AI 中文总结
该研究通过可微分物理模型等方法协同优化软机器人储备池，使其在多任务上较未优化的软机器人储备池平均提升33.7%，接近数字参考，验证了动力学级协同优化的可行性。
AI 中文摘要
软机器人基质是物理储备池计算（PRC）的理想载体，因为其柔顺的非线性动力学可提供时间记忆、高维状态变换及高效推理能力。然而，物理储备池常被直接使用，而非经过预训练或协同优化，这可能导致软机器人PRC的性能弱于数字储备池。本研究探讨物理储备池是否可针对高性能数字参考动力学进行预训练。我们的方案利用可微分物理模型和避免时间积分的加速度级方程误差目标，协同优化物理参数、微分同胚的物理-参考状态映射及前馈-反馈控制。作为概念验证，我们用模拟软机器人、随机振荡器网络（RON）参考及并行多起点梯度下降实现该方案。我们在4种储备池维度下，针对分类任务（sMNIST和ADIAC）与预测任务（Mackey-Glass和Lorenz96）评估优化后的储备池。与未优化的软机器人储备池相比，优化后的储备池在所有任务和数据集上实现了33.7%的平均相对提升，同时接近数字参考。这些结果证明了针对所考虑的模拟软机器人储备池进行动力学级协同优化的可行性。
英文摘要
Soft robotic substrates are promising for Physical Reservoir Computing (PRC) because their compliant nonlinear dynamics can provide temporal memory, high-dimensional state transformations, and efficient inference. However, physical reservoirs are often adopted as-is rather than pretrained or co-optimized, potentially limiting soft robotic PRC performance relative to digital reservoirs. We investigate whether a physical reservoir can instead be pretrained against high-performing digital reference dynamics. Our formulation jointly optimizes physical parameters, a diffeomorphic physical-reference state map, and feedforward-feedback control using a differentiable physical model and an acceleration-level equation-error objective that avoids temporal integration. As a proof of concept, we instantiate the formulation with simulated soft robots, a Random Oscillators Network (RON) reference, and parallel multi-start gradient descent. We evaluate the optimized reservoirs on classification (sMNIST and ADIAC) and forecasting (Mackey-Glass and Lorenz96) tasks across four reservoir dimensions. Compared with unoptimized soft robot reservoirs, the optimized reservoirs achieve a mean relative improvement of 33.7% across all tasks and datasets, while remaining close to the digital reference. These results demonstrate the feasibility of dynamics-level co-optimization for the simulated soft robotic reservoirs considered here.
