Survey of Simulators for Deformable Objects in Robotics
DOI:
https://doi.org/10.17979/ja-cea.2025.46.12171Palabras clave:
Robótica, Modelado, Simulación, Control de estructuras flexibles, Materiales flexibles e inteligentesResumen
La simulación de objetos deformables sigue siendo un gran desafío en la robótica actual, yendo desde la necesidad de emular partes del cuerpo humano en cirugías autónomas, hasta el modelado de tareas de manipulación industrial. Esta breve revisión analiza los entornos de desarrollo actuales más usados para la simulación de deformaciones, mencionando y evaluando sus características más relevantes. En concreto se presentan y analizan ocho sistemas distintos, entre los que se encuentran NVIDIA Isaac Sim, MuJoCo y SOFA. Por otro lado, se describen en detalle tres librerías del entorno Unity mediante las cuales se pueden modelar objetos deformables. Esta guía ayudará a seleccionar las herramientas más adecuadas para sus respectivos proyectos.
Referencias
Afzal, A., Katz, D.S., Goues, C.L., Timperley, C.S., 2020. A study on the challenges of using robotics simulators for testing arXiv:2004.07368.
Antonova, R., Shi, P., Yin, H., Weng, Z., Kragic, D., 2021. Dynamic environments with deformable objects, in: Conference on Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track.
Arriola-Rios, V.E., Guler, P., Ficuciello, F., Kragic, D., Siciliano, B., Wyatt, J.L., 2020. Modeling of deformable objects for robotic manipulation: A tutorial and review. Frontiers in Robotics and AI 7, 534750. doi:10.3389/FROBT.2020.00082/XML/NLM.
Blanco-Mulero, D., Barbany, O., Alcan, G., Colome, A., Torras, C., Kyrki, V., 2024. Benchmarking the sim-to-real gap in cloth manipulation. IEEE RA-L 9, 2981–2988. doi:10.1109/LRA.2024.3360814.
Camargo, C., Gonc¸alves, J., Conde, M., Rodr´ıguez-Sedano, F.J., Costa, P., Garc´ıa-Pe˜nalvo, F.J., 2021. Systematic literature review of realistic simulators applied in educational robotics context. Sensors 21, 4031. doi:10.3390/S21124031.
Choi, H.S., et al., 2021. On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward. Proc. Natl. Acad. Sci. U.S.A. 118, e1907856118. doi:10.1073/pnas.1907856118.
Chowdhury, G., 2024. Leveraging robotics simulation for safer, more efficient deployments. URL: https://www.abiresearch.com/blog/robotics-simulation-overview.
Coenen, S., 2021. Nvidia flex soft bodies graduation work digital arts and entertainment. URL: https://simoncoenen.com/downloads/flex_paper.pdf.
Coevoet, E., et al., 2017. Software toolkit for modeling, simulation and control of soft robots. Advanced Robotics 31, 1208–1224. doi:10.1080/01691864.2017.1395362.
Collins, J., Chand, S., Vanderkop, A., Howard, D., 2021. A review of physics simulators for robotic applications. IEEE Access 9, 51416–51431. doi:10.1109/ACCESS.2021.3068769.
Cook, R.D., 1994. Finite Element Modeling for Stress Analysis. 1st ed., John Wiley & Sons, Inc., USA.
Coumans, E., Bai, Y., 2016. Pybullet, a python module for physics simulation for games, robotics and machine learning. http://pybullet.org.
Coumans, E., Bai, Y., 2025. Bullet collision detection and physics library: Bullet documentation. URL: https://pybullet.org/Bullet/BulletFull/index.html.
Daniel Zakaria, M.H., et al., 2022. Robotic control of the deformation of soft linear objects using deep reinforcement learning, in: IEEE Int. Conf. on Automation Science and Engineering, pp. 1516–1522. doi:10.1109/ CASE49997.2022.9926667.
Dickson, B., 2022. This deep learning technique solves one of the tough challenges of robotics. URL: https://bdtechtalks.com/2022/05/09/diffskill-robotics-deformable-object-manipulation.
Faure, F., et al., 2025. SOFA documentation. URL: https://sofa-framework.github.io/doc/.
Fernández-Fernández, J.A., Lange, R., Laible, S., Arras, K.O., Bender, J., 2024. Stark: A unified framework for strongly coupled simulation of rigid and deformable bodies with frictional contact, in: IEEE Int. Conf. on Robotics and Automation, pp. 16888–16894. doi:10.1109/ICRA57147.2024.10610574.
Harris, A., Conrad, J.M., 2011. Survey of popular robotics simulators, frameworks, and toolkits, in: 2011 Proceedings of IEEE Southeastcon, pp. 243– 249. doi:10.1109/SECON.2011.5752942.
Hu, Y., et al., 2020. DiffTaichi: Differentiable programming for physical simulation. arXiv:1910.00935.
Huang, I., et al., 2022. DefGraspSim: Physics-based simulation of grasp outcomes for 3D deformable objects. IEEE RA-L 7, 6274–6281. doi:10. 1109/LRA.2022.3158725.
Huang, Z., et al., 2021. Plasticinelab: A soft-body manipulation benchmark with differentiable physics. arXiv:2104.03311.
Igarashi, T., Moscovich, T., Hughes, J.F., 2005. As-rigid-as-possible shape manipulation. ACM Transactions on Graphics 24, 1134–1141. doi:10.1145/1073204.1073323/SUPPL_FILE/PPS088.MP4.
iGibson Team, 2025. iGibson documentation. URL: https://stanfordvl.github.io/iGibson/index.html.
