Fetching the paper…
Reading the bibliography…
Deformable object manipulation (DOM) is an emerging research problem in robotics.
J. C. Mutter, “Plastic surgeon Vishal Kapoor, MD performing abdominoplasty surgery.” Oct 2010. [Online]. Available: https://commons.wikimedia.org/wiki/File:Vishal_Kapoor_MD_TummyTuck_Suture.jpg
2010
Earlier work this paper cites.
J. Van Den Berg, S. Miller, K. Goldberg, and P. Abbeel, “Gravity-based robotic cloth folding,” in Algorithmic Foundations of Robotics IX . Springer, 2010, pp. 409–424
2010
Earlier work this paper cites.
T. Tamei, T. Matsubara, A. Rai, and T. Shibata, “Reinforcement learning of clothing assistance with a dual-arm robot,” in 2011 11th IEEE-RAS International Conference on Humanoid Robots . IEEE, 2011, pp. 733–738
2011
Earlier work this paper cites.
P. Jiménez, “Survey on model-based manipulation planning of deformable objects,” Robotics and Computer-integrated Manufacturing , vol. 28, no. 2, pp. 154–163, 2012
2012
Earlier work this paper cites.
S. Miller, J. Van Den Berg, M. Fritz, T. Darrell, K. Goldberg, and P. Abbeel, “A geometric approach to robotic laundry folding,” Int. J. of Robotics Research , vol. 31, no. 2, pp. 249–267, 2012
2012
Earlier work this paper cites.
P. Güler, Y. Bekiroglu, X. Gratal, K. Pauwels, and D. Kragic, “What’s in the container? classifying object contents from vision and touch,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2014, pp. 3961–3968
2014
Earlier work this paper cites.
I. Ramirez-Alpizar, K. Harada, and E. Yoshida, “Motion planning for dual-arm assembly of ring-shaped elastic objects,” in IEEE-RAS International Conference on Humanoid Robots , 2014, pp. 594–600
2014
Earlier work this paper cites.
C. Della Santina, G. Grioli, M. Catalano, A. Brando, and A. Bicchi, “Dexterity augmentation on a synergistic hand: the pisa/iit softhand+,” in 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) . IEEE, 2015, pp. 497–503
2015
Earlier work this paper cites.
L. Sun, G. Aragon-Camarasa, S. Rogers, and J. P. Siebert, “Accurate garment surface analysis using an active stereo robot head with application to dual-arm flattening,” in 2015 IEEE international conference on robotics and automation (ICRA) . IEEE, 2015, pp. 185–192
2015
Earlier work this paper cites.
Y. Li, D. Xu, Y. Yue, Y. Wang, S.-F. Chang, E. Grinspun, and P. K. Allen, “Regrasping and unfolding of garments using predictive thin shell modeling,” in 2015 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2015, pp. 1382–1388
2015
Earlier work this paper cites.
E. Yoshida, K. Ayusawa, I. G. Ramirez-Alpizar, K. Harada, and C. Duriez, “Simulation-based optimal motion planning for deformable object,” in 2015 IEEE International Workshop in Advanced Robotics and its Social Impacts (ARSO) , 2015
2015
Earlier work this paper cites.
J. Alonso-Mora, R. Knepper, R. Siegwart, and D. Rus, “Local motion planning for collaborative multi-robot manipulation of deformable objects,” in 2015 IEEE international conference on robotics and automation (ICRA) . IEEE, 2015, pp. 5495–5502
2015
Earlier work this paper cites.
A. X. Lee, H. Lu, A. Gupta, S. Levine, and P. Abbeel, “Learning force-based manipulation of deformable objects from multiple demonstrations,” in 2015 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2015, pp. 177–184
2015
Earlier work this paper cites.
W. Yuan, M. A. Srinivasan, and E. H. Adelson, “Estimating object hardness with a gelsight touch sensor,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 208–215
2016
Earlier work this paper cites.
S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” J. of Machine Learning Research , vol. 17, no. 39, pp. 1–40, 2016
2016
Earlier work this paper cites.
