I am a Master’s student at the CoMMA lab in the Computer Science Department of Purdue University. Though it’s too early to say for sure, my research interests include vision for robotics, physics informed networks, and motion planning, especially in wacky and unusual situations. In the past, I have worked with RL, language-controlled robots, and planning with PDEs. I am also interested in how we can make proof-of-concept methods from literature reliable and explainable enough to deploy to commercial robots.
2025
RoboSoft
Physics-Grounded Differentiable Simulation for Soft Growing Robots
Soft-growing robots (i.e., vine robots) are a promising class of soft robots that allow for navigation and growth in tightly confined environments. However, these robots remain challenging to model and control due to the complex interplay of the inflated structure and inextensible materials, which leads to obstacles for autonomous operation and design optimization. Although there exist simulators for these systems that have achieved qualitative and quantitative success in matching high-level behavior, they still often fail to capture realistic vine robot shapes using simplified parameter models and have difficulties in high-throughput simulation necessary for planning and parameter optimization. We propose a differentiable simulator for these systems, enabling the use of the simulator "in-the-loop" of gradient-based optimization approaches to address the issues listed above. With the more complex parameter fitting made possible by this approach, we experimentally validate and integrate a closed-form nonlinear stiffness model for thin-walled inflated tubes based on a first-principles approach to local material wrinkling. Our simulator also takes advantage of data-parallel operations by leveraging existing differentiable computation frameworks, allowing multiple simultaneous rollouts. We demonstrate the feasibility of using a physics-grounded nonlinear stiffness model within our simulator, and how it can be an effective tool in sim-to-real transfer. We provide our implementation open source.
@inproceedings{chengao2025diffsim,title={Physics-Grounded Differentiable Simulation for Soft Growing Robots},author={Chen, Lucas and Gao, Yitian and Wang, Sicheng and Fuentes, Francesco and Blumenschein, Laura H. and Kingston, Zachary},year={2025},booktitle={IEEE-RAS International Conference on Soft Robotics},eprint={2501.17963},archiveprefix={arXiv},primaryclass={cs.RO},note={To Appear},}
