A Neuromorphic Reinforcement Learning Framework for Efficient Pathfinding in Robotic Mobile Fulfillment Systems

• URL: http://arxiv.org/abs/2606.20031v2
• Date: Mon, 22 Jun 2026 02:52:30 GMT
• Title: A Neuromorphic Reinforcement Learning Framework for Efficient Pathfinding in Robotic Mobile Fulfillment Systems
• Abstract summary: We present SDQN-RMFS, an end-to-end framework that achieves high-fidelity deployment of an RL-trained policy.<n>By computing only when triggered by sparse events, this framework unlocks ultra-low-power Robotic Mobile Fulfillment Systems.
• Score: 17.217026932751132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic environmental changes, confined workspaces, and stringent real-time constraints make pathfinding in Robotic Mobile Fulfillment Systems (RMFS) a challenging problem for conventional search- and rule-based methods, which typically suffer from high computational complexity and long decision latency. While reinforcement learning (RL) has emerged as a powerful alternative, deploying learned policies with extreme energy efficiency on resource-constrained hardware remains an open challenge. We present SDQN-RMFS, an end-to-end framework that achieves high-fidelity deployment of an RL-trained policy from a full-precision artificial neural network (ANN) through to a neuromorphic chip. By computing only when triggered by sparse events, this framework unlocks ultra-low-power RMFS pathfinding. Our full-stack pipeline operates as follows: an ANN policy is first efficiently trained via a collision-allowing strategy to densify informative trajectories, and then converted into a spiking neural network (SNN) via a hard-label knowledge distillation approach. This effectively addresses the output distribution mismatch, preserving policy capability across the ANN-to-SNN pipeline while substantially reducing inference latency. Hardware experiments demonstrate up to 11,281$\times$ energy savings and a nearly two-fold reduction in latency compared to a high-performance GPU baseline, while maintaining decision quality on par with the original trained policy. These results establish physical neuromorphic inference as a practical and energy-sustainable pathway for large-scale RMFS operations.

Related papers

• Reducing ANN-SNN Conversion Error via Residual Membrane Potential Alignment [52.910770415431244]
  Spiking Neural Networks (SNNs) serve as core architectures for neuromorphic computing.<n>ANN-SNN conversion circumvents issues by reusing well-trained ANN weights for low-latency, energy-efficient inference.<n>Existing conversion schemes suffer from severe accuracy drops at small timesteps, large inference delays and cumulative quantization errors.
  arXiv  Detail & Related papers  (2026-08-14T04:46:03Z)
• Brain-Inspired Multimodal Spiking Neural Network for Image-Text Retrieval [79.04396446873932]
  Spiking neural networks (SNNs) have recently shown strong potential in unimodal visual and textual tasks.<n>Building a directly trained, low-energy, and high-performance SNN for multimodal applications such as image-text retrieval (ITR) remains highly challenging.<n>We present a brain-inspired Cross-Modal Spike Fusion network (CMSF) and apply it to ITR for the first time.
  arXiv  Detail & Related papers  (2026-03-25T08:41:07Z)
• A Latency Coding Framework for Deep Spiking Neural Networks with Ultra-Low Latency [51.75891259753546]
  Spiking neural networks (SNNs) offer a biologically inspired computing paradigm with significant potential for energy-efficient neural processing.<n>Time-To-First-Spike (TTFS) coding encodes information through the precise timing of a neuron's first spike.<n>TTFS models lack efficient training methods, suffering from high inference latency and limited performance.<n>We present a comprehensive framework, which enables the efficient training of deep TTFS-coded SNNs by employing backpropagation throuh time (BPTT) algorithm.
  arXiv  Detail & Related papers  (2026-03-24T13:52:03Z)
• Population-Coded Spiking Neural Networks for High-Dimensional Robotic Control [0.9916299448089259]
  This paper introduces a novel framework combining population-coded Spiking Neural Networks (SNNs) with Deep Reinforcement Learning (DRL)<n>We show that our approach achieves energy savings of up to 96.10% compared to traditional Artificial Neural Networks (ANNs) while maintaining comparable control performance.<n>These results position population-coded SNNs as a promising solution for energy-efficient, high-performance robotic control in resource-constrained applications.
  arXiv  Detail & Related papers  (2025-10-12T09:27:25Z)
• FFGAF-SNN: The Forward-Forward Based Gradient Approximation Free Training Framework for Spiking Neural Networks [7.310627646090302]
  Spiking Neural Networks (SNNs) offer a biologically plausible framework for energy-efficient neuromorphic computing.<n>It is a challenge to train SNNs due to their non-differentiability, efficiently.<n>We propose a Forward-Forward (FF) based gradient approximation-free training framework for Spiking Neural Networks.
  arXiv  Detail & Related papers  (2025-07-31T15:22:23Z)
• Learning to Control Dynamical Agents via Spiking Neural Networks and Metropolis-Hastings Sampling [1.0533738606966752]
  Spiking Neural Networks (SNNs) offer biologically inspired, energy-efficient alternatives to traditional Deep Neural Networks (DNNs) for real-time control systems.<n>We introduce what is, to our knowledge, the first framework that employs Metropolis-Hastings sampling, a Bayesian inference technique, to train SNNs for dynamical agent control in RL environments.
  arXiv  Detail & Related papers  (2025-07-13T08:50:00Z)
• Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control [59.65431931190187]
  Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision making on neuromorphic hardware.<n>Most continuous control algorithms for continuous control are designed for Artificial Neural Networks (ANNs)<n>We show that this mismatch destabilizes SNN training and degrades performance.<n>We propose a novel proxy target framework to bridge the gap between discrete SNNs and continuous-control algorithms.
  arXiv  Detail & Related papers  (2025-05-30T03:08:03Z)
• Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning [69.00997996453842]
  We propose a deep Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for virtual network embedding. We show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue.
  arXiv  Detail & Related papers  (2024-06-25T07:42:30Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks [51.92362217307946]
  Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems. PINNs are trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features. In this paper, we propose to employ implicit gradient descent (ISGD) method to train PINNs for improving the stability of training process.
  arXiv  Detail & Related papers  (2023-03-03T08:17:47Z)
• Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control [0.0]
  We propose a population-coded spiking actor network (PopSAN) trained in conjunction with a deep critic network using deep reinforcement learning (DRL) We deployed the trained PopSAN on Intel's Loihi neuromorphic chip and benchmarked our method against the mainstream DRL algorithms for continuous control. Our results support the efficiency of neuromorphic controllers and suggest our hybrid RL as an alternative to deep learning, when both energy-efficiency and robustness are important.
  arXiv  Detail & Related papers  (2020-10-19T16:20:45Z)
• Deep Networks with Fast Retraining [0.0]
  This paper proposes a novel MP inverse-based fast retraining strategy for deep convolutional neural network (DCNN) learning. In each training, a random learning strategy that controls the number of convolutional layers trained in the backward pass is first utilized. Then, an MP inverse-based batch-by-batch learning strategy, which enables the network to be implemented without access to industrial-scale computational resources, is developed.
  arXiv  Detail & Related papers  (2020-08-13T15:17:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.