Goal-Conditioned Model Simplification for 1-D and 2-D Deformable Object Manipulation
Open accessAbstract
Motion planning for deformable object manipulation has been a challenge for a long time in robotics due to its high computational cost. In this work, we propose to mitigate this cost by limiting the number of picking points on a deformable object within the action space and simplifying the dynamics model. We do this first by identifying a minimal geometric model that closely approximates the original model at the goal state; specifically, we implement this general approach for 1-D linear deformable objects (e.g., ropes) using a piece-wise line-fitted model, and for 2-D surface deformable objects (e.g., cloth) using a mesh-simplified model. Then a small number of key particles are extracted as the pickable points in the action space which are sufficient to represent and reach the given goal. Additionally, a simplified dynamics model is constructed based on the simplified geometric model, containing much fewer particles and thus being much faster to simulate than the original dynamics model, albeit with some loss of precision. We further refine this model iteratively by adding more details from the actually achieved final state of the original model until a satisfactory trajectory is generated. Extensive simulation experiments are conducted on a set of representative tasks for ropes and cloth, which show a significant decrease in time cost while achieving similar or better trajectory costs. Finally, we establish a closed-loop system of perception, planning, and control with a real robot for cloth folding, which validates the effectiveness of our proposed method.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
Surface simplification using quadric error metrics
Garland, M., & Heckbert, P. S. (1997) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/258734.258849 · Cited by 3,445 · see citing works
A Tutorial on the Cross-Entropy Method
Boer, P., Kroese, D. P., Mannor, S., & Rubinstein, R. (2005) · Annals of Operations Research · doi:10.1007/s10479-005-5724-z · Cited by 3,348 · cited 1× · see citing works
“And as our method is independent of the planners used, we implement two widely used algorithms, Cross-Entropy Method (CEM [57]) and Rapidly-exploring Random Tree (RRT [58]), of which the former is optimization-based and the latter is search-based.”
MeshLab: an Open-Source Mesh Processing Tool
Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., & Ranzuglia, G. (2008) · ISTI Open Portal · doi:10.2312/localchapterevents/italchap/italianchapconf2008/129-136 · Cited by 2,445 · cited 1× · see citing works
“[53].”
The Open Motion Planning Library
Sucan, I. A., Moll, M., & Kavraki, L. E. (2012) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2012.2205651 · Cited by 1,616 · cited 1× · see citing works
“We implement RRT from Open Motion Planning Library [59] (OMPL), and set the goal bias and error threshold to 0.5 and 1 e − 6 respectively.”
InfluentialMESH: measuring errors between surfaces using the Hausdorff distance
Aspert, N., Santa-Cruz, D., & Ebrahimi, T. (2003) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2002.1035879 · Cited by 721 · cited 1× · see citing works
“To implement the Error function and find the distance between the simplified mesh and the original one (Algorithm 2, Line 4), we use the Hausdorff distance [54], which is a widely used similarity metric for mesh and image comparison.”
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 · cited 2× · see citing works
“For example, controlling the shape of a rope by manipulating only its two ends [16], folding or unfolding cloth by focusing on its visible corners [17], [18], [19], or removing wrinkles from fabric [20].”
Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions
Noreen, I., Khan, A., & Habib, Z. (2016) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2016.071114 · Cited by 329 · cited 1× · see citing works
“And as our method is independent of the planners used, we implement two widely used algorithms, Cross-Entropy Method (CEM [57]) and Rapidly-exploring Random Tree (RRT [58]), of which the former is optimization-based and the latter is search-based.”
Modeling, learning, perception, and control methods for deformable object manipulation
Yin, H., Varava, A., & Kragic, D. (2021) · Science Robotics · doi:10.1126/scirobotics.abd8803 · Cited by 299 · see citing works
Challenges and Outlook in Robotic Manipulation of Deformable Objects
Zhu, J., Cherubini, A., Dune, C., Navarro-Alarcón, D., Alambeigi, F., Berenson, D., Ficuciello, F., Harada, K., Kober, J., Li, X., et al. (2022) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2022.3147415 · Cited by 276 · cited 3× · see citing works
“14 and 15.”
