The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch
Open accessAbstract
For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this letter, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp. To this end, we propose an end-to-end action-conditional model that learns regrasping policies from raw visuo-tactile data. This model - a deep, multimodal convolutional network - predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions. Our approach requires neither calibration of the tactile sensors nor any analytical modeling of contact forces, thus reducing the engineering effort required to obtain efficient grasping policies. We train our model with data from about 6450 grasping trials on a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. Across extensive experiments, our approach outperforms a variety of baselines at 1) estimating grasp adjustment outcomes, 2) selecting efficient grasp adjustments for quick grasping, and 3) reducing the amount of force applied at the fingers, while maintaining competitive performance. Finally, we study the choices made by our model and show that it has successfully acquired useful and interpretable grasping behaviors.
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Cites
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Coding and use of tactile signals from the fingertips in object manipulation tasks
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Self-citationDeep learning for detecting robotic grasps
Lenz, I., Lee, H., & Saxena, A. (2015) · The International Journal of Robotics Research · doi:10.1177/0278364914549607 · Cited by 1,634
GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force
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Self-citationSupersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours
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Cited by
Lee, M. A., Zhu, Y., Srinivasan, K., Shah, P., Savarese, S., Fei-Fei, L., Garg, A., & Bohg, J. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8793485 · Cited by 349
Object detection recognition and robot grasping based on machine learning: a survey
Bai, Q., Li, S., Yang, J., Song, Q., Li, Z., & Zhang, X. (2020) · IEEE Access · doi:10.1109/access.2020.3028740 · Cited by 154
A survey of multimodal learning: Methods, applications, and future
Yuan, Y., Li, Z., & Zhao, B. (2025) · ACM Computing Surveys · doi:10.1145/3713070 · Cited by 90
In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning
Suzuki, K., Kanamura, M., Suga, Y., Mori, H., & Ogata, T. (2021) · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9635954 · Cited by 31
See to touch: Learning tactile dexterity through visual incentives
Guzey, I., Dai, Y., Evans, B., Chintala, S., & Pinto, L. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611407 · Cited by 26
Metadata
- Authors
- Calandra, R., Owens, A., Jayaraman, D., Lin, J., Yuan, W., Malik, J., Adelson, E. H., & Levine, S.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2018-10
- Volume
- 3
- Issue
- 4
- Pages
- 3300-3307
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2804941773
- Cited by
- 359
- References
- 40
- FWCI
- 20.48
- Type
- journal article
- Language
- en
- Open access
- green
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- Calandra, R., Owens, A., Jayaraman, D., Lin, J., Yuan, W., Malik, J., Adelson, E. H., & Levine, S. (2018). More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2018.2852779
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