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Deformable object manipulation (DOM) for robots has a wide range of applications in various fields such as industrial, service and health care sectors.
Y. Yang, J. A. Stork, and T. Stoyanov, “Learning to propagate interaction effects for modeling deformable linear objects dynamics,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 1950–1957
1957
Earlier work this paper cites.
D. T. Chen and D. Zeltzer, “Pump it up: Computer animation of a biomechanically based model of muscle using the finite element method,” in Proceedings of the 19th annual conference on computer graphics and interactive techniques , 1992, pp. 89–98
1992
Earlier work this paper cites.
U. Kühnapfel, H. K. Cakmak, and H. Maaß, “Endoscopic surgery training using virtual reality and deformable tissue simulation,” Computers & graphics , vol. 24, no. 5, pp. 671–682, 2000
2000
Earlier work this paper cites.
W. Mollemans, F. Schutyser, J. Van Cleynenbreugel, and P. Suetens, “Tetrahedral mass spring model for fast soft tissue deformation,” in International Symposium on Surgery Simulation and Soft Tissue Modeling . Springer, 2003, pp. 145–154
2003
Earlier work this paper cites.
M. Teschner, B. Heidelberger, M. Muller, and M. Gross, “A versatile and robust model for geometrically complex deformable solids,” in Proceedings Computer Graphics International, 2004. IEEE, 2004, pp. 312–319
2004
Earlier work this paper cites.
R. Goldenthal, D. Harmon, R. Fattal, M. Bercovier, and E. Grinspun, “Efficient simulation of inextensible cloth,” in ACM SIGGRAPH 2007 papers , 2007, pp. 49–es
2007
Earlier work this paper cites.
M. Müller, J. Stam, D. James, and N. Thürey, “Real time physics: class notes,” in ACM siggraph 2008 classes , 2008, pp. 1–90
2008
Earlier work this paper cites.
M. Bergou, M. Wardetzky, S. Robinson, B. Audoly, and E. Grinspun, “Discrete elastic rods,” in ACM SIGGRAPH 2008 papers , 2008, pp. 1–12
2008
Earlier work this paper cites.
A. Myronenko and X. Song, “Point set registration: Coherent point drift,” IEEE transactions on pattern analysis and machine intelligence , vol. 32, no. 12, pp. 2262–2275, 2010
2010
Earlier work this paper cites.
J. Das and N. Sarkar, “Planning and control of an internal point of a deformable object,” in 2010 IEEE International Conference on Robotics and Automation . IEEE, 2010, pp. 2877–2882
2010
Earlier work this paper cites.
Y. Kita, F. Kanehiro, T. Ueshiba, and N. Kita, “Clothes handling based on recognition by strategic observation,” in 2011 11th IEEE-RAS International Conference on Humanoid Robots . IEEE, 2011, pp. 53–58
2011
Earlier work this paper cites.
J. B. Tenenbaum, C. Kemp, T. L. Griffiths, and N. D. Goodman, “How to grow a mind: Statistics, structure, and abstraction,” science , vol. 331, no. 6022, pp. 1279–1285, 2011
2011
Earlier work this paper cites.
M. Cusumano-Towner, A. Singh, S. Miller, J. F. O’Brien, and P. Abbeel, “Bringing clothing into desired configurations with limited perception,” in 2011 IEEE international conference on robotics and automation . IEEE, 2011, pp. 3893–3900
2011
Earlier work this paper cites.
J. A. Fishel and G. E. Loeb, “Sensing tactile microvibrations with the biotac—comparison with human sensitivity,” in 2012 4th IEEE RAS & EMBS international conference on biomedical robotics and biomechatronics (BioRob) . IEEE, 2012, pp. 1122–1127
2012
Earlier work this paper cites.
S. Kinio and A. Patriciu, “A comparative study of h∞ and pid control for indirect deformable object manipulation,” in 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO) . IEEE, 2012, pp. 414–420
2012
Earlier work this paper cites.