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.15818 · Cited by 276 · cited 1× · see citing works
“DOM tasks have mostly been studied using model-free learning-based approaches in the literature, including reinforcement learning [9], [10], imitation learning [11], and large vision-language-model-based systems [12].”
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 · see citing works
A geometric approach to robotic laundry folding
Miller, S., van den Berg, J., Fritz, M., Darrell, T., Goldberg, K., & Abbeel, P. (2011) · The International Journal of Robotics Research · doi:10.1177/0278364911430417 · Cited by 238 · see citing works
Balkcom, D., & Mason, M. T. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908090235 · Cited by 192 · see citing works
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation
Lin, X., Wang, Y., Olkin, J., & Held, D. (2020) · arXiv · doi:10.48550/arxiv.2011.07215 · Cited by 140 · see citing works
Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges
Bhagat, S., Banerjee, H., Tse, Z. T. H., & Ren, H. (2019) · Robotics · doi:10.3390/robotics8010004 · Cited by 134 · see citing works
Survey on model-based manipulation planning of deformable objects
Jiménez, P. (2011) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2011.08.002 · Cited by 129 · see citing works
Manipulate-Anything: Automating Real-World Robots using Vision-Language ModelsPreprint
Duan, J., Yuan, W., Pumacay, W., et al. (2024) · Conference on Robot Learning · doi:10.48550/arxiv.2406.18915 · Cited by 127 · cited 1× · see citing works
“Recent advances in end-to-end vision-language-based systemshaveshownpromiseinroboticobjectmanipulation[50], [51].”
Modeling of deformable objects for robotic manipulation: A tutorial and review
Arriola-Rios, V. E., Guler, P., Ficuciello, F., Kragic, D., Siciliano, B., & Wyatt, J. L. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00082 · Cited by 126 · cited 2× · see citing works
“Typically, a dynamics model is first built in simulation, often utilizing mass-spring systems [13] for their effectiveness.”
Doumanoglou, 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 · cited 1× · see citing works
“These challenges make both planning and perception complex problems, particularly for traditional optimization or search based methods [24], [25], [26].”
Folding clothes autonomously: A complete pipeline
Doumanoglou, A., Stria, J., Peleka, G., Mariolis, I., Petrik, V., Kargakos, A., Wagner, L., Hlavac, V., Kim, T.-K., & Malassiotis, S. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2602376 · Cited by 122 · cited 1× · see citing works
“For example, controlling the shape of a rope by manipulating only its two ends [16], folding or unfolding cloth by focusing on its visible corners [17], [18], [19], or removing wrinkles from fabric [20].”
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 · see citing works
A Grasping-Centered Analysis for Cloth Manipulation
Borràs, J., Alenyà, G., & Carme Torras, A. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.2986921 · Cited by 106 · see citing works
Lv, N., Liu, J., & Jia, Y. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3139838 · Cited by 105 · see citing works
Folding deformable objects using predictive simulation and trajectory optimization
Li, Y., Yue, Y., Xu, D., Grinspun, E., & Allen, P. K. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7354231 · Cited by 103 · cited 1× · see citing works
“These challenges make both planning and perception complex problems, particularly for traditional optimization or search based methods [24], [25], [26].”
TinyVLA: Toward Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation
Wen, J., Zhu, Y., Li, J., Zhu, M., Tang, Z., Wu, K., Xu, Z., Liu, N., Cheng, R., Shen, C., et al. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3544909 · Cited by 97 · cited 1× · see citing works
“Recent advances in end-to-end vision-language-based systemshaveshownpromiseinroboticobjectmanipulation[50], [51].”
