Bayesian nonparametric learning of cloth models for real-time state estimation
Open accessAbstract
Robotic solutions to clothing assistance can significantly improve quality of life for the elderly and disabled. Real-time estimation of the human-cloth relationship is crucial for efficient learning of motor skills for robotic clothing assistance. The major challenge involved is cloth-state estimation due to inherent nonrigidity and occlusion. In this study, we present a novel framework for real-time estimation of the cloth state using a low-cost depth sensor, making it suitable for a feasible social implementation. The framework relies on the hypothesis that clothing articles are constrained to a low-dimensional latent manifold during clothing tasks. We propose the use of manifold relevance determination (MRD) to learn an offline cloth model that can be used to perform informed cloth-state estimation in real time. The cloth model is trained using observations from a motion capture system and depth sensor. MRD provides a principled probabilistic framework for inferring the accurate motion-capture state when only the noisy depth sensor feature state is available in real time. The experimental results demonstrate that our framework is capable of learning consistent task-specific latent features using few data samples and has the ability to generalize to unseen environmental settings. We further present several factors that affect the predictive performance of the learned cloth-state model.
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?
Robust real-time estimation of topological relationships between body and cloth is essential for real-world trials.
“Robust real-time estimation [300,310,311,312] of such topological relationships is essential for real-world trials.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [300]
Cites
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354 · see citing works
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705 · see citing works
Multidimensional binary search trees used for associative searching
Bentley, J. L. (1975) · Communications of the ACM · doi:10.1145/361002.361007 · Cited by 7,659 · see citing works
ROS: an open-source Robot Operating System
Quigley, M. (2009) · Medical Entomology and Zoology · Cited by 7,121 · see citing works
Bradski, G. R. (2000) · Medical Entomology and Zoology · Cited by 5,994 · see citing works
The unscented Kalman filter for nonlinear estimation
Wan, E. A., & van der Merwe, R. (2002) · IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium (AS-SPCC) · doi:10.1109/asspcc.2000.882463 · Cited by 4,373 · see citing works
Least-squares estimation of transformation parameters between two point patterns
Umeyama, S. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.88573 · Cited by 2,429 · see citing works
Computer Vision Face Tracking For Use in a Perceptual User Interface
Gary, R. B. (1998) · Cited by 1,400 · see citing works
Bayesian Gaussian Process Latent Variable Model
Titsias, M. K., & Lawrence, N. D. (2010) · International Conference on Artificial Intelligence and Statistics · Cited by 378 · see citing works
Ensemble of shape functions for 3D object classification
Wohlkinger, W., & Vincze, M. (2011) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2011.6181760 · Cited by 359 · see citing works
A geometric approach to robotic laundry folding
Miller, S., van den Berg, J., Fritz, M., Darrell, T., Goldberg, K., & Abbeel, P. (2011) · The International Journal of Robotics Research · doi:10.1177/0278364911430417 · Cited by 238 · see citing works
Learning Shared Latent Structure for Image Synthesis and Robotic Imitation
Shon, A. P., Grochow, K., Hertzmann, A., et al. (2005) · Cited by 179 · see citing works
Probabilistic movement modeling for intention inference in human–robot interaction
Wang, Z., Mülling, K., Deisenroth, M. P., Ben Amor, H., Vogt, D., Schölkopf, B., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913478447 · Cited by 171 · see citing works
Doumanoglou, A., Kargakos, A., Kim, T.-K., & Malassiotis, S. (2014) · 2014 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2014.6906974 · Cited by 123 · see citing works
Reinforcement learning of clothing assistance with a dual-arm robot
Tamei, T., Matsubara, T., Rai, A., & Shibata, T. (2011) · 2011 11th IEEE-RAS International Conference on Humanoid Robots · doi:10.1109/humanoids.2011.6100915 · Cited by 103 · see citing works
Self-citationParametrized shape models for clothing
Miller, S. D., Fritz, M., Darrell, T., & Abbeel, P. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980453 · Cited by 92 · see citing works
Classification of clothing using interactive perception
Willimon, B., Birchfield, S. T., & Walker, I. D. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980336 · Cited by 87 · see citing works
Learning GP-BayesFilters via Gaussian process latent variable models
Ko, J., & Fox, D. (2010) · Autonomous Robots · doi:10.1007/s10514-010-9213-0 · Cited by 78 · see citing works
User modelling for personalised dressing assistance by humanoid robots
Gao, Y., Chang, H. J., & Demiris, Y. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353617 · Cited by 71 · see citing works
Character Motion Synthesis by Topology Coordinates
Ho, E. S. L., & Komura, T. (2009) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2009.01369.x · Cited by 65 · see citing works
Colomé, A., Planells, A., & Torras, C. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139990 · Cited by 61 · see citing works
Variational Gaussian Process Dynamical SystemsPreprint
Damianou, A., Titsias, M. K., & Lawrence, N. D. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1107.4985 · Cited by 55 · see citing works
