The DOI matches its Crossref record; 70 of the 76 references it cites were found in a registry or an index.
Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks
Open accessAbstract
Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a long-standing challenge in robotic manipulation. The complex dynamics and high-dimensional configuration spaces of deformables, compared to rigid objects, make manipulation difficult not only for multi-step planning, but even for goal specification. Goals cannot be as easily specified as rigid object poses, and may involve complex relative spatial relations such as "place the item inside the bag". In this work, we develop a suite of simulated benchmarks with 1D, 2D, and 3D deformable structures, including tasks that involve image-based goal-conditioning and multi-step deformable manipulation. We propose embedding goal-conditioning into Transporter Networks, a recently proposed model architecture for learning robotic manipulation that rearranges deep features to infer displacements that can represent pick and place actions. We demonstrate that goal-conditioned Transporter Networks enable agents to manipulate deformable structures into flexibly specified configurations without test-time visual anchors for target locations. We also significantly extend prior results using Transporter Networks for manipulating deformable objects by testing on tasks with 2D and 3D deformables. Supplementary material is available at https://berkeleyautomation.github.io/bags/.
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In the citing paper
“Seita et al. (2021) [34] extend Transporter Net to the goal-conditioned control domain and apply it to cloth-shaping and bag-manipulation tasks in simulation.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [34]
In the citing paper
“Simulation-trained policies are affected since modern simulators cannot accurately emulate the interaction between the gripper and the CDO [29,34,38].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [34]
In the citing paper
“In simulation, Seita et al. (2021a) [34] achieve a 63.3% success rate out of 60 trials for sack opening and a 51.7% success rate for inserting one item in simulation.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [34]
In the citing paper
“The canonical task of bag manipulation is item loading [238] which involves opening the bag, inserting items [34] (see Figure 8b) and lifting the bag.”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [34]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Bathe, K. (1995) · CERN Document Server (European Organization for Nuclear Research) · Cited by 10,651
A survey of robot learning from demonstration
Argall, B. D., Chernova, S., Veloso, M., & Browning, B. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.10.024 · Cited by 3,227
End-to-End Training of Deep Visuomotor PoliciesPreprint
Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.00702 · Cited by 2,641
Large steps in cloth simulation
Baraff, D., & Witkin, A. (1998) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/280814.280821 · Cited by 1,702
Learning dexterous in-hand manipulation
Andrychowicz, O. M., Baker, B., Chociej, M., Józefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., Ray, A., et al. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364919887447 · Cited by 1,643
Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., McGrew, B., Tobin, J., Abbeel, P., & Zaremba, W. (2017) · arXiv · doi:10.48550/arxiv.1707.01495 · Cited by 1,355
(1994) · Aston Publications Explorer (Aston University) · Cited by 1,245
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
Tan, J., Zhang, T., Coumans, E., Iscen, A., Bai, Y., Hafner, D., Bohez, S., & Vanhoucke, V. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.010 · Cited by 735
Self-citationQT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., Quillen, D., Holly, E., Kalakrishnan, M., Vanhoucke, V., et al. (2018) · arXiv · doi:10.48550/arxiv.1806.10293 · Cited by 697
Efficient Training of Artificial Neural Networks for Autonomous Navigation
Pomerleau, D. (1991) · Neural Computation · doi:10.1162/neco.1991.3.1.88 · Cited by 693
Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning
Zeng, A., Song, S., Welker, S., Lee, J., Rodriguez, A., & Funkhouser, T. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593986 · Cited by 606
Self-citationBrockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016) · arXiv · doi:10.48550/arxiv.1606.01540 · Cited by 582
Learning ambidextrous robot grasping policies
Mahler, J., Matl, M., Satish, V., Danielczuk, M., DeRose, B., McKinley, S., & Goldberg, K. (2019) · Science Robotics · doi:10.1126/scirobotics.aau4984 · Cited by 567
Self-citationZeng, A., Song, S., Yu, K., Donlon, E., Hogan, F. R., Bauzá, M., Ma, D., Taylor, O., Liu, M., Romo, E., et al. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461044 · Cited by 498
Self-citationVisual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic ControlPreprint
Ebert, F., Finn, C., Dasari, S., Xie, A., Lee, A. X., & Levine, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00568 · Cited by 480
Sánchez, J. M. O., Ramón, J. A. C., Bouzgarrou, B. C., & Mezouar, Y. (2018) · The International Journal of Robotics Research · doi:10.1177/0278364918779698 · Cited by 454
Maneuver-based motion planning for nonlinear systems with symmetries
Frazz, E., Dahleh, M. A., & Féron, É. (2005) · IEEE Transactions on Robotics · doi:10.1109/tro.2005.852260 · Cited by 444
Maitin-Shepard, J., Cusumano-Towner, M., Lei, J., & Abbeel, P. (2010) · 2010 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2010.5509439 · Cited by 418
Combining self-supervised learning and imitation for vision-based rope manipulation
Nair, A., Chen, D., Agrawal, P., Isola, P., Abbeel, P., Malik, J., & Levine, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989247 · Cited by 260
Sim-to-real reinforcement learning for deformable object manipulation
Matas, J., James, S., & Davison, A. J. (2018) · arXiv · doi:10.48550/arxiv.1806.07851 · Cited by 241
Pathak, D., Mahmoudieh, P., Luo, G., Agrawal, P., Chen, D., Shentu, F., Shelhamer, E., Malik, J., Efros, A. A., & Darrell, T. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00278 · Cited by 203
Transporter Networks: Rearranging the Visual World for Robotic Manipulation
Zeng, A., Florence, P., Tompson, J., Welker, S., Chien, J., Attarian, M., Armstrong, T., Krasin, I., Duong, D., Wahid, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.14406 · Cited by 200
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Florence, P., Manuelli, L., & Tedrake, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.08756 · Cited by 181
Self-citationLearning to Manipulate Deformable Objects without Demonstrations
Wu, Y., Yan, W., Kurutach, T., Pinto, L., & Abbeel, P. (2020) · Robotics: Science and Systems · doi:10.15607/rss.2020.xvi.065 · Cited by 181
Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects
Yan, M., Zhu, Y., Jin, N., & Bohg, J. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2969931 · Cited by 166
Bringing clothing into desired configurations with limited perception
Cusumano-Towner, M., Singh, A., Miller, S., O’Brien, J. F., & Abbeel, P. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980327 · Cited by 144
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Lin, X., Wang, Y., Olkin, J., & Held, D. (2020) · arXiv · doi:10.48550/arxiv.2011.07215 · Cited by 140
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Schulman, J., Ho, J., Lee, C., & Abbeel, P. (2016) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-319-28872-7_20 · Cited by 133 · cited 1×
“In recent years, learning-based approaches for manipulation have grown in popularity to improve the generalization of knot-tying and also to manipulate rope towards general target configurations [52], [46], [49], [57], [64], [61].”
