2000 character limit reached
Differentiable Rendering as a Way to Program Cable-Driven Soft Robots
Published 11 Apr 2024 in cs.RO and cs.GR | (2404.07590v1)
Abstract: Soft robots have gained increased popularity in recent years due to their adaptability and compliance. In this paper, we use a digital twin model of cable-driven soft robots to learn control parameters in simulation. In doing so, we take advantage of differentiable rendering as a way to instruct robots to complete tasks such as point reach, gripping an object, and obstacle avoidance. This approach simplifies the mathematical description of such complicated tasks and removes the need for landmark points and their tracking. Our experiments demonstrate the applicability of our method.
- Soft Robots for Ocean Exploration and Offshore Operations: A Perspective. Soft Robotics 8, 6 (2021), 625–639. https://doi.org/10.1089/soro.2020.0011 arXiv:https://doi.org/10.1089/soro.2020.0011 PMID: 33450174.
- Differentiable Depth for Real2Sim Calibration of Soft Body Simulations. Computer Graphics Forum 42, 1 (2023), 277–289. https://doi.org/10.1111/cgf.14720 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.14720
- A 3D-printed, functionally graded soft robot powered by combustion. Science 349, 6244 (2015), 161–165. https://doi.org/10.1126/science.aab0129 arXiv:https://www.science.org/doi/pdf/10.1126/science.aab0129
- Biomedical applications of soft robotics. Nature Reviews Materials 3, 6 (2018), 143–153.
- Software toolkit for modeling, simulation, and control of soft robots. Advanced Robotics 31, 22 (2017), 1208–1224. https://doi.org/10.1080/01691864.2017.1395362 arXiv:https://doi.org/10.1080/01691864.2017.1395362
- Real-time simulation of contact and cutting of heterogeneous soft-tissues. Medical Image Analysis 18, 2 (2014), 394–410. https://doi.org/10.1016/j.media.2013.11.001
- Model-Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges. IEEE Control Systems Magazine 43, 3 (2023), 30–65. https://doi.org/10.1109/MCS.2023.3253419
- Sim-to-Real for Soft Robots Using Differentiable FEM: Recipes for Meshing, Damping, and Actuation. IEEE Robotics and Automation Letters 7, 2 (2022), 5015–5022. https://doi.org/10.1109/LRA.2022.3154050
- Christian Duriez. 2013. Control of elastic soft robots based on real-time finite element method. In 2013 IEEE International Conference on Robotics and Automation. IEEE, Karlsruhe, Germany, 3982–3987. https://doi.org/10.1109/ICRA.2013.6631138
- ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics. In 2019 International Conference on Robotics and Automation (ICRA). IEEE, Montreal Convention Center, Montreal, Canada, 6265–6271. https://doi.org/10.1109/ICRA.2019.8794333
- Accelerating 3D deep learning with PyTorch3D. In SIGGRAPH Asia 2020 Courses (Virtual Event) (SA ’20). Association for Computing Machinery, New York, NY, USA, Article 10, 1 pages. https://doi.org/10.1145/3415263.3419160
- Soft robotics: a bioinspired evolution in robotics. Trends in Biotechnology 31, 5 (2013), 287–294. https://doi.org/10.1016/j.tibtech.2013.03.002
- Real-time control of soft-robots using asynchronous finite element modeling. In 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, Washington State Convention Center, Seattle, WA, USA, 2550–2555. https://doi.org/10.1109/ICRA.2015.7139541
- Hod Lipson. 2014. Challenges and Opportunities for Design, Simulation, and Fabrication of Soft Robots. Soft Robotics 1, 1 (2014), 21–27. https://doi.org/10.1089/soro.2013.0007 arXiv:https://doi.org/10.1089/soro.2013.0007
- DiffAqua: a differentiable computational design pipeline for soft underwater swimmers with shape interpolation. ACM Trans. Graph. 40, 4, Article 132 (jul 2021), 14Â pages. https://doi.org/10.1145/3450626.3459832
- Miles Macklin. 2022. Warp: A High-performance Python Framework for GPU Simulation and Graphics. https://github.com/nvidia/warp. NVIDIA GPU Technology Conference (GTC).
- gradSim: Differentiable simulation for system identification and visuomotor control. In International Conference on Learning Representations. OpenReview, Vienna, Austria. https://openreview.net/forum?id=c_E8kFWfhp0
- Discrete Cosserat Approach for Multisection Soft Manipulator Dynamics. IEEE Transactions on Robotics 34, 6 (2018), 1518–1533. https://doi.org/10.1109/TRO.2018.2868815
- Pierre Schegg and Christian Duriez. 2022. Review on generic methods for mechanical modeling, simulation and control of soft robots. Plos one 17, 1 (2022), e0251059. https://doi.org/10.1371/journal.pone.0251059
- Soft Robotic Grippers. Advanced Materials 30, 29 (2018), 1707035. https://doi.org/10.1002/adma.201707035 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.201707035
- Domain Randomization for Robust, Affordable and Effective Closed-Loop Control of Soft Robots. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, Detroit, USA, 612–619. https://doi.org/10.1109/IROS55552.2023.10342537
- Visual servo control of cable-driven soft robotic manipulator. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, Tokyo Big Sight - Tokyo, Japan, 57–62. https://doi.org/10.1109/IROS.2013.6696332
- Review of Modeling and Control Methods of Soft Robots Based on Machine Learning. In 2023 42nd Chinese Control Conference (CCC). IEEE, Tianjin, China, 4318–4323. https://doi.org/10.23919/CCC58697.2023.10240787
- Recent Advances in Design and Actuation of Continuum Robots for Medical Applications. Actuators 9, 4 (2020). https://doi.org/10.3390/act9040142
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.