Personalized Assistance for Dressing Users
Klee, S. D., Ferreira, B. Q., Silva, R., Costeira, J. P., Melo, F. S., & Veloso, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25554-5_36 · Cited by 51 · see citing works
Learning RGB-D descriptors of garment parts for informed robot grasping
Arnau Ramisa, A., Alenyà, G., Moreno-Noguer, F., & Carme Torras, A. (2014) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.06.025 · Cited by 50 · see citing works
Manifold Relevance DeterminationPreprint
Damianou, A., Ek, C. H., Titsias, M. K., et al. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.4610 · Cited by 48 · see citing works
Using dimensionality reduction to exploit constraints in reinforcement learning
Bitzer, S., Howard, M., & Vijayakumar, S. (2010) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2010.5650243 · Cited by 43 · see citing works
Bottom dressing by a life-sized humanoid robot provided failure detection and recovery functions
Yamazaki, K., Oya, R., Nagahama, K., Okada, K., & Inaba, M. (2014) · 2014 IEEE/SICE International Symposium on System Integration · doi:10.1109/sii.2014.7028101 · Cited by 42 · see citing works
Clothes handling based on recognition by strategic observation
Kita, Y., Kanehiro, F., Ueshiba, T., & Kita, N. (2011) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2011.6100817 · Cited by 39 · see citing works
Huang, S. H., Pan, J., Mulcaire, G., & Abbeel, P. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353475 · Cited by 39 · see citing works
FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation
Arnau Ramisa, A., Alenyà, G., Moreno-Noguer, F., & Carme Torras, A. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696446 · Cited by 32 · see citing works
Koganti, N., Tamei, T., Matsubara, T., & Shibata, T. (2014) · The 23rd IEEE International Symposium on Robot and Human Interactive Communication · doi:10.1109/roman.2014.6926241 · Cited by 24 · see citing works
Self-citationCloth dynamics modeling in latent spaces and its application to robotic clothing assistance
Koganti, N., Ngeo, J. G., Tomoya, T., Ikeda, K., & Shibata, T. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353860 · Cited by 23 · see citing works
Self-citationA Constraint-Aware Motion Planning Algorithm for Robotic Folding of Clothes
Lakshmanan, K., Sachdev, A., Xie, Z., Berenson, D., Goldberg, K. Y., & Abbeel, P. (2013) · Springer tracts in advanced robotics · doi:10.1007/978-3-319-00065-7_37 · Cited by 22 · see citing works
Latent space policy search for robotics
Luck, K. S., Neumann, G. M., Berger, E., Peters, J., & Amor, H. B. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6942745 · Cited by 22 · see citing works
A method of state recognition of dressing clothes based on dynamic state matching
Yamazaki, K., Oya, R., Nagahama, K., & Inaba, M. (2013) · Proceedings of the 2013 IEEE/SICE International Symposium on System Integration · doi:10.1109/sii.2013.6776728 · Cited by 20 · see citing works
Yamazaki, K. (2016) · Autonomous Robots · doi:10.1007/s10514-016-9559-z · Cited by 12 · see citing works
Clothing Extraction by Coarse Region Localization and Fine Foreground/Background Estimation
Wu, X. Y., Zhao, B., Liang, L.-L., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-35728-2_30 · Cited by 9 · see citing works
Recognizing clothing states using 3D data observed from multiple directions
Kita, Y., Ueshiba, T., Kanehiro, F., & Kita, N. (2013) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2013.7029980 · Cited by 6 · see citing works
Interactive Perception of Rigid and Non-Rigid Objects
Willimon, B., Birchfield, S. T., & Walker, I. D. (2012) · International Journal of Advanced Robotic Systems · doi:10.5772/53810 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Modeling, learning, perception, and control methods for deformable object manipulation
Yin, H., Varava, A., & Kragic, D. (2021) · Science Robotics · doi:10.1126/scirobotics.abd8803 · Cited by 299 · see citing works
Probabilistic real-time user posture tracking for personalized robot-assisted dressing
Zhang, F., Cully, A., & Demiris, Y. (2019) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2904461 · Cited by 70 · see citing works
A framework for robotic clothing assistance by imitation learning
Joshi, R. P., Koganti, N., & Shibata, T. (2019) · Advanced Robotics · doi:10.1080/01691864.2019.1636715 · Cited by 50 · see citing works
Self-citationKadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 19 · see citing works
“Robust real-time estimation [300,310–312] of such topological relationships is essential for real-world trials.”
Background
- Authors
- Koganti, N., Tamei, T., Ikeda, K., & Shibata, T.
- Affiliations
- Nara Institute of Science and Technology (JP)
- Kyushu Institute of Technology (JP)
- Venue
- IEEE Transactions on Robotics
- Published
- 2017-08
- Volume
- 33
- Issue
- 4
- Pages
- 916-931
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W2615384492
- Cited by
- 41
- References
- 40
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 1 · 2023: 5 · 2022: 5 · 2021: 4 · 2020: 7 · 2019: 8 · 2018: 4 · 2017: 2
- FWCI
- 3.61
- Type
- journal article
- Language
- en
- Open access
- Locations
- IEEE Transactions on Robotics (publishedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion)
- Funders
- Grant-in-Aid for Scientific Research from Japan Society for the Promotion of Science (16H01749)
- ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan) (2015-PM07-36-01)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Keywords
- Bayesian nonparametric models, latent manifold, depth sensors, motion capture, real-time estimation, occlusion
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
