Beyond providing accurate movements, achieving smooth motion trajectories is a long-standing goal of robotics control theory for arms aiming to replicate natural human movements. Drawing inspiration from biological agents, whose reaching control networks effortlessly give rise to smooth and precise movements, can simplify these control objectives for robot arms. Neuromorphic processors, which mimic the brain's computational principles, are an ideal platform to approximate the accuracy and smoothness of biological controllers while maximizing their energy efficiency and robustness. However, the incompatibility of conventional control methods with neuromorphic hardware limits the computational efficiency and explainability of their existing adaptations. In contrast, the neuronal subnetworks underlying smooth and accurate reaching movements are effective, minimal, and inherently compatible with neuromorphic hardware. In this work, we emulate these networks with a biologically realistic spiking neural network for motor control on neuromorphic hardware. The proposed controller incorporates experimentally-identified short-term synaptic plasticity and specialized neurons that regulate sensory feedback gain to provide smooth and accurate joint control across a wide motion range. Concurrently, it preserves the minimal complexity of its biological counterpart and is directly deployable on Intel's neuromorphic processor. Using the joint controller as a building block and inspired by joint coordination in human arms, we scaled up this approach to control real-world robot arms. The trajectories and smooth, bell-shaped velocity profiles of the resulting motions resembled those of humans, verifying the biological relevance of the controller. Notably, the method achieved state-of-the-art control performance while decreasing the motion jerk by 19% to improve motion smoothness.

翻译：超越精确运动，实现平滑运动轨迹是机械臂控制理论中旨在复制人类自然运动的长期目标。生物体的抓取控制网络能够毫不费力地产生平滑而精确的运动，从中汲取灵感可简化机械臂的这些控制目标。神经形态处理器模仿大脑的计算原理，是逼近生物控制器精确性和平滑性、同时最大化能效与鲁棒性的理想平台。然而，传统控制方法与神经形态硬件的不兼容性限制了其现有适配方案的计算效率和可解释性。相比之下，支撑平滑精确抓取运动的神经元亚网络高效、简洁，且天然与神经形态硬件兼容。本研究利用生物逼真的脉冲神经网络在神经形态硬件上模拟这些网络，实现运动控制。所提出的控制器整合了实验确认的短时突触可塑性和调控感觉反馈增益的特化神经元，从而在大范围运动内提供平滑精确的关节控制。同时，该方法保留了生物对应物的最小复杂度，可直接部署于Intel神经形态处理器。以该关节控制器为基本单元，并受人类手臂关节协调机制启发，我们将该方法扩展至真实机械臂控制。最终运动的轨迹和光滑的钟形速度曲线与人类运动相似，验证了控制器的生物相关性。值得注意的是，该方法在实现领先控制性能的同时，将运动加加速度降低了19%，从而提升了运动平滑度。