Deformable robots are notoriously difficult to model or control due to its high-dimensional configuration spaces. Direct trajectory optimization suffers from the curse-of-dimensionality and incurs a high computational cost, while learning-based controller optimization methods are sensitive to hyper-parameter tuning. To overcome these limitations, we hypothesize that high fidelity soft robots can be both simulated and controlled by restricting to low-dimensional spaces. Under such assumption, we propose a two-stage algorithm to identify such simulation- and control-spaces. Our method first identifies the so-called simulation-space that captures the salient deformation modes, to which the robot's governing equation is restricted. We then identify the control-space, to which control signals are restricted. We propose a multi-fidelity Riemannian Bayesian bilevel optimization to identify task-specific control spaces. We show that the dimension of control-space can be less than $10$ for a high-DOF soft robot to accomplish walking and swimming tasks, allowing low-dimensional MPC controllers to be applied to soft robots with tractable computational complexity.

翻译：可变形机器人由于其高维配置空间而极难建模或控制。直接轨迹优化受维度灾难影响且计算成本高昂，而基于学习的控制器优化方法对超参数调优敏感。为克服这些局限，我们假设高保真软体机器人可通过限定于低维空间实现仿真与控制。基于此假设，我们提出两阶段算法来识别此类仿真空间与控制空间。该方法首先识别捕捉显著变形模态的所谓仿真空间，并将机器人控制方程限定于此空间内。随后识别控制空间，并将控制信号限制在该空间内。我们提出多保真黎曼贝叶斯双层优化方法以识别任务特定的控制空间。研究表明，对于执行行走与游泳任务的高自由度软体机器人，控制空间维度可降至$10$以下，使得低维MPC控制器能够以可计算复杂度应用于软体机器人。