SpeedFolding: Learning Efficient Bimanual Folding of Garments
Avigal, Y., Berscheid, L., Asfour, T., Kröger, T., & Goldberg, K. (2022) · 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981402 · Cited by 95 · see citing works
FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding
Ha, H., & Song, S. (2021) · arXiv · doi:10.48550/arxiv.2105.03655 · Cited by 93 · see citing works
Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making
Seita, D., Jamali, N., Laskey, M., Tanwani, A. K., Berenstein, R., Baskaran, P., Iba, S., Canny, J., & Goldberg, K. Y. (2022) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-95459-8_17 · Cited by 88 · cited 1× · see citing works
“…interest from robotic researchers in recent years [1], [2], [3], [4] due to the ubiquitous existence of de-formable objects in our daily life and their extensive applications in both domestic and industrial scenarios [5], [6], [7], [8], such as folding laundry or handling surgical materials.”
Chitta, S. (2016) · Studies in computational intelligence · doi:10.1007/978-3-319-26054-9_1 · Cited by 72 · see citing works
Learning Visible Connectivity Dynamics for Cloth Smoothing
Lin, X., Wang, Y., Huang, Z., & Held, D. (2021) · arXiv · doi:10.48550/arxiv.2105.10389 · Cited by 62 · see citing works
Manipulating deformable objects by interleaving prediction, planning, and control
McConachie, D., Dobson, A., Ruan, M., & Berenson, D. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920918299 · Cited by 61 · see citing works
Occlusion-robust Deformable Object Tracking without Physics Simulation
Chi, C., & Berenson, D. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967827 · Cited by 55 · cited 1× · see citing works
“Specifically, we use CDCPD [60], [61] as the shape tracking method, but modify it in two ways to improve its performance for our tasks: 1) we explicitly reason about self-occlusions by comparing the distance between the current and previous point cloud, and combine them accordingly, the process of…”
LaSeSOM: A Latent and Semantic Representation Framework for Soft Object Manipulation
Zhou, P., Zhu, J., Huo, S., et al. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3074872 · Cited by 51 · see citing works
Vision-guided robotic fabric manipulation for apparel manufacturing
Torgerson, E., & Paul, F. W. (1988) · IEEE Control Systems Magazine · doi:10.1109/37.463 · Cited by 49 · see citing works
Tracking Partially-Occluded Deformable Objects while Enforcing Geometric Constraints
Wang, Y., McConachie, D., & Berenson, D. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561012 · Cited by 46 · cited 1× · see citing works
“Specifically, we use CDCPD [60], [61] as the shape tracking method, but modify it in two ways to improve its performance for our tasks: 1) we explicitly reason about self-occlusions by comparing the distance between the current and previous point cloud, and combine them accordingly, the process of…”
Dexterous Manipulation of Cloth
Bai, Y., Yu, W., & Liu, C. K. (2016) · Computer graphics forum (Print) · doi:10.1111/cgf.12852 · Cited by 45 · cited 1× · see citing works
“Simulating such models is computationally expensive, and motion planners must often evaluate thousands of trajectories, potentially taking hours to plan a single trajectory [14].”
Visuospatial foresight for physical sequential fabric manipulation
Hoque, R., Seita, D., Balakrishna, A., Ganapathi, A., Tanwani, A. K., Jamali, N., Yamane, K., Iba, S., & Goldberg, K. (2022) · Autonomous Robots · doi:10.1007/s10514-021-10001-0 · Cited by 45 · cited 2× · see citing works
“For instance, Hoque et al. [42] demonstrated a visual dynamics model trained on domain-randomized RGBD images for fabric folding tasks, while Li et al. [36] used a recurrent state-space model for latent dynamics modeling.”
