As robotic arm applications expand beyond traditional industrial settings into service-oriented domains such as catering, household and retail, existing control algorithms struggle to achieve the level of agile manipulation required in unstructured environments characterized by dynamic trajectories, unpredictable interactions, and diverse objects. This paper presents a biomimetic control framework based on Spiking Neural Network (SNN), inspired by the human Central Nervous System (CNS), to address these challenges. The proposed framework comprises five control modules-cerebral cortex, cerebellum, thalamus, brainstem, and spinal cord-organized into three hierarchical control levels (first-order, second-order, and third-order) and two information pathways (ascending and descending). All modules are fully implemented using SNN. The framework is validated through both simulation and experiments on a commercial robotic arm platform across a range of control tasks. The results demonstrate that the proposed method outperforms the baseline in terms of agile motion control capability, offering a practical and effective solution for achieving agile manipulation.

翻译：随着机械臂应用从传统工业场景扩展到餐饮、家庭和零售等面向服务的领域，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特征的非结构化环境中实现所需的敏捷操作水平。本文提出一种基于脉冲神经网络（SNN）的仿生控制框架，其灵感来源于人类中枢神经系统（CNS），以应对这些挑战。所提出的框架包含五个控制模块——大脑皮层、小脑、丘脑、脑干和脊髓——组织为三个层次控制级别（一阶、二阶和三阶）和两条信息通路（上行与下行）。所有模块均完全采用SNN实现。该框架通过仿真实验及在商用机械臂平台上的一系列控制任务进行了验证。结果表明，所提方法在敏捷运动控制能力方面优于基线方法，为实现敏捷操作提供了一种实用且有效的解决方案。