D. McConachie and D. Berenson, “Bandit-Based Model Selection for Deformable Object Manipulation,” Workshop on the Algorithmic Foundations of Robotics (WAFR) , 2016
2016
Earlier work this paper cites.
P. W. Battaglia, R. Pascanu, M. Lai, D. Rezende, and K. Kavukcuoglu, “Interaction networks for learning about objects, relations and physics,” 2016
2016
Earlier work this paper cites.
E. Coevoet, A. Escande, and C. Duriez, “Optimization-based inverse model of soft robots with contact handling,” IEEE Robotics and Automation Letters , vol. 2, no. 3, pp. 1413–1419, 2017
2017
Earlier work this paper cites.
A. Nair, D. Chen, P. Agrawal, P. Isola, P. Abbeel, J. Malik, and S. Levine, “Combining self-supervised learning and imitation for vision-based rope manipulation,” in 2017 IEEE international conference on robotics and automation (ICRA) . IEEE, 2017, pp. 2146–2153
2017
Earlier work this paper cites.
A. Petit, V. Lippiello, G. A. Fontanelli, and B. Siciliano, “Tracking elastic deformable objects with an rgb-d sensor for a pizza chef robot,” Robotics and Autonomous Systems , vol. 88, pp. 187–201, 2017
2017
Earlier work this paper cites.
W. Yuan, S. Dong, and E. H. Adelson, “Gelsight: High-resolution robot tactile sensors for estimating geometry and force,” Sensors , vol. 17, no. 12, p. 2762, 2017
2017
Earlier work this paper cites.
E. Coevoet, T. Morales-Bieze, F. Largilliere, Z. Zhang, M. Thieffry, M. Sanz-Lopez, B. Carrez, D. Marchal, O. Goury, J. Dequidt et al. , “Software toolkit for modeling, simulation, and control of soft robots,” Advanced Robotics , vol. 31, no. 22, pp. 1208–1224, 2017
2017
Earlier work this paper cites.
A. Wolf, “Japanese robotics biz helps lick laundry,” Dec 2017. [Online]. Available: https://www.twice.com/product/japans-seven-dreamers-robot-solution-folding-laundry
2017
Earlier work this paper cites.
F. Nadon, A. J. Valencia, and P. Payeur, “Multi-modal sensing and robotic manipulation of non-rigid objects: A survey,” Robotics , vol. 7, no. 4, p. 74, 2018
2018
Earlier work this paper cites.
J. Sanchez, J.-A. Corrales, B.-C. Bouzgarrou, and Y. Mezouar, “Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey,” Int. J. of Robotics Research , 2018
2018
Earlier work this paper cites.
F. Alambeigi, Z. Wang, R. Hegeman, Y.-H. Liu, and M. Armand, “Autonomous data-driven manipulation of unknown anisotropic deformable tissues using unmodelled continuum manipulators,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 254–261, 2018
2018
Earlier work this paper cites.
D. M. Vogt, K. P. Becker, B. T. Phillips, M. A. Graule, R. D. Rotjan, T. M. Shank, E. E. Cordes, R. J. Wood, and D. F. Gruber, “Shipboard design and fabrication of custom 3d-printed soft robotic manipulators for the investigation of delicate deep-sea organisms,” PLOS ONE , vol. 13, no. 8, pp. 1–16, 08 2018
2018
Earlier work this paper cites.
F. Ficuciello, A. Migliozzi, E. Coevoet, A. Petit, and C. Duriez, “Fem-based deformation control for dexterous manipulation of 3d soft objects,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 4007–4013
2018
Cited alongside, same era.
W. Yuan, Y. Mo, S. Wang, and E. H. Adelson, “Active clothing material perception using tactile sensing and deep learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 4842–4849
2018
Cited alongside, same era.
J. Matas, S. James, and A. J. Davison, “Sim-to-real reinforcement learning for deformable object manipulation,” in Conference on Robot Learning . PMLR, 2018, pp. 734–743
2018
Cited alongside, same era.
