As robotic arm applications extend beyond industrial settings into healthcare, service, and daily life, existing control algorithms struggle to achieve the agile manipulation required for complex environments with dynamic trajectories, unpredictable interactions, and diverse objects. This paper presents a biomimetic control framework based on Spiking Neural Networks (SNN), inspired by the human Central Nervous System (CNS), to achieve agile control in such environments. The proposed framework features five control modules (cerebral cortex, cerebellum, thalamus, brainstem, spinal cord), three hierarchical control levels (first-order, second-order, third-order), and two information pathways (ascending, descending). Each module is fully implemented using SNN. The spinal cord module uses spike encoding and Leaky Integrate-and-Fire (LIF) neurons for feedback control. The brainstem module employs a network of LIF and non-spiking LIF neurons to dynamically adjust spinal cord parameters via reinforcement learning. The thalamus module similarly adjusts the cerebellum's torque outputs. The cerebellum module uses a recurrent SNN to learn the robotic arm's dynamics through regression, providing feedforward gravity compensation torques. The framework is validated both in simulation and on real-world robotic arm platform under various loads and trajectories. Results demonstrate that our method outperforms the industrial-grade position control in manipulation agility.

翻译：随着机械臂应用从工业场景扩展到医疗、服务和日常生活领域，现有控制算法难以在具有动态轨迹、不可预测交互和多样化物体的复杂环境中实现灵巧操作。本文提出一种受人类中枢神经系统启发的基于脉冲神经网络的仿生控制框架，以在此类环境中实现敏捷控制。该框架包含五个控制模块（大脑皮层、小脑、丘脑、脑干、脊髓）、三个层次化控制级别（一阶、二阶、三阶）以及两条信息通路（上行、下行）。所有模块均完全采用脉冲神经网络实现。脊髓模块利用脉冲编码和漏积分发放神经元进行反馈控制；脑干模块通过强化学习动态调整脊髓参数，其网络由漏积分发放神经元与非脉冲式漏积分发放神经元构成；丘脑模块以类似方式调节小脑的扭矩输出；小脑模块采用循环脉冲神经网络通过回归学习机械臂动力学，提供前馈重力补偿扭矩。该框架在仿真和真实机械臂平台上针对不同负载与轨迹进行了验证。结果表明，本方法在操作灵巧性方面优于工业级位置控制算法。