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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Seita et al. extend Transporter Net to the goal-conditioned control domain and apply it to cloth-shaping and bag-manipulation tasks in simulation.
“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]
Simulation-trained policies are affected because modern simulators cannot accurately emulate the interaction between the gripper and the CDO.
“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 simulation, Seita et al. 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, 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]
The canonical task of bag manipulation is item loading which involves opening the bag, inserting items and lifting the bag.
“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]
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Self-citationLearning Visual Feedback Control for Dynamic Cloth Folding
Hietala, J., Blanco–Mulero, D., Alcan, G., & Kyrki, V. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981376 · Cited by 40 · see citing works
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement
Goyal, A., Mousavian, A., Paxton, C., Chao, Y.-W., Okorn, B., Deng, J., & Fox, D. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01437 · Cited by 35 · see citing works
RoboCraft: Learning to see, simulate, and shape elasto-plastic objects in 3D with graph networks
Shi, H., Xu, H., Huang, Z., Li, Y., & Wu, J. (2023) · The International Journal of Robotics Research · doi:10.1177/02783649231219020 · Cited by 33 · see citing works
Unfolding the Literature: A Review of Robotic Cloth Manipulation
Longhini, A., Wang, Y., Garcia-Camacho, I., Blanco–Mulero, D., Moletta, M., Welle, M. C., Alenyà, G., Yin, H., Erickson, Z., Held, D., et al. (2024) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-022723-033252 · Cited by 38 · see citing works
RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks
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Benchmarking the Sim-to-Real Gap in Cloth Manipulation
Blanco–Mulero, D., Barbany, O., Alcan, G., Colomé, A., Carme Torras, A., & Kyrki, V. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3360814 · Cited by 31 · see citing works
Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention
Mo, K., Xia, C., Wang, X., Deng, Y., Gao, X., & Liang, B. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3229573 · Cited by 26 · see citing works
Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies
Sundaresan*, P., Grannen*, J., Thananjeyan, B., Balakrishna, A., Ichnowski, J., Novoseller, E., Hwang, M., Laskey, M., Gonzalez, J., & Goldberg, K. (2021) · Robotics: Science and Systems XVII · doi:10.15607/rss.2021.xvii.013 · Cited by 26 · see citing works
Self-citationRyu, H., Kim, J., An, H., Chang, J., Seo, J., Kim, T.-H., Kim, Y., Hwang, C., Choi, J., & Horowitz, R. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01705 · Cited by 26 · see citing works
Learning to Singulate Layers of Cloth using Tactile Feedback
Tirumala, S., Weng, T., Seita, D., Kroemer, O., Temel, Z., & Held, D. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981341 · Cited by 26 · see citing works
Self-citationOne Policy to Dress Them All: Learning to Dress People with Diverse Poses and Garments
Wang, Y., Sun, Z., Erickson, Z., & Held, D. (2023) · Robotics: Science and Systems · doi:10.15607/rss.2023.xix.008 · Cited by 27 · see citing works
Daniel, M., Magassouba, A., Aranda, M., Lequièvre, L., Ramón, J. A. C., Iglesias, R., & Mezouar, Y. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3342672 · Cited by 21 · see citing works
Efficiently Learning Single-Arm Fling Motions to Smooth Garments
Chen, L. Y., Huang, H., Novoseller, E., Seita, D., Ichnowski, J., Laskey, M., Cheng, R., Kollar, T., & Goldberg, K. Y. (2023) · Springer proceedings in advanced robotics · doi:10.1007/978-3-031-25555-7_4 · Cited by 21 · see citing works
Self-citationAutonomously Untangling Long Cables
Viswanath, V., Shivakumar, K., Kerr, J. H., Thananjeyan, B., Novoseller, E., Ichnowski, J., Escontrela, A., Laskey, M., Gonzalez, J. E., & Goldberg, K. Y. (2022) · Robotics: Science and Systems · doi:10.15607/rss.2022.xviii.034 · Cited by 20 · see citing works
Self-citationWu, R., Lu, H., Wang, Y., Wang, Y., & Dong, H. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01546 · Cited by 20 · see citing works
Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 19 · see citing works
“Some methods [34,226,228,229] train MFRL agents with HER [182], a self-supervised technique to train agents in a sparse reward environment and generalise across the task family, to improve the data efficiency.”