N. Haouchine, J. Dequidt, I. Peterlik, E. Kerrien, M.-O. Berger, and S. Cotin, “Image-guided simulation of heterogeneous tissue deformation for augmented reality during hepatic surgery,” in 2013 IEEE international symposium on mixed and augmented reality (ISMAR) . IEEE, 2013, pp. 199–208
2013
Earlier work this paper cites.
J. Schulman, A. Lee, J. Ho, and P. Abbeel, “Tracking deformable objects with point clouds,” in 2013 IEEE International Conference on Robotics and Automation . IEEE, 2013, pp. 1130–1137
2013
Earlier work this paper cites.
D. Navarro-Alarcon, Y.-H. Liu, J. G. Romero, and P. Li, “Model-free visually servoed deformation control of elastic objects by robot manipulators,” IEEE Transactions on Robotics , vol. 29, no. 6, pp. 1457–1468, 2013
2013
Earlier work this paper cites.
W. H. Lui and A. Saxena, “Tangled: Learning to untangle ropes with rgb-d perception. in 2013 ieee/rsj int. conf. on intelligent robots and systems,” 2013
2013
Earlier work this paper cites.
D. Berenson, “Manipulation of deformable objects without modeling and simulating deformation,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2013, pp. 4525–4532
2013
Earlier work this paper cites.
J. Schulman, A. Gupta, S. Venkatesan, M. Tayson-Frederick, and P. Abbeel, “A case study of trajectory transfer through non-rigid registration for a simplified suturing scenario,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2013, pp. 4111–4117
2013
Earlier work this paper cites.
M. C. Gemici and A. Saxena, “Learning haptic representation for manipulating deformable food objects,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2014, pp. 638–645
2014
Earlier work this paper cites.
N. D. Goodman, J. B. Tenenbaum, and T. Gerstenberg, “Concepts in a probabilistic language of thought,” Center for Brains, Minds and Machines (CBMM), Tech. Rep., 2014
2014
Earlier work this paper cites.
M. Macklin, M. Müller, N. Chentanez, and T.-Y. Kim, “Unified particle physics for real-time applications,” ACM Transactions on Graphics (TOG) , vol. 33, no. 4, pp. 1–12, 2014
2014
Earlier work this paper cites.
M. Zollhöfer, M. Nießner, S. Izadi, C. Rehmann, C. Zach, M. Fisher, C. Wu, A. Fitzgibbon, C. Loop, C. Theobalt et al. , “Real-time non-rigid reconstruction using an rgb-d camera,” ACM Transactions on Graphics (ToG) , vol. 33, no. 4, pp. 1–12, 2014
2014
Earlier work this paper cites.
T. Bretl and Z. McCarthy, “Quasi-static manipulation of a kirchhoff elastic rod based on a geometric analysis of equilibrium configurations,” The International Journal of Robotics Research , vol. 33, no. 1, pp. 48–68, 2014
2014
Earlier work this paper cites.
R. A. Newcombe, D. Fox, and S. M. Seitz, “Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real-time,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 343–352
2015
Earlier work this paper cites.
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum, “Human-level concept learning through probabilistic program induction,” Science , vol. 350, no. 6266, pp. 1332–1338, 2015
2015
Earlier work this paper cites.
E. Yoshida, K. Ayusawa, I. G. Ramirez-Alpizar, K. Harada, C. Duriez, and A. Kheddar, “Simulation-based optimal motion planning for deformable object,” in 2015 IEEE international workshop on advanced robotics and its social impacts (ARSO) . IEEE, 2015, pp. 1–6
2015
Earlier work this paper cites.
Y. Li, Y. Yue, D. Xu, E. Grinspun, and P. K. Allen, “Folding deformable objects using predictive simulation and trajectory optimization,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 6000–6006
2015
Earlier work this paper cites.
H. Lin, F. Guo, F. Wang, and Y.-B. Jia, “Picking up a soft 3d object by “feeling” the grip,” The International Journal of Robotics Research , vol. 34, no. 11, pp. 1361–1384, 2015
2015
Earlier work this paper cites.
