The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Combining self-supervised learning and imitation for vision-based rope manipulation
Open accessAbstract
Manipulation of deformable objects, such as ropes and cloth, is an important but challenging problem in robotics. We present a learning-based system where a robot takes as input a sequence of images of a human manipulating a rope from an initial to goal configuration, and outputs a sequence of actions that can reproduce the human demonstration, using only monocular images as input. To perform this task, the robot learns a pixel-level inverse dynamics model of rope manipulation directly from images in a self-supervised manner, using about 60K interactions with the rope collected autonomously by the robot. The human demonstration provides a high-level plan of what to do and the low-level inverse model is used to execute the plan. We show that by combining the high and low-level plans, the robot can successfully manipulate a rope into a variety of target shapes using only a sequence of human-provided images for direction.
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Cites
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Self-citationA new point matching algorithm for non-rigid registration
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Self-citationHand eye coordination in rope handling
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Cited by
An algorithmic perspective on imitation learning
Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · Foundations and Trends in Robotics · doi:10.1561/2300000053 · Cited by 405
Self-citationOne-Shot Visual Imitation Learning via Meta-Learning
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Self-citationModeling, learning, perception, and control methods for deformable object manipulation
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Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
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Challenges and Outlook in Robotic Manipulation of Deformable Objects
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Metadata
- Authors
- Nair, A., Chen, D., Agrawal, P., Isola, P., Abbeel, P., Malik, J., & Levine, S.
- Venue
- 2017 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 2146-2153
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2592285981
- Cited by
- 255
- References
- 9
- FWCI
- 40.34
- Type
- conference paper
- Language
- en
- Open access
- green
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- Nair, A., Chen, D., Agrawal, P., Isola, P., Abbeel, P., Malik, J., & Levine, S. (2017). Combining self-supervised learning and imitation for vision-based rope manipulation. 2017 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2017.7989247
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