mlsanityData to 14 Oct 2026

Fully Spiking Actor-Critic Neural Network for Robotic Manipulation

arXiv 2508.12038 ↗First posted 16 Aug 2025cs.ROcited by 0

On the map: Deep reinforcement learning methods → Spiking and neuromorphic RL

Abstract (arXiv)

This study proposes a hybrid curriculum reinforcement learning (CRL) framework based on a fully spiking neural network (SNN) for 9-degree-of-freedom robotic arms performing target reaching and grasping tasks. To reduce network complexity and inference latency, the SNN architecture is simplified to include only an input and an output layer, which shows strong potential for resource-constrained environments. Building on the advantages of SNNs-high inference speed, low energy consumption, and spike-based biological plausibility, a temporal progress-partitioned curriculum strategy is integrated with the Proximal Policy Optimization (PPO) algorithm. Meanwhile, an energy consumption modeling framework is introduced to quantitatively compare the theoretical energy consumption between SNNs and conventional Artificial Neural Networks (ANNs). A dynamic two-stage reward adjustment mechanism and optimized observation space further improve learning efficiency and policy accuracy. Experiments on the Isaac Gym simulation platform demonstrate that the proposed method achieves superior performance under realistic physical constraints. Comparative evaluations with conventional PPO and ANN baselines validate the scalability and energy efficiency of the proposed approach in dynamic robotic manipulation tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Spiking and neuromorphic RL, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 25 references that are in the map. First posted on arXiv 16 Aug 2025.

Builds on

12 of 25 references.

References · 12

  1. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  2. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 406
  3. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 266
  4. 2023Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networkscited by 122
  5. 2021Accurate and efficient time-domain classification with adaptive spiking recurrent neural networkscited by 115
  6. 2022Attention Spiking Neural Networkscited by 107
  7. 2021HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noisecited by 65
  8. 2020Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Controlcited by 49
  9. 2020Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardwarecited by 36
  10. 2018Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networkscited by 27
  11. 2019Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Datacited by 9
  12. 2023Fully Spiking Neural Network for Legged Robotscited by 6

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  1. 2025Population-Coded Spiking Neural Networks for High-Dimensional Robotic Control
  2. 2025Multimodal Spiking Neural Network for Space Robotic Manipulation
  3. 2025Energy-Efficient Deep Reinforcement Learning with Spiking Transformers
  4. 2026S2Act: Simple Spiking Actor
  5. 2024Neuromorphic force-control in an industrial task: validating energy and latency benefits ↗
  6. 2025Adaptive Surrogate Gradients for Sequential Reinforcement Learning in Spiking Neural Networks
  7. 2024SDP: Spiking Diffusion Policy for Robotic Manipulation with Learnable Channel-Wise Membrane Thresholds
  8. 2025SpikeRL: A Scalable and Energy-efficient Framework for Deep Spiking Reinforcement Learning