Liu, F., Su, E., Lu, J., et al. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3264766 · Cited by 45 · see citing works
A Heuristic-Based Approach for Flattening Wrinkled Clothes
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 · see citing works
Vision-Based Robotic Manipulation of Flexible PCBs
Li, X., Su, X., & Liu, Y.-H. (2018) · IEEE/ASME transactions on mechatronics · doi:10.1109/tmech.2018.2869147 · Cited by 40 · see citing works
Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation
Huang, Z., Lin, X., & Held, D. (2022) · Robotics: Science and Systems XVIII · doi:10.15607/rss.2022.xviii.011 · Cited by 39 · see citing works
Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objects
Ma, X., Hsu, D., & Lee, W. S. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9811597 · Cited by 39 · cited 3× · see citing works
“Other approaches involve using graph neural networks to learn deformable dynamics based on key points or mesh representations [35], [43], [44], [45].”
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
Learning Deformable Object Manipulation From Expert Demonstrations
Salhotra, G., Liu, I.-C. A., Dominguez-Kuhne, M., & Sukhatme, G. S. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3187843 · Cited by 37 · cited 1× · see citing works
“DOM tasks have mostly been studied using model-free learning-based approaches in the literature, including reinforcement learning [9], [10], imitation learning [11], and large vision-language-model-based systems [12].”
Soft Robot Optimal Control Via Reduced Order Finite Element Models
Tonkens, S., Lorenzetti, J., & Pavone, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560999 · Cited by 35 · see citing works
Optimal Reduced-order Modeling of Bipedal Locomotion
Chen, Y.-M., & Posa, M. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197004 · Cited by 32 · cited 1× · see citing works
“Interests in model reduction extend to related areas in robotics, such as the framework proposed by Chen et al. [48], which aims to optimize reduced-order models and trajectories simultaneously for bipedal locomotion, and the application of model order reduction techniques to solve optimal control…”
Ruan, M., McConachie, D., & Berenson, D. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594520 · Cited by 26 · cited 3× · see citing works
“As a result, various simplified models have been proposed for specific tasks [21], [38], [39], [40].”
Bandit-Based Model Selection for Deformable Object Manipulation
McConachie, D., & Berenson, D. (2020) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-43089-4_45 · Cited by 25 · cited 2× · see citing works
“As a result, various simplified models have been proposed for specific tasks [21], [38], [39], [40].”
Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces
McConachie, D., Power, T., Mitrano, P., et al. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2972858 · Cited by 23 · cited 2× · see citing works
“McConachie et al. [38] employed a virtual elastic band model to approximate true dynamics and trained a classifier to decide when to trust the simplified model for rope manipulation in clutter.”
Learning Keypoints for Robotic Cloth Manipulation Using Synthetic Data
Lips, T., Gusseme, V.-L. D., & wyffels, F. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3405335 · Cited by 20 · 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
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 · cited 1× · see citing works
“Mitrano et al. [46] proposed adapting a learned dynamics model to novel domains by focusing on the regions where source and dynamics are similar.”
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
Bimanual Deformable Bag Manipulation Using a Structure-of-Interest Based Neural Dynamics Model
Zhou, P., Zheng, P., Qi, J., Li, C., Lee, H.-Y., Pan, Y., Yang, C., Navarro-Alarcón, D., & Pan, J. (2024) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2024.3485471 · Cited by 14 · cited 1× · see citing works
“Zhou et al. [41] focused on identifying, modeling, and manipulating structures of interest rather than the entire object for bimanual bag manipulation, which substantially reduces computational load compared to full dynamics modeling.”
Self-supervised Cloth Reconstruction via Action-conditioned Cloth Tracking
Huang, Z., Lin, X., & Held, D. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160653 · Cited by 13 · cited 1× · see citing works
“D EFORMABLE object manipulation (DOM) has received significant interest from robotic researchers in recent years [1], [2], [3], [4] due to the ubiquitous existence of de-formable objects in our daily life and their extensive applications in both domestic and industrial scenarios [5], [6], [7], [8],…”
Keep It Simple: Data-Efficient Learning for Controlling Complex Systems With Simple Models
Power, T., & Berenson, D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3056368 · Cited by 11 · cited 2× · see citing works
“As a result, various simplified models have been proposed for specific tasks [21], [38], [39], [40].”