BackgroundMethodInfluentialDisentangling Dense Multi-Cable Knots
Viswanath, V., Grannen, J., Sundaresan, P., Thananjeyan, B., Balakrishna, A., Novoseller, E., Ichnowski, J., Laskey, M., Gonzalez, J. E., & Goldberg, K. (2021) · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636397 · Cited by 21 · see citing works
Self-citationEDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics
Longhini, A., Moletta, M., Reichlin, A., Welle, M. C., Held, D., Erickson, Z., & Kragić, D. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161234 · Cited by 19 · see citing works
Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangement
Gkanatsios, N., Jain, A., Zhou, X., Zhang, Y., Atkeson, C. G., & Fragkiadaki, K. (2023) · Robotics: Science and Systems · doi:10.15607/rss.2023.xix.030 · Cited by 19 · see citing works
Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects
Weng, Z., Paus, F., Varava, A., Yin, H., Asfour, T., & Kragic, D. (2021) · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636660 · Cited by 17 · see citing works
Iterative residual policy: For goal-conditioned dynamic manipulation of deformable objects
Chi, C., Burchfiel, B., Cousineau, E. A., Feng, S., & Song, S. (2023) · The International Journal of Robotics Research · doi:10.1177/02783649231201201 · Cited by 17 · see citing works
ClothesNet: An Information-Rich 3D Garment Model Repository with Simulated Clothes Environment
Zhou, B., Zhou, H., Liang, T., Yu, Q., Zhao, S., Zeng, Y., Lv, J., Luo, S., Wang, Q., Yu, X., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01868 · Cited by 17 · see citing works
Learning Language-Conditioned Deformable Object Manipulation with Graph Dynamics
Deng, Y., Mo, K., Xia, C., & Wang, X. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610890 · Cited by 17 · see citing works
ReorientDiff: Diffusion Model based Reorientation for Object Manipulation
Mishra, U. A., & Chen, Y. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610749 · Cited by 17 · see citing works
Focused Adaptation of Dynamics Models for Deformable Object Manipulation
Mitrano, P. P., LaGrassa, A., Kroemer, O., & Berenson, D. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161366 · Cited by 16 · see citing works
Learning-Based Fabric Folding and Box Wrapping
Wang, X., Zhao, J., Jiang, X., & Liu, Y. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3158434 · Cited by 16 · see citing works
Deep Reinforcement Learning Based on Local GNN for Goal-Conditioned Deformable Object Rearranging
Deng, Y., Xia, C., Wang, X., & Chen, L. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981669 · Cited by 15 · see citing works
Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation
Wu, R., Ning, C., & Dong, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01005 · Cited by 15 · see citing works
Bag All You Need: Learning a Generalizable Bagging Strategy for Heterogeneous Objects
Bahety, A., Jain, S., Ha, H., Hager, N., Burchfiel, B., Cousineau, E., Feng, S., & Song, S. (2023) · 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341841 · Cited by 15 · see citing works
Multidimensional Deformable Object Manipulation Based on DN-Transporter Networks
Teng, Y., Lu, H., Li, Y., Kamiya, T., Nakatoh, Y., Serikawa, S., & Gao, P. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3168303 · Cited by 15 · see citing works
Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks
Wu, H., Ye, J., Meng, X., Paxton, C., & Chirikjian, G. S. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981832 · Cited by 14 · see citing works
Ma, W., Zhang, B., Han, L., Huo, S., Wang, H., & Navarro-Alarcón, D. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3223990 · Cited by 15 · see citing works
Learning to Unfold Garment Effectively Into Oriented Direction
Gu, N., He, R., & Yu, L. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3341763 · Cited by 15 · see citing works
Zhou, Y., Jin, Y., Lu, P., Jiang, S., Wang, Z., & He, B. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3440047 · Cited by 14 · see citing works
SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception
Shivakumar, K., Viswanath, V., Gu, A., Avigal, Y., Kerr, J. H., Ichnowski, J., Cheng, R., Kollar, T., & Goldberg, K. Y. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160574 · Cited by 13 · see citing works
Self-citationZhaole, S., Zhu, J., & Fisher, R. B. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610754 · Cited by 13 · see citing works
Planar Robot Casting with Real2Sim2Real Self-Supervised LearningPreprint
Lim, V., Huang, H., Chen, L. Y., Wang, J., Ichnowski, J., Seita, D., Laskey, M., & Goldberg, K. Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.04814 · Cited by 13 · see citing works
Self-citationIOSG: Image-Driven Object Searching and Grasping
Yu, H., Lou, X., Yang, Y., & Choi, C. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342009 · Cited by 12 · see citing works
