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CBMC-V3: A CNS-inspired Control Framework Towards Manipulation Agility with SNN
arXiv:2511.04109v2 Announce Type: replace
Abstract: As robotic arm applications extend beyond industrial settings into service-oriented sectors such as catering, household and retail, 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 (SNNs), 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, and spinal cord), three hierarchical control levels (first-order, second-order, and third-order), and two information pathways (ascending and 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 employs a network of LIF and non-spiking LIF neurons to adjust the cerebellum's torque outputs via reinforcement learning. The cerebellum module, which provides feedfoward gravity compensation torques, uses a recurrent SNN to learn the robotic arm's dynamics through regression. 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.
Abstract: As robotic arm applications extend beyond industrial settings into service-oriented sectors such as catering, household and retail, 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 (SNNs), 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, and spinal cord), three hierarchical control levels (first-order, second-order, and third-order), and two information pathways (ascending and 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 employs a network of LIF and non-spiking LIF neurons to adjust the cerebellum's torque outputs via reinforcement learning. The cerebellum module, which provides feedfoward gravity compensation torques, uses a recurrent SNN to learn the robotic arm's dynamics through regression. 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.
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