Reed Emily A, Pereira Marcus A, Valero-Cuevas Francisco J, Theodorou Evangelos A
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4694-4699. doi: 10.1109/EMBC44109.2020.9175861.
Determining how the nervous system controls tendon-driven bodies remains an open question. Stochastic optimal control (SOC) has been proposed as a plausible analogy in the neuroscience community. SOC relies on solving the Hamilton-Jacobi-Bellman equation, which seeks to minimize a desired cost function for a given task with noisy controls. We evaluate and compare three SOC methodologies to produce tapping by a simulated planar 3-joint human index finger: iterative Linear Quadratic Gaussian (iLQG), Model-Predictive Path Integral Control (MPPI), and Deep Forward-Backward Stochastic Differential Equations (FBSDE). We show that averaged over 128 repeats these methodologies can place the fingertip at the desired final joint angles but-because of kinematic redundancy and the presence of noise-they each have joint trajectories and final postures with different means and variances. iLQG in particular, had the largest kinematic variance and departure from the final desired joint angles. We demonstrate that MPPI and FBSDE have superior performance for such nonlinear, tendon-driven systems with noisy controls.Clinical relevance- The mathematical framework provided by MPPI and FBSDE may be best suited for tendon-driven anthropomorphic robots, exoskeletons, and prostheses for amputees.
确定神经系统如何控制肌腱驱动的身体仍然是一个悬而未决的问题。随机最优控制（SOC）在神经科学界被认为是一种合理的类比。SOC依赖于求解汉密尔顿-雅可比-贝尔曼方程，该方程旨在在存在噪声控制的情况下，将给定任务的期望成本函数最小化。我们评估并比较了三种SOC方法，以通过模拟的平面三关节人类食指产生敲击动作：迭代线性二次高斯（iLQG）、模型预测路径积分控制（MPPI）和深度前向-后向随机微分方程（FBSDE）。我们表明，在128次重复实验中进行平均，这些方法可以将指尖置于期望的最终关节角度，但由于运动冗余和噪声的存在，它们各自具有不同均值和方差的关节轨迹和最终姿势。特别是iLQG，其运动学方差最大，且偏离最终期望的关节角度。我们证明，对于这种具有噪声控制的非线性肌腱驱动系统，MPPI和FBSDE具有卓越的性能。临床相关性——MPPI和FBSDE提供的数学框架可能最适合肌腱驱动的拟人机器人、外骨骼以及截肢者的假肢。