D. Navarro-Alarcon and Y.-H. Liu, “Fourier-based shape servoing: A new feedback method to actively deform soft objects into desired 2-D image contours,” IEEE Trans. on Robotics , vol. 34, no. 1, pp. 272–1279, 2018
2018
Cited alongside, same era.
V. E. Arriola-Rios, P. Guler, F. Ficuciello, D. Kragic, B. Siciliano, and J. L. Wyatt, “Modeling of deformable objects for robotic manipulation: A tutorial and review,” Frontiers in Robotics and AI , vol. 7, 2020
2020
Later among the works it cites.
A. Cherubini, V. Ortenzi, A. Cosgun, R. Lee, and P. Corke, “Model-free vision-based shaping of deformable plastic materials,” The Int. J. of Robotics Research , 2020
2020
Later among the works it cites.
H. Liu, M. Selvaggio, P. Ferrentino, R. Moccia, S. Pirozzi, U. Bracale, and F. Ficuciello, “The musha hand ii: A multi-functional hand for robot-assisted laparoscopic surgery,” IEEE/ASME Transactions on Mechatronics , 2020
2020
Later among the works it cites.
S. Abondance, C. B. Teeple, and R. J. Wood, “A dexterous soft robotic hand for delicate in-hand manipulation,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 5502–5509, 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Li, X. Su, and Y.-H. Liu, “Vision-based robotic manipulation of flexible pcbs,” IEEE/ASME Transactions on Mechatronics , vol. 23, no. 6, pp. 2739–2749, 2018
2018
Cited alongside, same era.
X. Li, X. Su, Y. Gao, and Y.-H. Liu, “Vision-based robotic grasping and manipulation of usb wires,” in IEEE International Conference on Robotics and Automation , 2018, pp. 3482–3487
2018
Cited alongside, same era.
M. Ruan, D. Mc Conachie, and D. Berenson, “Accounting for directional rigidity and constraints in control for manipulation of deformable objects without physical simulation,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 512–519
2018
Cited alongside, same era.
A. Clegg, W. Yu, J. Tan, C. K. Liu, and G. Turk, “Learning to dress: Synthesizing human dressing motion via deep reinforcement learning,” ACM Transactions on Graphics (TOG) , vol. 37, no. 6, pp. 1–10, 2018
2018
Cited alongside, same era.
F. Alambeigi, Z. Wang, R. Hegeman, Y.-H. Liu, and M. Armand, “A robust data-driven approach for online learning and manipulation of unmodeled 3-d heterogeneous compliant objects,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 4140–4147, 2018
2018
Cited alongside, same era.
J. Zhu, B. Navarro, R. Passama, P. Fraisse, A. Crosnier, and A. Cherubini, “Robotic manipulation planning for shaping deformable linear objects with environmental contacts,” IEEE Robotics and Automation Letters , vol. 5, no. 1, pp. 16–23, 2019
2019
Cited alongside, same era.
R. Herguedas, G. López-Nicolás, R. Aragüés, and C. Sagüés, “Survey on multi-robot manipulation of deformable objects,” in 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) . IEEE, 2019, pp. 977–984
2019
Cited alongside, same era.
Z. Hu, T. Han, P. Sun, J. Pan, and D. Manocha, “3-d deformable object manipulation using deep neural networks,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 4255–4261, 2019
2019
Cited alongside, same era.
M. Yan, Y. Zhu, N. Jin, and J. Bohg, “Self-supervised learning of state estimation for manipulating deformable linear objects,” IEEE robotics and automation letters , vol. 5, no. 2, pp. 2372–2379, 2020
2020
Later among the works it cites.
Y. She, S. Wang, S. Dong, N. Sunil, A. Rodriguez, and E. Adelson, “Cable manipulation with a tactile-reactive gripper,” in Robotics: Science and Systems , 2020
2020
Later among the works it cites.
R. Lagneau, A. Krupa, and M. Marchal, “Automatic shape control of deformable wires based on model-free visual servoing,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 5252–5259, 2020
2020
Later among the works it cites.
A. J. Valencia and P. Payeur, “Combining self-organizing and graph neural networks for modeling deformable objects in robotic manipulation,” Frontiers in Robotics and AI , vol. 7, 2020
2020
Later among the works it cites.
