The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A Review of Tactile Information: Perception and Action Through Touch
Open accessAbstract
Tactile sensing is a key sensor modality for robots interacting with their surroundings. These sensors provide a rich and diverse set of data signals that contain detailed information collected from contacts between the robot and its environment. The data are however not limited to individual contacts and can be used to extract a wide range of information about the objects in the environment as well as the actions of the robot during the interactions. In this article, we provide an overview of tactile information and its applications in robotics. We present a hierarchy consisting of raw, contact, object, and action levels to structure the tactile information, with higher-level information often building upon lower-level information. We discuss different types of information that can be extracted at each level of the hierarchy. The article also includes an overview of different types of robot applications and the types of tactile information that they employ. Finally we end the article with a discussion for future tactile applications which are still beyond the current capabilities of robots.
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Cites
Coding and use of tactile signals from the fingertips in object manipulation tasks
Johansson, R. S., & Flanagan, J. R. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2621 · Cited by 2,063
Johansson, R. S., & Vallbo, A. B. (1979) · The Journal of Physiology · doi:10.1113/jphysiol.1979.sp012619 · Cited by 1,291
Lederman, S. J., & Klatzky, R. L. (2009) · Attention, Perception & Psychophysics · doi:10.3758/app.71.7.1439 · Cited by 1,108
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., & Levine, S. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.049 · Cited by 847
Deep learning for tactile understanding from visual and haptic data
Gao, Y., Hendricks, L. A., Kuchenbecker, K. J., & Darrell, T. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487176 · Cited by 264
Learning Dexterous In-Hand Manipulation
OpenAI, Andrychowicz, M., Baker, B., Chociej, M., Jozefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., et al. (2018) · arXiv · doi:10.48550/arxiv.1808.00177 · Cited by 248
Polymer Micromachined Flexible Tactile Sensor for Three-Axial Loads Detection
Choi, W.-C. (2010) · Transactions on Electrical and Electronic Materials · doi:10.4313/teem.2010.11.3.130 · Cited by 44
Cited by
Neuro-inspired electronic skin for robots
Liu, F., Deswal, S., Christou, A., Sandamirskaya, Y., Kaboli, M., & Dahiya, R. (2022) · Science Robotics · doi:10.1126/scirobotics.abl7344 · Cited by 300
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Metadata
- Authors
- Li, Q., Kroemer, O., Su, Z., Veiga, F. F., Kaboli, M., & Ritter, H. J.
- Venue
- IEEE Transactions on Robotics
- Published
- 2020-12
- Volume
- 36
- Issue
- 6
- Pages
- 1619-1634
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W3041148233
- Cited by
- 332
- References
- 7
- FWCI
- 17.14
- Type
- journal article
- Language
- en
- Open access
- green
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