BackgroundDoumanoglou, 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
Learning Predictive Representations for Deformable Objects Using Contrastive Estimation
Yan, W., Vangipuram, A., Abbeel, P., & Pinto, L. (2020) · arXiv · doi:10.48550/arxiv.2003.05436 · Cited by 122
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Erickson, Z., Gangaram, V., Kapusta, A. S., Liu, C. K., & Kemp, C. C. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197411 · Cited by 104
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Osawa, F., Seki, H., & Kamiya, Y. (2007) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2007.p0457 · Cited by 102
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Sun, L., Aragón-Camarasa, G., Rogers, S., & Siebert, J. P. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7138998 · Cited by 94
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Klingbeil, E., Rao, D., Carpenter, B., Ganapathi, V., Ng, A. Y., & Khatib, O. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980287 · Cited by 91
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Corona, E., Alenyà, G., Gabas, A., & Carme Torras, A. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.09.042 · Cited by 90
Introduction to Reinforcement Learning
Sewak, M. (2019) · Springer eBooks · doi:10.1007/978-981-13-8285-7_1 · Cited by 88
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Self-citationA case study of flexible object manipulation
Hopcroft, J. E., Kearney, J. K., & Krafft, D. B. (1991) · The International Journal of Robotics Research · doi:10.1177/027836499101000105 · Cited by 82
VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
Hoque, R., Seita, D., Balakrishna, A., Ganapathi, A., Tanwani, A., Jamali, N., Yamane, K., Iba, S., & Goldberg, K. (2020) · Robotics: Science and Systems XVI · doi:10.15607/rss.2020.xvi.034 · Cited by 74
Self-citation(2003) · doi:10.1109/robot.2003.1242193 · Cited by 73
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Kita, Y., Ueshiba, T., Neo, E., & Kita, N. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152741 · Cited by 73
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Ganapathi, A., Sundaresan, P., Thananjeyan, B., Balakrishna, A., Seita, D., Grannen, J., Hwang, M., Hoque, R., Gonzalez, J. E., Jamali, N., et al. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561980 · Cited by 69
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Balaguer, B., & Carpin, S. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6094992 · Cited by 59
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Sun, L., Aragon-Camarasa, G., Cockshott, P., Rogers, S., & Siebert, J. P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-662-43645-5_16 · Cited by 43
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Verschoor, M., & Jalba, A. C. (2019) · ACM Transactions on Graphics · doi:10.1145/3209887 · Cited by 43
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Hellman, R. B., Tekin, C., van der Schaar, M., & Santos, V. J. (2017) · IEEE Transactions on Haptics · doi:10.1109/toh.2017.2753233 · Cited by 40
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Lippi, M., Poklukar, P., Welle, M. C., Varava, A., Yin, H., Marino, A., & Kragić, D. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340764 · Cited by 39
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Bai, Y., Yu, W., & Liu, C. K. (2016) · Computer Graphics Forum · doi:10.1111/cgf.12852 · Cited by 33
A method for handling a specific part of clothing by dual arms
Kita, Y., Ueshiba, T., Neo, E. S., & Kita, N. (2009) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2009.5354213 · Cited by 30
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Kazerooni, H., & Foley, C. (2005) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2005.844630 · Cited by 21
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Kirchheim, A., Burwinkel, M., & Echelmeyer, W. (2008) · IEEE International Conference on Automation and Logistics (ICAL) · doi:10.1109/ical.2008.4636286 · Cited by 21
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Zeng, A. (2019) · Cited by 17
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Hayashi, N., Suehiro, T., & Kudoh, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989151 · Cited by 11
Dual arm robot fabric wrapping operation using target lines
Hayashi, N., Tomizawa, T., Suehiro, T., & Kudoh, S. (2014) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2014.7090661 · Cited by 9
Research works printed but not linked: 1
- community, 2018, Blender — a 3D modelling and rendering package
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Metadata
- Authors
- Seita, D., Florence, P., Tompson, J., Coumans, E., Sindhwani, V., Goldberg, K., & Zeng, A.
- Affiliations
- University of California, Berkeley (US)
- Google (United States) (US)
- Venue
- 2021 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2021-05-30
- Pages
- 4568-4575
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3207181464
- Cited by
- 130
- References
- 76
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 32 · 2023: 32 · 2022: 29 · 2021: 11
- FWCI
- 38.75
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- deformable object manipulation, robotic manipulation, cables, textiles, bags, pick-and-place, displacement estimation
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