Multi-Task Learning with Sequence-Conditioned Transporter Networks
Lim, M. H., Zeng, A., Ichter, B., Bandari, M., Coumans, E., Tomlin, C. J., Schaal, S., & Faust, A. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812096 · Cited by 12 · see citing works
Self-citationHiruma, H., Ito, H., Hiroki, M. O. R. I., & Ogata, T. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3187614 · Cited by 12 · see citing works
DeformNet: Latent Space Modeling and Dynamics Prediction for Deformable Object Manipulation
Li, C., Ai, Z., Wu, T., Li, X., Ding, W., & Xu, H. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611243 · Cited by 11 · see citing works
Towards Living Machines: current and future trends of tactile sensing, grasping, and social robotics
Vouloutsi, V., Cominelli, L., Doğar, M. R., Lepora, N. F., Zito, C., & Martínez-Hernández, U. (2023) · Bioinspiration & Biomimetics · doi:10.1088/1748-3190/acb7b9 · Cited by 10 · see citing works
Learning to Shape by Grinding: Cutting-Surface-Aware Model-Based Reinforcement Learning
Hachimine, T., Morimoto, J., & Matsubara, T. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3303721 · Cited by 10 · see citing works
General-Purpose Clothes Manipulation with Semantic Keypoints
Deng, Y., & Hsu, D. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128573 · Cited by 11 · see citing works
Learning visual-based deformable object rearrangement with local graph neural networks
Deng, Y., Wang, X., & Chen, L. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01048-w · Cited by 9 · see citing works
Autonomous Manipulation Learning for Similar Deformable Objects via Only One Demonstration
Ren, Y., Chen, R., & Cong, Y. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01637 · Cited by 9 · see citing works
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning
Niu, Y., Jin, S., Zhang, Z., Zhu, J., Zhao, D. X., & Zhang, L. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342221 · Cited by 9 · see citing works
TRTM: Template-based Reconstruction and Target-oriented Manipulation of Crumpled Cloths
Wang, W., Li, G., Zamora, M., & Coros, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10609868 · Cited by 7 · see citing works
A Collision-Aware Cable Grasping Method in Cluttered Environment
Zhang, L., Bai, K., Li, Q., Chen, Z., & Zhang, J. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610559 · Cited by 7 · see citing works
PLASTR: Planning for Autonomous Sampling-Based Trowelling
Kuhlmann-Jørgensen, M. A., Pankert, J., Pietrasik, L. L., & Hutter, M. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3291894 · Cited by 7 · see citing works
Leveraging symmetries in pick and place
Huang, H., Wang, D., Tangri, A., Walters, R. G., & Platt, R. (2024) · The International Journal of Robotics Research · doi:10.1177/02783649231225775 · Cited by 7 · see citing works
Interactive Perception for Deformable Object Manipulation
Weng, Z., Zhou, P., Yin, H., Kravberg, A., Varava, A., Navarro-Alarcon, D., & Kragic, D. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3431943 · Cited by 7 · see citing works
“Environment Setup: Our simulation environment is built on the basis of Pybullet [40] which has been previously extended to handle various cloth-like deformable object manipulation tasks [33], [41] under static viewpoint camera.”
MethodBagging by Learning to Singulate Layers Using Interactive Perception
Chen, L. Y., Shi, B., Lin, R., Seita, D., Ahmad, A., Cheng, R., Kollar, T., Held, D., & Goldberg, K. Y. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341634 · Cited by 6 · see citing works
Self-citationZhang, Y., Chen, D., He, W., Petrilli-Barceló, A. E., Luces, J. V. S., & Hirata, Y. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3527003 · Cited by 6 · see citing works
Deng, Y., Xia, C., Wang, X., & Chen, L. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945180 · Cited by 6 · see citing works
Akbulut, B., Girgin, T., Mehrabi, A., Asada, M., Uğur, E., & Öztop, E. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160895 · Cited by 6 · see citing works
Deformable Object Manipulation in Caregiving Scenarios: A Review
Wang, L., & Zhu, J. (2023) · Machines · doi:10.3390/machines11111013 · Cited by 5 · see citing works
Dual-arm shaping of soft objects in 3D based on visual servoing and online FEM simulations
Saghour, C., Navarro-Alarcón, D., Fraisse, P., & Cherubini, A. (2024) · The International Journal of Robotics Research · doi:10.1177/02783649241301076 · Cited by 5 · see citing works
A Survey on Deep Generative Models for Robot Learning From Multimodal Demonstrations
Urain, J., Mandlekar, A., Du, Y., Shafiullah, N. M. “., Xu, D., Fragkiadaki, K., Chalvatzaki, G., & Peters, J. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3631816 · Cited by 13 · see citing works
Robotic perception and manipulation of deformable linear objects: A survey
Caporali, A., Cuiral-Zueco, I., Nicolás, G. L., & Palli, G. (2026) · The International Journal of Robotics Research · doi:10.1177/02783649261432253 · Cited by 5 · see citing works
