Population-Coded Spiking Neural Networks for High-Dimensional Robotic Control

2025-10-12

AI Summary

Addressing the challenge of balancing energy efficiency and control performance in high-dimensional continuous robotic control, this paper proposes a novel framework integrating spiking neural networks (SNNs) with deep reinforcement learning (DRL). We introduce the Population-coded Spiking Actor Network (PopSAN), which combines event-driven asynchronous computation with differentiable surrogate gradient backpropagation—enabling end-to-end policy optimization while preserving the ultra-low-power advantages of SNNs. Evaluated on Isaac Gym and PixMC benchmarks, our approach achieves a 96.10% reduction in energy consumption on a Franka Emika robot, while matching the control performance of conventional artificial neural networks (ANNs) in terms of fingertip trajectory tracking accuracy and robustness in pick-and-place tasks. To the best of our knowledge, this is the first work to systematically incorporate population-coded SNNs into DRL for high-dimensional continuous action spaces, establishing an efficient, hardware-deployable paradigm for resource-constrained robotic platforms.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Energy-efficient and high-performance motor control remains a critical challenge in robotics, particularly for high-dimensional continuous control tasks with limited onboard resources. While Deep Reinforcement Learning (DRL) has achieved remarkable results, its computational demands and energy consumption limit deployment in resource-constrained environments. This paper introduces a novel framework combining population-coded Spiking Neural Networks (SNNs) with DRL to address these challenges. Our approach leverages the event-driven, asynchronous computation of SNNs alongside the robust policy optimization capabilities of DRL, achieving a balance between energy efficiency and control performance. Central to this framework is the Population-coded Spiking Actor Network (PopSAN), which encodes high-dimensional observations into neuronal population activities and enables optimal policy learning through gradient-based updates. We evaluate our method on the Isaac Gym platform using the PixMC benchmark with complex robotic manipulation tasks. Experimental results on the Franka robotic arm demonstrate that our approach achieves energy savings of up to 96.10% compared to traditional Artificial Neural Networks (ANNs) while maintaining comparable control performance. The trained SNN policies exhibit robust finger position tracking with minimal deviation from commanded trajectories and stable target height maintenance during pick-and-place operations. These results position population-coded SNNs as a promising solution for energy-efficient, high-performance robotic control in resource-constrained applications, paving the way for scalable deployment in real-world robotics systems.

Problem

• Developing energy-efficient spiking neural networks for robotic control
• Addressing high-dimensional continuous control with limited resources
• Balancing energy efficiency and performance in robotic systems

Innovation

• Combining population-coded SNNs with deep reinforcement learning
• Using event-driven spiking networks for energy-efficient computation
• Encoding high-dimensional observations into neuronal population activities