Are there any resources on using Spiking Neural Network (SNN) with Isaac Lab for reinforcement learning?

[H-Hisamichi]

Hi everyone,

Lately, I've become interested in Spiking Neural Network (SNN).
For mobile robots, where control policies can become complex, I believe lightweight neural networks are very important from a computational cost perspective.
However, as I started to look into SNN, I realized that their training methods are quite different from those of traditional neural networks.
I'm wondering if there are any existing projects or resources related to using SNN-based robot controllers with Isaac Lab.

If anyone has any info or links, I’d love to check them out!

[RandomOakForest]

It seems so, however, there is some work to do to integrate such examples to Isaac Lab. Here is a list of recent findings:

SpikeGym Framework for SNN-PPO Training

A dedicated framework called SpikeGym has been developed to enable SNN training using Proximal Policy Optimization (PPO) in NVIDIA Isaac Gym environments¹²³. Key features include:

• GPU-accelerated training: Reduces SNN training time from 3+ hours to ~7 minutes compared to prior tools¹.
• Integration with Isaac Gym: Built on the skrl RL library, supporting both Isaac Gym and MuJoCo simulators¹².
• Architecture flexibility: Allows configuration of SNN layers, neuron types (Rectangular/Gaussian/Sigmoid), and comparison with ANNs via YAML files¹.

Performance Insights from Experiments

• SNNs vs. ANNs:
  • SNNs outperformed ANNs in simpler tasks (e.g., Cartpole) with shallow networks but struggled with deeper layers in complex tasks like Ant locomotion¹².
  • Achieved 4.4× performance improvement over state-of-the-art SNNs in the Ant-v4 MuJoCo benchmark¹².
• Training acceleration: Enabled rapid exploration of SNN topologies and hyperparameters using Isaac Gym's parallel simulation capabilities¹⁴.

Isaac Lab Integration

While SpikeGym specifically targets Isaac Gym, which is no longer supported, NVIDIA Isaac Lab provides complementary tools for RL workflows:

• Massively parallel environments: Accelerates policy training through GPU-optimized simulations⁵⁴.
• Modular pipeline: Supports perception-enabled RL and seamless transition from simulation to real robots⁵.
• Tutorials: Official Isaac Lab documentation and tutorials cover RL setup with frameworks like skrl, which is compatible with SpikeGym⁶⁴.

Key Resources

1. SpikeGym Framework:
   • Code and configuration files (GitHub)¹².
   • Research papers detailing SNN performance in Isaac Gym environments¹²³.
2. Isaac Lab Documentation:
   • Tutorials on RL training pipelines with skrl⁶⁴.
   • Reference architecture for single-GPU training workflows⁴.

For practical implementation, combine SpikeGym's SNN components with Isaac Lab's RL infrastructure.

Footnotes

1. https://www.nature.com/articles/s41598-024-77779-8 ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹

2. https://pmc.ncbi.nlm.nih.gov/articles/PMC11680704/ ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

3. https://orbit.dtu.dk/en/publications/exploring-spiking-neural-networks-for-deep-reinforcement-learning ↩ ↩²

4. https://isaac-sim.github.io/IsaacLab/main/source/refs/reference_architecture/index.html ↩ ↩² ↩³ ↩⁴ ↩⁵

5. https://www.nvidia.com/en-us/use-cases/reinforcement-learning/ ↩ ↩²

6. https://www.youtube.com/watch?v=BSQEYj3Wm0Q ↩ ↩²

[H-Hisamichi]

Hi @RandomOakForest,

Thank you for the information!
I’ll read the documentation right away.