D. Kruse, R. J. Radke, and J. T. Wen, “Collaborative human-robot manipulation of highly deformable materials,” in 2015 IEEE international conference on robotics and automation (ICRA) . IEEE, 2015, pp. 3782–3787
2015
Earlier work this paper cites.
O. Roussel, A. Borum, M. Taix, and T. Bretl, “Manipulation planning with contacts for an extensible elastic rod by sampling on the submanifold of static equilibrium configurations,” in 2015 IEEE International Conference on Robotics and Automation (ICRA) . Ieee, 2015, pp. 3116–3121
2015
Earlier work this paper cites.
S. Kudoh, T. Gomi, R. Katano, T. Tomizawa, and T. Suehiro, “In-air knotting of rope by a dual-arm multi-finger robot,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 6202–6207
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.
P. Battaglia, R. Pascanu, M. Lai, D. Jimenez Rezende et al. , “Interaction networks for learning about objects, relations and physics,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
W. Qiu and A. Yuille, “Unrealcv: Connecting computer vision to unreal engine,” in Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14 . Springer, 2016, pp. 909–916
2016
Earlier work this paper cites.
T. Collins, A. Bartoli, N. Bourdel, and M. Canis, “Robust, real-time, dense and deformable 3d organ tracking in laparoscopic videos,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2016, pp. 404–412
2016
Earlier work this paper cites.
S. Caccamo, P. Güler, H. Kjellström, and D. Kragic, “Active perception and modeling of deformable surfaces using gaussian processes and position-based dynamics,” in 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) . IEEE, 2016, pp. 530–537
2016
Earlier work this paper cites.
M. Macklin, M. Müller, and N. Chentanez, “Xpbd: position-based simulation of compliant constrained dynamics,” in Proceedings of the 9th International Conference on Motion in Games , 2016, pp. 49–54
2016
Earlier work this paper cites.
D. Navarro-Alarcon, H. M. Yip, Z. Wang, Y.-H. Liu, F. Zhong, T. Zhang, and P. Li, “Automatic 3-d manipulation of soft objects by robotic arms with an adaptive deformation model,” IEEE Transactions on Robotics , vol. 32, no. 2, pp. 429–441, 2016
2016
Earlier work this paper cites.
A. J. Shah and J. A. Shah, “Towards manipulation planning for multiple interlinked deformable linear objects,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 3908–3915
2016
Earlier work this paper cites.
J. Schulman, J. Ho, C. Lee, and P. Abbeel, “Learning from demonstrations through the use of non-rigid registration,” in Robotics Research: The 16th International Symposium ISRR . Springer, 2016, pp. 339–354
2016
Earlier work this paper cites.
P.-C. Yang, K. Sasaki, K. Suzuki, K. Kase, S. Sugano, and T. Ogata, “Repeatable folding task by humanoid robot worker using deep learning,” IEEE Robotics and Automation Letters , vol. 2, no. 2, pp. 397–403, 2016
2016
Earlier work this paper cites.
M. Dou, P. Davidson, S. R. Fanello, S. Khamis, A. Kowdle, C. Rhemann, V. Tankovich, and S. Izadi, “Motion2fusion: Real-time volumetric performance capture,” ACM Transactions on Graphics (ToG) , vol. 36, no. 6, pp. 1–16, 2017
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.
T. Tang, Y. Fan, H.-C. Lin, and M. Tomizuka, “State estimation for deformable objects by point registration and dynamic simulation,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 2427–2433
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.
R. B. Hellman, C. Tekin, M. van der Schaar, and V. J. Santos, “Functional contour-following via haptic perception and reinforcement learning,” IEEE transactions on haptics , vol. 11, no. 1, pp. 61–72, 2017
2017
Earlier work this paper cites.
