SoRoMoX：快速、可微分且可并行化的软体机器人模型
SoRoMoX: Fast, Differentiable, and Parallelizable Soft Robot Models
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中文总结 AI 辅助
本文提出首个直接在GPU运行且端到端可微分的软体机器人建模框架SoRoMoX，其CPU顺序rollout提速18.1倍、GPU并行吞吐量提升234.6倍，可实现高精度系统辨识、控制优化及强化学习等先进工作流。
中文摘要 AI 辅助
基于Cosserat杆理论的降阶模型现已成熟，建模理论不再是软体机器人控制的主要瓶颈。然而，这些模型的实现方式不支持支撑先进刚性机器人应用的可微分、GPU并行及面向控制的工作流。本文通过SoRoMoX（基于JAX的软体机器人模型）填补这一空白，它是一个完全数值化、可即时编译（JIT-compilable）的Python/JAX框架。SoRoMoX通过统一的、可直接用于控制的接口实现了铰接式、分段常应变及变应变模型，提供惯性矩阵、重力与弹性力、雅可比矩阵及其导数。据我们所知，它是首个直接在GPU上运行且对状态、输入和参数具有端到端可微分性的基于杆/应变的软体机器人建模框架。与现有最优替代方案相比，CPU上的顺序 rollout 速度最高提升18.1倍，而GPU并行 rollout 的吞吐量最高提升234.6倍。该性能使此前不切实际或无法实现的工作流成为可能：静态平衡系统辨识的标记均方根误差（RMSE）降低66%；残余力学习进一步降低64%；计算力矩跟踪的RMSE相比无模型PD控制降低约500倍；控制增益优化的损失比未调谐增益低62%；使用高阶控制障碍函数的安全约束控制可将峰值接触力控制在规定的5 N范围内，而无安全约束时为33.5 N；强化学习策略训练通过大规模并行 rollout 比CPU上的PyElastica离散杆基线快7倍。
英文摘要
Reduced-order models based on Cosserat-rod theory are now well established, and modeling theory is no longer the primary bottleneck in soft-robot control. Their implementations, however, do not support the differentiable, GPU-parallel, and control-oriented workflows that underpin advanced rigid-robotics applications. Here, we fill this gap with SoRoMoX (Soft Robot Models in JAX), a fully numerical, JIT-compilable Python/JAX framework. SoRoMoX implements articulated, Piecewise Constant Strain, and Variable Strain models through a unified, control-ready interface that provides inertia matrices, gravitational and elastic forces, Jacobians, and their derivatives. To our knowledge, it is the first rod/strain-based soft-robot modeling framework that runs directly on GPUs, with Warp kernels accelerating parallel continuum-model execution, and is end-to-end differentiable with respect to states, inputs, and parameters. Sequential CPU rollouts are up to 27.0 times faster than SoRoSim, while GPU-parallel GVS rollouts increase throughput by up to 679.7 times. This performance enables workflows that were previously impractical or impossible: static-equilibrium system identification with 66% lower marker RMSE; residual-force learning with a further 64% reduction; computed-torque tracking with RMSE reduced by a factor of approximately 500 relative to model-free PD; control-gain optimization with up to 98% lower loss than untuned gains; safety-constrained control using high-order control barrier functions to keep the peak contact force within a prescribed 5 N bound, compared with 33.5 N without the safety constraint; and reinforcement-learning policy training up to 7 times faster than a CPU PyElastica discrete-rod baseline through massively parallel rollouts.