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 · cited 4× · see citing works
“Recently,learning-basedmethodsfordeformableobjectmanipulationhavebeen widelyused[31],[32],[33],[34],[35],[36].”
InfluentialRobotic Fabric Flattening with Wrinkle Direction Detection
Qiu, Y., Zhu, J., Santina, C. D., Gienger, M., & Kober, J. (2024) · Springer proceedings in advanced robotics · doi:10.1007/978-3-031-63596-0_30 · Cited by 7 · see citing works
One Fling to Goal: Environment-aware Dynamics for Goal-conditioned Fabric Flinging
Yang, L.-H., Yang, L., Sun, H.-R., et al. (2024) · arXiv.org · doi:10.48550/arxiv.2406.14136 · Cited by 7
Arnold, S., Tanaka, D., & Yamazaki, K. (2023) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.1045747 · Cited by 6 · cited 2× · see citing works
“Recently,learning-basedmethodsfordeformableobjectmanipulationhavebeen widelyused[31],[32],[33],[34],[35],[36].”
Goal-Conditioned Action Space Reduction for Deformable Object Manipulation
Wang, S., Papallas, R., Leonetti, M., & Doğar, M. R. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161541 · Cited by 6 · see citing works
Self-citationEfficient Cloth Simulation using Miniature Cloth and Upscaling Deep Neural NetworksPreprint
Lee, T. M., Oh, Y. J., & Lee, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03953 · Cited by 3 · see citing works
One Fling to Goal: Environment-Aware Dynamics for Goal-Conditioned Fabric Flinging
Yang, L., Yang, L., Sun, H., et al. (2026) · Springer proceedings in advanced robotics · doi:10.1007/978-3-032-09970-9_4 · Cited by 1 · see citing works
Flagged first, then by citations
7 of the 70 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Surface Flattening Approach for Forming Variable‐Curvature CFRP Stringers
Xiao, W., Hui, Y., Wang, H., et al. (2025) · Polymer Composites · doi:10.1002/pc.70538 · Cited by 1 · see citing works
“…one of the most widely used in the field of surface flattening due to its simplicity, high computational efficiency [28] and ability to dynamically simulate material deformation [29] and has been extensively validated in applications such as sheet metal flattening and garment design [30, 31].”
Robust Manipulation of Deformable Linear Objects
Envall, J., & Coros, S. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3692099
“In a recent work, Wang et al. presented an approach to simplify the geometry of the manipulated object [13], which efficiently reduces the search space while maintaining high fidelity.”
Chen, D., Tang, K., Zhang, Y., et al. (2026) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2026.3698472
Chen, J., Chu, H., Chao, Z., et al. (2026) · Frontiers in Robotics and AI · doi:10.3389/frobt.2026.1917009
- Authors
- Wang, S., Leonetti, M., & Dogar, M.
- Affiliations
- University of Leeds (GB)
- King's College London (GB)
- School of Computer Science, University of Leeds, Leeds, U.K.
- Venue
- IEEE Transactions on Robotics
- Published
- 2025
- Volume
- 41
- Pages
- 4023-4040
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W4411055115
- Semantic Scholar
- ea876fbf6d02740848886e189687fc1157538e39
- Cited by
- 4
- References
- 70
- Citations by year
- 2026: 3 · 2025: 1
- FWCI
- 1.60
- Type
- journal article
- Language
- en
- Open access
- green
- kclpure.kcl.ac.uk
- Licence
- Locations
- IEEE Transactions on Robotics (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- deformable object manipulation, motion planning, fabric manipulation, rope manipulation, mesh simplification, cloth folding
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 ›
Are you an author, or do you see a problem with this page? Request a review