L. Zaidi, J. A. Corrales, B. C. Bouzgarrou, Y. Mezouar, and L. Sabourin, “Model-based strategy for grasping 3d deformable objects using a multi-fingered robotic hand,” Robotics and Autonomous Systems , vol. 95, pp. 196–206, 2017
2017
Earlier work this paper cites.
V. E. Arriola-Rios and J. L. Wyatt, “A multimodal model of object deformation under robotic pushing,” IEEE Transactions on Cognitive and Developmental Systems , vol. 9, no. 2, pp. 153–169, 2017
2017
Earlier work this paper cites.
N. Lv, J. Liu, X. Ding, J. Liu, H. Lin, and J. Ma, “Physically based real-time interactive assembly simulation of cable harness,” Journal of Manufacturing Systems , vol. 43, pp. 385–399, 2017
2017
Earlier work this paper cites.
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 Transactions on Robotics , vol. 34, no. 1, pp. 272–279, 2017
2017
Earlier work this paper cites.
E. Pignat and S. Calinon, “Learning adaptive dressing assistance from human demonstration,” Robotics and Autonomous Systems , vol. 93, pp. 61–75, 2017
2017
Earlier work this paper cites.
C. Garrett, T. Lozano-Pérez, and L. Kaelbling, “Sample-based methods for factored task and motion planning,” 2017
2017
Earlier work this paper cites.
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. Chen and J. Hu, “A review of haptic simulator for oral and maxillofacial surgery based on virtual reality,” Expert Review of Medical Devices , vol. 15, no. 6, pp. 435–444, 2018
2018
Cited alongside, same era.
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,” The International Journal of Robotics Research , vol. 37, no. 7, pp. 688–716, 2018
2018
Cited alongside, same era.
D. De Gregorio, G. Palli, and L. Di Stefano, “Let’s take a walk on superpixels graphs: Deformable linear objects segmentation and model estimation,” in Asian Conference on Computer Vision . Springer, 2018, pp. 662–677
2018
Cited alongside, same era.
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, p. 103798, 2021
2021
Later among the works it cites.
F. Xia, K. Sun, S. Yu, A. Aziz, L. Wan, S. Pan, and H. Liu, “Graph learning: A survey,” IEEE Transactions on Artificial Intelligence , vol. 2, no. 2, pp. 109–127, 2021
2021
Later among the works it cites.
T. Power and D. Berenson, “Keep it simple: Data-efficient learning for controlling complex systems with simple models,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 1184–1191, 2021
2021
Later among the works it cites.
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
Later among the works it cites.
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T. Tang, C. Wang, and M. Tomizuka, “A framework for manipulating deformable linear objects by coherent point drift,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 3426–3433, 2018
2018
Cited alongside, same era.
J. Sanchez, C. M. Mateo, J. A. Corrales, B.-C. Bouzgarrou, and Y. Mezouar, “Online shape estimation based on tactile sensing and deformation modeling for robot manipulation,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 504–511
2018
Cited alongside, same era.
J. Zhu, B. Navarro, P. Fraisse, A. Crosnier, and A. Cherubini, “Dual-arm robotic manipulation of flexible cables,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 479–484
2018
Cited alongside, same era.
2018
Cited alongside, same era.
D. Mrowca, C. Zhuang, E. Wang, N. Haber, L. F. Fei-Fei, J. Tenenbaum, and D. L. Yamins, “Flexible neural representation for physics prediction,” Advances in neural information processing systems , vol. 31, 2018
2018
Cited alongside, same era.
S. Duenser, J. M. Bern, R. Poranne, and S. Coros, “Interactive robotic manipulation of elastic objects,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 3476–3481
2018
Cited alongside, same era.
M. Ruan, D. McConachie, and D. Berenson, “Accounting for directional rigidity and constraints in control for manipulation of deformable objects without physical simulation,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 512–519
2018
Cited alongside, same era.
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) . IEEE, 2018, pp. 4007–4013
2018
Cited alongside, same era.
