
- ORCID
- Seungmoon Choi
- Venue
- 2010 IEEE Haptics Symposium
- Publisher
- IEEE
- Published
- 2010-03
- Pages
- 335-342
- ISSN
- 2324-7347 (ISSN-L)
- OpenAlex
- W2171383895
- Cited by
- 118
- References
- 42
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 8 · 2023: 6 · 2022: 4 · 2021: 3 · 2020: 9 · 2019: 8 · 2018: 5 · 2017: 10 · 2016: 5 · 2015: 6 · 2014: 11 · 2013: 10 · 2012: 12
- FWCI
- 20.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Haptics Symposium (publishedVersion)
- Funders
- National IT Industry Promotion Agency
- Iran Telecommunication Research Center
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Physical Sciences › Engineering › Mechanical Engineering › Teleoperation and Haptic Systems
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- haptic guidance, motor skill learning, haptic feedback, repulsive force, visuomotor tracking, retention, haptic interface
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