受大脑启发的脉冲神经网络框架，提升机械臂在复杂环境中的敏捷操作能力。
CBMC-V3: A CNS-inspired Control Framework Towards Agile Manipulation with SNN
- 基于脑区结构设计五模块分层控制框架，全用脉冲神经网络实现。
- 在仿真与真实机器人平台上验证，显著优于基线方法的敏捷运动控制表现。
- 适合服务类机器人在非结构化场景中实现快速、灵活的操作。
随着机械臂应用从传统工业场景拓展至餐饮、家庭和零售等服务领域，现有控制算法难以应对动态轨迹、不可预测交互及多样化物体带来的非结构化环境挑战。本文提出一种受人类中枢神经系统（CNS）启发的生物仿生控制框架，采用脉冲神经网络（SNN）构建，包含大脑皮层、小脑、丘脑、脑干和脊髓五个控制模块，分为一阶、二阶、三阶三个层级，并设有上下行信息通路。所有模块均基于SNN实现。该框架在商业机器人平台上的仿真与实验中得到验证，涵盖多种控制任务。结果表明，所提方法在敏捷运动控制能力上显著优于基线模型，为实现复杂环境下的敏捷操作提供了可行且高效的技术方案。
原文摘要 · Abstract (English)
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.
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