mlsanityData to 7 Oct 2026

Fully Spiking Neural Network for Legged Robots

arXiv 2310.05022 ↗First posted 8 Oct 2023cs.RO cs.AIcited by 4

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Abstract (arXiv)

Recent advancements in legged robots using deep reinforcement learning have led to significant progress. Quadruped robots can perform complex tasks in challenging environments, while bipedal and humanoid robots have also achieved breakthroughs. Current reinforcement learning methods leverage diverse robot bodies and historical information to perform actions, but previous research has not emphasized the speed and energy consumption of network inference and the biological significance of neural networks. Most networks are traditional artificial neural networks that utilize multilayer perceptrons (MLP). This paper presents a novel Spiking Neural Network (SNN) for legged robots, showing exceptional performance in various simulated terrains. SNNs provide natural advantages in inference speed and energy consumption, and their pulse-form processing enhances biological interpretability. This study presents a highly efficient SNN for legged robots that can be seamless integrated into other learning models.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 29 of its 52 references that are in the map. First posted on arXiv 8 Oct 2023.

Builds on

29 of 52 references.

References · 29

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  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2019Event-based Vision: A Surveycited by 1,010
  5. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 882
  6. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 430
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  8. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
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17 more
  1. 2021Deep Residual Learning in Spiking Neural Networkscited by 326
  2. 2016Training Deep Spiking Neural Networks using Backpropagationcited by 203
  3. 2018Long short-term memory and learning-to-learn in networks of spiking neuronscited by 179
  4. 2021Accurate and efficient time-domain classification with adaptive spiking recurrent neural networkscited by 113
  5. 2022Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behaviorcited by 113
  6. 2022Attention Spiking Neural Networkscited by 103
  7. 2021HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noisecited by 61
  8. 2020Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Controlcited by 44
  9. 2023DribbleBot: Dynamic Legged Manipulation in the Wildcited by 33
  10. 2020Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardwarecited by 32
  11. 2020Event-Driven Visual-Tactile Sensing and Learning for Robotscited by 27
  12. 2019Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAMcited by 19
  13. 2022Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motionscited by 13
  14. 2019Neural Population Coding for Effective Temporal Classificationcited by 10
  15. 2023Benchmarking Potential Based Rewards for Learning Humanoid Locomotioncited by 7
  16. 2018Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradientscited by 4
  17. 2020A Spiking Neural Network Emulating the Structure of the Oculomotor System Requires No Learning to Control a Biomimetic Robotic Headcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2026SpikeTrack: A Spike-driven Framework for Efficient Visual Trackingcited by 4
  2. 2024SDP: Spiking Diffusion Policy for Robotic Manipulation with Learnable Channel-Wise Membrane Thresholdscited by 1
  3. 2025SpikingSoft: A Spiking Neuron Controller for Bio-inspired Locomotion with Soft Snake Robotscited by 0
  4. 2025Spiking Neural Networks for Continuous Control via End-to-End Model-Based Learningcited by 0