D. M c Conachie, T. Power, P. Mitrano, and D. Berenson, “Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces,” IEEE Robotics and Automation Letters (RA-L) , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
T. Li, K. Srinivasan, M. Q.-H. Meng, W. Yuan, and J. Bohg, “Learning hierarchical control for robust in-hand manipulation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 8855–8862
2020
Later among the works it cites.
2020
Later among the works it cites.
M. Aranda, J. A. C. Ramon, Y. Mezouar, A. Bartoli, and E. Özgür, “Monocular visual shape tracking and servoing for isometrically deforming objects,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 7542–7549
2020
Later among the works it cites.
F. Nadon and P. Payeur, “Grasp selection for in-hand robotic manipulation of non-rigid objects with shape control,” in 2020 IEEE International Systems Conference (SysCon) . IEEE, 2020, pp. 1–8
2020
Later among the works it cites.
A. Verleysen, M. Biondina, and F. Wyffels, “Video dataset of human demonstrations of folding clothing for robotic folding,” The International Journal of Robotics Research , vol. 39, no. 9, pp. 1031–1036, 2020
2020
Later among the works it cites.
J. Borràs, G. Alenyà, and C. Torras, “A grasping-centered analysis for cloth manipulation,” IEEE Transactions on Robotics , vol. 36, no. 3, pp. 924–936, 2020
2020
Later among the works it cites.
A. Clegg, Z. Erickson, P. Grady, G. Turk, C. C. Kemp, and C. K. Liu, “Learning to collaborate from simulation for robot-assisted dressing,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 2746–2753, 2020
2020
Later among the works it cites.
M. Laranjeira, C. Dune, and V. Hugel, “Catenary-based visual servoing for tether shape control between underwater vehicles,” Ocean Engineering , vol. 200, p. 107018, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0029801820300949
2020
Later among the works it cites.
H. Yin, A. Varava, and D. Kragic, “Modeling, learning, perception, and control methods for deformable object manipulation,” Science Robotics , vol. 6, no. 54, 2021
2021
Closest in time.
Y. Hao and Y. Visell, “Beyond soft hands: Efficient grasping with non-anthropomorphic soft grippers,” Frontiers in Robotics and AI , vol. 8, p. 210, 2021. [Online]. Available: https://www.frontiersin.org/article/10.3389/frobt.2021.632006
2021
Closest in time.
G. Rouhafzay, A.-M. Cretu, and P. Payeur, “Transfer of learning from vision to touch: A hybrid deep convolutional neural network for visuo-tactile 3d object recognition,” Sensors , vol. 21, no. 1, p. 113, 2021
2021
Closest in time.
J. Zhu, D. Navarro-Alarcon, R. Passama, and A. Cherubini, “Vision-based manipulation of deformable and rigid objects using subspace projections of 2d contours,” Robotics and Autonomous Systems , vol. 142, 2021
2021
Closest in time.
T. Power and D. Berenson, “Keep it Simple: Data-efficient Learning for Controlling Complex Systems with Simple Models,” IEEE Robotics and Automation Letters (RA-L) , 2021
2021
Closest in time.
P. Mitrano, D. McConachie, and D. Berenson, “Learning where to trust unreliable models in an unstructured world for deformable object manipulation,” Science Robotics , vol. 6, no. 54, p. eabd8170, 2021
2021
Closest in time.
P. Zhou, J. Zhu, S. Huo, and D. Navarro-Alarcon, “LaSeSOM: A latent and semantic representation framework for soft object manipulation,” IEEE Robotics and Automation Letters , 2021
2021
Closest in time.
S. Li, N. Figueroa, A. Shah, and J. A. Shah, “Provably Safe and Efficient Motion Planning with Uncertain Human Dynamics,” in Proceedings of Robotics: Science and Systems , Virtual, July 2021
2021
Closest in time.
J. Luo, E. Solowjow, C. Wen, J. A. Ojea, and A. M. Agogino, “Deep reinforcement learning for robotic assembly of mixed deformable and rigid objects,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 2062–2069
2069
Closest in time.