A. Koessler, N. R. Filella, B.-C. Bouzgarrou, L. Lequièvre, and J.-A. C. Ramon, “An efficient approach to closed-loop shape control of deformable objects using finite element models,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 1637–1643
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Zhu, A. Cherubini, C. Dune, D. Navarro-Alarcon, F. Alambeigi, D. Berenson, F. Ficuciello, K. Harada, J. Kober, X. Li et al. , “Challenges and outlook in robotic manipulation of deformable objects,” IEEE Robotics & Automation Magazine , vol. 29, no. 3, pp. 67–77, 2022
2022
Later among the works it cites.
Y. Gao, Z. Chen, Y. Ling, J. Yang, Y.-H. Liu, and X. Li, “A hierarchical manipulation scheme for robotic sorting of multiwire cables with hybrid vision,” IEEE/ASME Transactions on Mechatronics , 2022
2022
Later among the works it cites.
G. Narasimhan, K. Zhang, B. Eisner, X. Lin, and D. Held, “Self-supervised transparent liquid segmentation for robotic pouring,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 4555–4561
2022
Later among the works it cites.
Y. Zheng, F. F. Veiga, J. Peters, and V. J. Santos, “Autonomous learning of page flipping movements via tactile feedback,” IEEE Transactions on Robotics , vol. 38, no. 5, pp. 2734–2749, 2022
2022
Later among the works it cites.
Y. Avigal, L. Berscheid, T. Asfour, T. Kröger, and K. Goldberg, “Speedfolding: Learning efficient bimanual folding of garments,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 1–8
2022
Later among the works it cites.
F. Zhang and Y. Demiris, “Learning garment manipulation policies toward robot-assisted dressing,” Science robotics , vol. 7, no. 65, p. eabm6010, 2022
2022
Later among the works it cites.
T. Tang and M. Tomizuka, “Track deformable objects from point clouds with structure preserved registration,” The International Journal of Robotics Research , vol. 41, no. 6, pp. 599–614, 2022
2022
Later among the works it cites.
A. Keipour, M. Bandari, and S. Schaal, “Deformable one-dimensional object detection for routing and manipulation,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4329–4336, 2022
2022
Later among the works it cites.
A. Caporali, R. Zanella, D. De Greogrio, and G. Palli, “Ariadne+: Deep learning–based augmented framework for the instance segmentation of wires,” IEEE Transactions on Industrial Informatics , vol. 18, no. 12, pp. 8607–8617, 2022
2022
Later among the works it cites.
A. Caporali, K. Galassi, R. Zanella, and G. Palli, “Fastdlo: Fast deformable linear objects instance segmentation,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 9075–9082, 2022
2022
Later among the works it cites.
H. Dinkel, J. Xiang, H. Zhao, B. Coltin, T. Smith, and T. Bretl, “Wire point cloud instance segmentation from rgbd imagery with mask r-cnn,” in IEEE Int. Conf. Robot. Autom.(ICRA) Workshop on Representing and Manipulating Deformable Objects , 2022
2022
Later among the works it cites.
S. Zhang, Z. Chen, Y. Gao, W. Wan, J. Shan, H. Xue, F. Sun, Y. Yang, and B. Fang, “Hardware technology of vision-based tactile sensor: A review,” IEEE Sensors Journal , 2022
2022
Later among the works it cites.
J. DelPreto, C. Liu, Y. Luo, M. Foshey, Y. Li, A. Torralba, W. Matusik, and D. Rus, “Actionsense: A multimodal dataset and recording framework for human activities using wearable sensors in a kitchen environment,” Advances in Neural Information Processing Systems , vol. 35, pp. 13 800–13 813, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
L. Pecyna, S. Dong, and S. Luo, “Visual-tactile multimodality for following deformable linear objects using reinforcement learning,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 3987–3994
2022
Later among the works it cites.
M. Yu, K. Lv, H. Zhong, S. Song, and X. Li, “Global model learning for large deformation control of elastic deformable linear objects: An efficient and adaptive approach,” IEEE Transactions on Robotics , vol. 39, no. 1, pp. 417–436, 2022
2022
Later among the works it cites.