Performing Efficient and Safe Deformable Package Transport Operations Using Suction Cups
Shukla, R., Yu, Z., Moode, S., Manyar, O. M., Wang, F., Mayya, S., & Gupta, S. K. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802323 · Cited by 5 · see citing works
Train What You Know – Precise Pick-and-Place with Transporter Networks
Sóti, G., Huang, X., Wurll, C., & Hein, B. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161242 · Cited by 5 · see citing works
Object-Centric Inference for Language Conditioned Placement: A Foundation Model based Approach
Xu, Z., Xu, K., Xiong, R., & Wang, Y. (2023) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm58088.2023.10218865 · Cited by 6 · see citing works
Routing Manipulation of Deformable Linear Object Using Reinforcement Learning and Diffusion Policy
Li, M., Yu, H., & Choi, C. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127451 · Cited by 4 · see citing works
Sivertsvik, M., Sumskiy, K., & Misimi, E. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610033 · Cited by 4 · see citing works
Human-Inspired Control of a Whip: Preparatory Movements Improve Hitting a Target
Edraki, M., Lokesh, R., Krotov, A. D., Ramezani, A., & Sternad, D. (2024) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob60516.2024.10719792 · Cited by 4 · see citing works
Effective cloth folding trajectories in simulation with only two parameters
Gusseme, V.-L. D., & wyffels, F. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.989702 · Cited by 5 · see citing works
GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy
Kuroki, S., Guo, J., Matsushima, T., Okubo, T., Kobayashi, M., Ikeda, Y., Takanami, R., Yoo, P., Matsuo, Y., & Iwasawa, Y. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611378 · Cited by 3 · see citing works
Learning for Deformable Linear Object Insertion Leveraging Flexibility Estimation from Visual Cues
Li, M., & Choi, C. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610419 · Cited by 3 · see citing works
Kamaras, G., & Ramamoorthy, S. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3581744 · Cited by 3 · see citing works
Hiruma, H., Mori, H., Ito, H., & Ogata, T. (2022) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon49645.2022.9969015 · Cited by 2 · see citing works
Benchmarking and Simulating Bimanual Robot Shoe Lacing
Luo, H., & Demiris, Y. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3440101 · Cited by 2 · see citing works
Kao, F.-C., Chen, Z.-R., Shih, C.-S., Lu, S.-H., & Lin, P. (2024) · IEEE International Conference on Advanced Intelligent Mechatronics (AIM) · doi:10.1109/aim55361.2024.10637182 · Cited by 2 · see citing works
Automating Deformable Gasket Assembly
Adebola, S., Sadjadpour, T., El-Refai, K., Panitch, W., Ma, Z., Lin, R., Qiu, T., Ganti, S., Le, C., Drake, J., et al. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711836 · Cited by 2 · see citing works
Self-citationHierarchical Visual Policy Learning for Long-Horizon Robot Manipulation in Densely Cluttered Scenes
Wang, H., Qi, L., Wang, Z., Ren, J., Li, W., & Sun, Y. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128752 · Cited by 3 · see citing works
Learning high-level robotic manipulation actions with visual predictive model
Ma, A., Chi, G., Ivaldi, S., & Chen, L. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01174-5 · Cited by 2 · see citing works
A Modular End Effector with Active Rolling Fingertip for Picking Cloth-Like Objects
Zhang, K., Yang, Y., Chen, Z., Chen, H., Wang, M. Y., & Zhang, W. (2023) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case56687.2023.10260468 · Cited by 2 · see citing works
Tool as Embodiment for Recursive ManipulationPreprint
Noguchi, Y., Matsushima, T., Matsuo, Y., & Gu, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.00359 · Cited by 3 · see citing works
PlaNet-ClothPick: Effective Fabric Flattening Based on Latent Dynamic Planning
Kadi, H. A., & Terzić, K. (2024) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii58957.2024.10417545 · Cited by 2 · see citing works
Precise Pick-and-Place using Score-Based Diffusion Networks
Guo, S.-W., Hsiao, T.-C., Liu, Y.-L., & Lee, C. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801772 · Cited by 1 · see citing works
Task-Oriented Language Grounding for Robot via Learning Object Mask
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Wang, M., Wang, J., Zhang, J., Liao, X., & Li, G. (2025) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-025-01299-3 · Cited by 1 · see citing works
Robot Trajectory Optimization for Safe Transport of Deformable Packages
Shukla, R., Moode, S., Wang, F., Mayya, S., & Gupta, S. K. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11164007 · Cited by 1 · see citing works
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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