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.

翻译：软体生长机器人（即藤蔓机器人）是一类具有前景的软体机器人，能够在紧密受限环境中实现导航与生长。然而，由于充气结构与不可延伸材料之间复杂的相互作用，这类机器人的建模与控制仍面临挑战，这给自主操作与设计优化带来了障碍。尽管已有针对此类系统的仿真器在匹配高层行为方面取得了定性与定量的成功，但它们通常仍难以通过简化的参数模型捕捉真实的藤蔓机器人形态，且在规划与参数优化所需的高通量仿真中存在困难。我们提出了一种针对此类系统的可微分仿真器，使其能够嵌入基于梯度的优化方法中，以解决上述问题。借助该方法实现的更复杂参数拟合，我们通过实验验证并集成了一种基于局部材料褶皱第一性原理的薄壁充气管非线性刚度闭式模型。我们的仿真器还通过利用现有的可微分计算框架，充分发挥数据并行操作的优势，支持多个仿真场景的同时运行。我们验证了在仿真器中采用基于物理的非线性刚度模型的可行性，并展示了其如何成为仿真到现实迁移的有效工具。我们的实现已开源提供。