Jong, J.H.D., Wormnes, K., Tiso, P., 2014. Simulating rigid-bodies, strings and nets for engineering applications using gaming industry physics simulators. Int. Sym. on Artificial Intell., Robotics and Aut. in Space , 17–19.
Kao, F.C., Chen, Z.R., Shih, C.S., Lu, S.H., Lin, P.C., 2024. Bridging mechanical behavior differences of deformable soft objects in simulation and experiments using a data-driven model, in: IEEE Int. Conf. on Advanced Intelligent Mechatronics, pp. 1297–1302. doi:10.1109/AIM55361.2024.10637182.
Kargar, S.M., Yordanov, B., Harvey, C., Asadipour, A., 2024. Emerging trends in realistic robotic simulations: A comprehensive systematic literature review. IEEE Access 12, 191264–191287. doi:10.1109/ACCESS.2024.3404881.
Li, C., et al., 2021. iGibson 2.0: Object-centric simulation for robot learning of everyday household tasks. Proceedings of Machine Learning Research 164, 455–465.
Li, C., et al., 2024. Behavior-1k: A human-centered, embodied ai benchmark with 1,000 everyday activities and realistic simulation. Proceedings of Machine Learning Research 205, 80–93.
Macklin, M., M¨uller, M., 2013. Position based fluids. ACM Trans. Graph. 32. doi:10.1145/2461912.2461984.
Macklin, M., M¨uller, M., Chentanez, N., Kim, T.Y., 2014. Unified particle physics for real-time applications. ACM Trans. Graph. 33. doi:10.1145/ 2601097.2601152.
Melo, M.S.P.D., et al., 2019. Analysis and comparison of robotics 3D simulators, in: 21st Symposium on Virtual and Augmented Reality (SVR), pp. 242–251. doi:10.1109/SVR.2019.00049.
Mittal, M., et al., 2023. Orbit: A unified simulation framework for interactive robot learning environments. IEEE RA-L 8, 3740–3747. doi:10.1109/LRA.2023.3270034.
Moore, P., Molloy, D., 2007. A survey of computer-based deformable models, in: Int. Machine Vision and Image Processing Conf., pp. 55–66. doi:10.1109/IMVIP.2007.31.
Mower, C.E., et al., 2022. ROS-pybullet interface: A framework for reliable contact simulation and human-robot interaction. Proceedings of Machine Learning Research 205, 1411–1423.
NVIDIA, 2025a. Deformable-body simulation: Omniverse extensions. URL: https://docs.omniverse.nvidia.com/extensions/latest/ext_physics/deformable-bodies.html.
NVIDIA, 2025b. Isaac Sim: Robotics simulation and synthetic data generation. URL: https://developer.nvidia.com/isaac/sim.
ROS2 Contributors, 2025. Programming multiple robots with ROS 2. URL: https://osrf.github.io/ros2multirobotbook/.
Shamshiri, R.R., et al., 2018. Simulation software and virtual environments for acceleration of agricultural robotics: Features highlights and performance comparison. Int J Agric & Biol Eng 11, 15–31. doi:10.25165/J.IJABE.20181104.4032.
Shan, J., et al., 2024. Soft contact simulation and manipulation learning of deformable objects with vision-based tactile sensor. arXiv:2405.07237.
Shen, B., et al., 2021. iGibson 1.0: A simulation environment for interactive tasks in large realistic scenes, in: IEEE/RSJ IROS, pp. 7520–7527. doi:10.1109/IROS51168.2021.9636667.
Siemens, 2018. ROS#. URL: https://assetstore.unity.com/packages/tools/physics/ros-107085#description.
Tagliabue, E., et al., 2020. Soft tissue simulation environment to learn manipulation tasks in autonomous robotic surgery, in: IEEE/RSJ IROS, pp. 3261–3266. doi:10.1109/IROS45743.2020.9341710.
Tedrake, R., et al., 2019. Drake: Model-based design and verification for robotics. URL: https://drake.mit.edu.
Todorov, E., Erez, T., Tassa, Y., 2012. Mujoco: A physics engine for modelbased control, in: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 5026–5033. doi:10.1109/IROS.2012.6386109.
Tola, D., Corke, P., 2024. Understanding URDF: A dataset and analysis. IEEE RA-L 9, 4479–4486. doi:10.1109/LRA.2024.3381482.
Va, H., Choi, M.H., Hong, M., 2023. Efficient simulation of volumetric deformable objects in Unity3D: GPU-accelerated position-based dynamics. Electronics 12, 2229. doi:10.3390/ELECTRONICS12102229.
Web of Science, 2025. URL: https://www.webofscience.com.
Woodall, K., 2019. Deform. URL: https://assetstore.unity.com/packages/tools/modeling/deform-148425.
Woodall, K., 2021. Deform: Documentation. URL: https://github.com/keenanwoodall/Deform/wiki.
Zakka, K., Tassa, Y., Contributors, M.M., 2022. Mujoco menagerie: A collection of high-quality simulation models for mujoco. URL: http://github.com/google-deepmind/mujoco_menagerie.
Zgeb, B., 2021. Unity softbody deformation: An example of mesh deformation in Unity. URL: https://bronsonzgeb.com/index.php/2021/07/10/mesh-deformation-in-unity/.
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2025 Alberto Zafra-Navarro, Rosario Aragüés, Gonzalo López-Nicolás

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.