Soft robots, as the name suggests, are made with soft materials rather than rigid ones. These soft robots will bend, deform, and buckle upon contact rather than simply smashing on through whatever is in their way, resulting in a natural compliance that allows them to interact safely with the environment and around humans. This compliance can also lead to robust behavior; think about how soft grippers have far more success wrapping themselves around objects to securely pick up objects compared to rigid pinch grasps.
Howevever, this compliant behavior is hard to model and thus planning for soft robots is a challenge. If this behavior can be understood and exploited, we can gain the best of both worlds: safe, compliant interaction with robust long-horizon reasoning.
Soft growing robots, commonly referred to as vine robots, have demonstrated remarkable ability to interact safely and robustly with unstructured and dynamic environments. It is therefore natural to exploit contact with the environment for planning and design optimization tasks. Previous research has focused on planning under contact for passively deforming robots with pre-formed bends. However, adding active steering to these soft growing robots is necessary for successful navigation in more complex environments. To this end, we develop a unified modeling framework that integrates vine robot growth, bending, actuation, and obstacle contact. We extend the beam moment model to include the effects of actuation on kinematics under growth and then use these models to develop a fast parallel simulation framework. We validate our model and simulator with real robot experiments. To showcase the capabilities of our framework, we apply our model in a design optimization task to find designs for vine robots navigating through cluttered environments, identifying designs that minimize the number of required actuators by exploiting environmental contacts. We show the robustness of the designs to environmental and manufacturing uncertainties. Finally, we fabricate an optimized design and successfully deploy it in an obstacle-rich environment.
@misc{gaochen2025actsim,title={Parallel Simulation of Contact and Actuation for Soft Growing Robots},author={Gao, Yitian and Chen, Lucas and Bhovad, Priyanka and Wang, Sicheng and Kingston, Zachary and Blumenschein, Laura H.},eprint={2509.15180},archiveprefix={arXiv},primaryclass={cs.RO},year={2025},note={Under Review},}
arXiv
Exteroception through Proprioception Sensing through Improved Contact Modeling for Soft Growing Robots
Passive deformation due to compliance is a commonly used benefit of soft robots, providing opportunities to achieve robust actuation with few active degrees of freedom. Soft growing robots in particular have shown promise in navigation of unstructured environments due to their passive deformation. If their collisions and subsequent deformations can be better understood, soft robots could be used to understand the structure of the environment from direct tactile measurements. In this work, we propose the use of soft growing robots as mapping and exploration tools. We do this by first characterizing collision behavior during discrete turns, then leveraging this model to develop a geometry-based simulator that models robot trajectories in 2D environments. Finally, we demonstrate the model and simulator validity by mapping unknown environments using Monte Carlo sampling to estimate the optimal next deployment given current knowledge. Over both uniform and non-uniform environments, this selection method rapidly approaches ideal actions, showing the potential for soft growing robots in unstructured environment exploration and mapping.
@misc{fuentes2025sensing,title={Exteroception through Proprioception Sensing through Improved Contact Modeling for Soft Growing Robots},author={Fuentes, Francesco and Diagne, Serigne and Kingston, Zachary and Blumenschein, Laura H.},eprint={2507.10694},archiveprefix={arXiv},primaryclass={cs.RO},year={2025},note={Under Review},}
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},booktitle={IEEE-RAS International Conference on Soft Robotics},pages={1--8},year={2025},doi={10.1109/RoboSoft63089.2025.11020809},}