C. Wang, Y. Zhang, X. Zhang, Z. Wu, X. Zhu, S. Jin, T. Tang, and M. Tomizuka, “Offline-online learning of deformation model for cable manipulation with graph neural networks,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 5544–5551, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
N. Lv, J. Liu, and Y. Jia, “Dynamic modeling and control of deformable linear objects for single-arm and dual-arm robot manipulations,” IEEE Transactions on Robotics , vol. 38, no. 4, pp. 2341–2353, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Yang, J. A. Stork, and T. Stoyanov, “Online model learning for shape control of deformable linear objects,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 4056–4062
2022
Later among the works it cites.
C. G. Rivera, D. A. Handelman, C. R. Ratto, D. Patrone, and B. L. Paulhamus, “Visual goal-directed meta-imitation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 3767–3773
2022
Later among the works it cites.
H. Ha and S. Song, “Flingbot: The unreasonable effectiveness of dynamic manipulation for cloth unfolding,” in Conference on Robot Learning . PMLR, 2022, pp. 24–33
2022
Later among the works it cites.
A. Simeonov, Y. Du, A. Tagliasacchi, J. B. Tenenbaum, A. Rodriguez, P. Agrawal, and V. Sitzmann, “Neural descriptor fields: Se (3)-equivariant object representations for manipulation,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 6394–6400
2022
Later among the works it cites.
2022
Later among the works it cites.
W. Huang, P. Abbeel, D. Pathak, and I. Mordatch, “Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,” in International Conference on Machine Learning . PMLR, 2022, pp. 9118–9147
2022
Later among the works it cites.
X. Lin, Z. Huang, Y. Li, J. B. Tenenbaum, D. Held, and C. Gan, “Diffskill: Skill abstraction from differentiable physics for deformable object manipulations with tools,” in International Conference on Learning Representations (ICLR) , 2022
2022
Later among the works it cites.
X. Lin, C. Qi, Y. Zhang, Z. Huang, K. Fragkiadaki, Y. Li, C. Gan, and D. Held, “Planning with spatial-temporal abstraction from point clouds for deformable object manipulation,” in Conference on Robot Learning (CoRL) , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2023
Closest in time.
2023
Closest in time.
H. A. Kadi and K. Terzić, “Data-driven robotic manipulation of cloth-like deformable objects: The present, challenges and future prospects,” Sensors , vol. 23, no. 5, p. 2389, 2023
2023
Closest in time.
J. Ma, S. Hu, J. Fu, and G. Chen, “A hierarchical attention detector for bearing surface defect detection,” Expert Systems with Applications , p. 122365, 2023
2023
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K. Lv, M. Yu, Y. Pu, X. Jiang, G. Huang, and X. Li, “Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 7119–7125
2023
Closest in time.
M. Yu, K. Lv, C. Wang, M. Tomizuka, and X. Li, “A coarse-to-fine framework for dual-arm manipulation of deformable linear objects with whole-body obstacle avoidance,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 10 153–10 159
2023
Closest in time.
Y. Huang, C. Xia, X. Wang, and B. Liang, “Learning graph dynamics with external contact for deformable linear objects shape control,” IEEE Robotics and Automation Letters , 2023
2023
Closest in time.
Y. Ren, R. Chen, and Y. Cong, “Autonomous manipulation learning for similar deformable objects via only one demonstration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 069–17 078
2023
Closest in time.
2023
Closest in time.
A. Brohan, Y. Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian et al. , “Do as i can, not as i say: Grounding language in robotic affordances,” in Conference on Robot Learning . PMLR, 2023, pp. 287–318
2023
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2023
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2023
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2023
Closest in time.
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for embodied control,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9493–9500
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
A. Wu, A. Piergiovanni, and M. S. Ryoo, “Action-conditioned convolutional future regression models for robot imitation learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 2035–2037
2037
Closest in time.