CBMC-V3: A CNS-inspired Control Framework Towards Manipulation Agility with SNN

2025-11-06

AI Summary

Robustness and real-time performance of robotic manipulator dexterous manipulation remain inadequate in complex dynamic environments. Method: This paper proposes a central-nervous-system-inspired, end-to-end spiking neural network (SNN) control framework featuring a five-module, three-level hierarchical architecture—comprising cortex, cerebellum, thalamus, brainstem, and spinal cord—and integrating ascending sensory and descending motor pathways. Spinal feedback is modeled using leaky integrate-and-fire (LIF) neurons, while recurrent SNN dynamics enable online learning; non-spiking LIF units combined with reinforcement learning facilitate online parameter adaptation in the brainstem–thalamus loop. Contribution/Results: Simulation and physical robot experiments demonstrate that, compared to industrial-grade position control, the proposed method significantly improves response speed, operational agility, and robustness under variable loads, dynamic trajectories, and external disturbances.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Manipulation
• Machine Learning: Bio-inspired Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

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.

Problem

• Achieving agile robotic manipulation in complex dynamic environments
• Overcoming limitations of existing control algorithms for diverse objects
• Implementing biomimetic CNS-inspired control using Spiking Neural Networks

Innovation

• SNN-based biomimetic control with CNS modules
• Hierarchical control using LIF neurons and reinforcement learning
• Recurrent SNN for robotic arm dynamics learning