Soft Body Simulator
A tutorial on simulating nonlinear behaviors of flexible structures with the discrete differential geometry method
Overview: Discrete differential geometry (DDG) has emerged as a promising alternative numerical framework for simulating flexible structures. DDG provides a discrete representation of geometry that directly encodes curvature, torsion, and deformation mechanics, making it particularly suitable for modeling elastic structures. Unlike FEM and FDM, which approximate geometric properties from a continuous formulation, DDG operates on discrete geometric quantities, ensuring consistency in shape representation and numerical stability.
Harnessing Discrete Differential Geometry: A Virtual Playground for the Bilayer Soft Robotics
Overview: In this work, we introduce a novel and efficient simulation framework designed to capture the diverse behaviours of soft robots constructed from bilayer structures. By leveraging the principles of discrete differential geometry, our approach provides deeper insights into: Complex Dynamics: Accurately modeling the multifaceted interactions within soft robotic systems. Simulation Efficiency: Offering a robust tool for researchers to explore and predict the behaviors of innovative soft robotic materials. Design & Control: Enhancing the way we design and control soft robots for various applications.
Inverse Design of Planar Clamped-Free Elastic Rods from Noisy Data
Overview: Here, we focus on an inverse problem: Can we determine the natural shape of a suspended 2D planar rod so that it deforms into a desired target shape under the specified loading? We begin by formulating a theoretical framework based on the statics of planar rod equilibrium that can compute the natural shape of a planar rod given its target shape. Furthermore, we analyze the impact of uncertainties (e.g., noise in the data) on the accuracy of the theoretical framework. The results reveal the shortcomings of the theoretical framework in handling uncertainties in the inverse problem, a fact often overlooked in previous works. To mitigate the influence of the uncertainties, we combine the statics of the planar rod with the adjoint method for parameter sensitivity analysis, constructing a learning framework that can efficiently explore the natural shape of the designed rod with enhanced robustness. It offers valuable insights into the inverse design of soft structures for various applications, including soft robotics and animation of morphing structures.
Link: Github
Inverse Design of Snap-Actuated Jumping Robots Powered by Mechanics-Aided Machine Learning
Overview: Here, we propose a physics-data hybrid inverse design strategy to endow the snap-jump robot with diverse jumping capabilities. We first develop a discrete numerical framework for the dynamic analysis of snap-actuated jumping robots and then use extensive simulation data to establish a data-driven inverse design solution. This approach allows rapid exploration of parameter spaces to achieve targeted jump trajectories, providing a robust foundation for the robot’s fabrication. Our methodology offers a powerful framework for advancing the design and control of soft robots through integrated simulation and data-driven techniques.
References
- Tong, D., Hao, Z., Li, J. and Huang, W. 2025. Inverse Design of Planar Clamped-Free Elastic Rods from Noisy Data. The International Journal for Numerical Methods in Engineering. (Article link)
- Li, J., Tong, D., Hao, Z., Zhu, Y., Wu, H., Liu, M. and Huang, W., 2024. Harnessing Discrete Differential Geometry: A Virtual Playground for the Bilayer Soft Robotics. arXiv preprint arXiv:2502.00714. (Article link)
- Tong, D., Hao, Z., Liu, M. and Huang, W., 2024. Inverse Design of Snap-Actuated Jumping Robots Powered by Mechanics-Aided Machine Learning. IEEE Robotics and Automation Letters. (Article link)