GPU-Accelerated Robotic Simulation: Complete Training Guide
By Braincuber Team
Published on February 2, 2026
Training robots in the real world is slow, dangerous, and expensive. A quadruped robot learning to walk might fall thousands of times before mastering locomotion—each fall risking hardware worth tens of thousands of dollars. Modern simulation changes this equation entirely. What would take months of real-world training now compresses into hours of GPU-accelerated simulation.
This guide covers how to set up and run GPU-accelerated robotic simulation training. We'll walk through the architecture, training configurations, custom environments, and performance optimization. Whether you're training locomotion policies, manipulation tasks, or complex multi-robot scenarios, these patterns apply.
- Why simulation training matters for robotics
- How to configure training and evaluation jobs
- Building and deploying custom environments
- Multi-node distributed training for large experiments
- Performance optimization and instance selection
Why Simulation Training Matters
The shift to Physical AI—systems that perceive, reason, and act in the physical world—requires training methods that can safely explore millions of scenarios. GPU-accelerated simulation runs thousands of robot instances in parallel, enabling rapid iteration that's impossible in the real world.
Faster Iteration
Test new reward functions, robot designs, or control strategies in hours instead of weeks. A policy requiring 10 million environment steps can train overnight rather than over months.
Safe Exploration
Robots learn aggressive maneuvers and recover from failures without hardware damage. Explore edge cases that would be dangerous or impractical in the real world.
Reproducibility
Deterministic environments for comparing algorithms and tracking improvements. Run the exact same scenario thousands of times with controlled variations.
Massive Scale
Run hundreds of experiments in parallel. Enable systematic hyperparameter searches that would be impossible with physical robots.
Architecture Overview
A production-grade simulation training pipeline orchestrates multiple services to deliver seamless training and evaluation:
Key Infrastructure Components
- Batch Compute Environment: Auto-scaling containerized environment across GPU instances (from entry-level to high-performance)
- Shared File System: Storage for training checkpoints across multi-node jobs
- Container Registry: Hosts the simulation container image
- Workflow Orchestration: Manages job lifecycle with proper error handling and timeouts
- Monitoring: Container insights for observability and debugging
Training Mode Configuration
Training mode creates new policies from scratch. You specify the simulation task, number of parallel environments, and training iterations. The pipeline handles everything else: downloading environments, executing the training loop, saving checkpoints, and uploading trained policies.
{
"name": "Quadruped Locomotion Training",
"description": "Train PPO policy for rough terrain locomotion",
"trainingConfig": {
"mode": "train",
"task": "Velocity-Rough-Quadruped-v0",
"numEnvs": 4096,
"maxIterations": 3000,
"rlLibrary": "rsl_rl"
},
"computeConfig": {
"numNodes": 1
}
}Configuration Parameters
numEnvs
Number of parallel simulation instances. Higher values increase GPU utilization. For locomotion tasks, 4096 typically achieves good saturation.
maxIterations
Total training iterations. More complex tasks need more iterations. Start with 1000 for initial testing, scale up for production.
rlLibrary
Reinforcement learning framework. Common choices include RSL-RL (optimized for locomotion) or Stable Baselines3.
numNodes
GPU nodes for distributed training. Set to 1 for single-node, increase for massive experiments requiring parallel compute.
Training Outputs
Training outputs are organized under a unique job identifier:
{uuid}/checkpoints/model_*.pt– Model checkpoints at regular intervals{uuid}/metrics.csv– Training metrics exported from TensorBoard{uuid}/training-config.json– Copy of input configuration{uuid}/*.txt– Log files for debugging
Evaluation Mode Configuration
Once you have a trained policy, evaluation mode assesses its performance. This loads an existing policy and runs it through a specified number of episodes, collecting metrics and recording videos.
{
"name": "Quadruped Evaluation Job",
"description": "Evaluate trained policy on rough terrain",
"trainingConfig": {
"mode": "evaluate",
"task": "Velocity-Rough-Quadruped-v0",
"checkpointPath": "checkpoints/model_3000.pt",
"numEnvs": 4,
"numEpisodes": 10,
"stepsPerEpisode": 1000,
"rlLibrary": "rsl_rl"
},
"computeConfig": {
"numNodes": 1
}
}| Parameter | Training Mode | Evaluation Mode |
|---|---|---|
| numEnvs | 4096 (high throughput) | 4 (assessment focus) |
| Purpose | Learn new behaviors | Assess performance |
| Outputs | Model checkpoints, metrics | Videos, evaluation metrics |
| Duration | Hours to days | Minutes |
Building Custom Environments
Pre-built environments cover common tasks, but many projects require custom scenarios. Here's how to create and deploy your own:
my_custom_env/
├── setup.py # Package installation
├── my_custom_env/
│ ├── __init__.py # Contains gym.register() call
│ ├── my_env.py # Environment implementation
│ └── my_env_cfg.py # Configuration classes
└── agents/
└── rsl_rl_ppo_cfg.py # RL algorithm configCreate the Environment
Implement your custom environment following the template structure. Define observations, actions, rewards, and termination conditions.
Package as Archive
tar -czf my_custom_env.tar.gz my_custom_env/
Upload to Asset System
Upload via web UI or API. The system tracks version history and lineage.
Reference in Training Config
{
"trainingConfig": {
"task": "MyCustom-Robot-v0",
"numEnvs": 4096,
"maxIterations": 5000
},
"customEnvironmentPath": "environments/my_custom_env.tar.gz"
}Performance Optimization
Getting the most out of GPU-accelerated simulation requires understanding instance selection and optimization strategies.
GPU Instance Selection
| Instance Type | GPU | Use Case | Relative Cost |
|---|---|---|---|
| g6.2xlarge | L4 (24GB) | Development, smaller models | $ |
| g6.12xlarge | L4 (24GB) x4 | Multi-GPU parallel training | $$ |
| g6e.12xlarge | L40S (48GB) | Large models, high env count | $$$ |
| g5.xlarge | A10G (24GB) | Fallback option | $$ |
The pipeline uses BEST_FIT_PROGRESSIVE allocation, prioritizing G6 for price/performance, then G6E, with G5 as fallback.
Multi-Node Training
For the largest experiments, enable distributed training across multiple GPU nodes:
{
"computeConfig": {
"numNodes": 4
}
}Checkpoints sync automatically via shared file systems.
Container Pull Optimization
The simulation container is ~10GB. Optimize startup with:
- Warm instances: Keep instances running to avoid cold starts
- Pre-baked AMI: Custom AMI with container pre-cached
- Docker layer caching: Larger EBS volumes for layer reuse
Environment Scaling
Tune numEnvs based on task complexity:
- Simple tasks: 4096-8192 environments
- Complex manipulation: 2048-4096 environments
- Heavy physics: 1024-2048 environments
{
"app": {
"useGlobalVpc": {
"enabled": true,
"addVpcEndpoints": true
},
"pipelines": {
"useSimulationTraining": {
"enabled": true,
"acceptEula": true,
"autoRegister": true,
"keepWarmInstance": false
}
}
}
}GPU-accelerated simulation training transforms robotics development from a hardware-limited process into a software-scalable one. The combination of parallel environments, cloud-native orchestration, and asset management integration creates a powerful platform for physical AI development. Start with pre-built environments to validate the pipeline, then graduate to custom environments as your requirements evolve.
Frequently Asked Questions
Simulation training offers four major advantages: speed (10 million environment steps can complete overnight instead of over months), safety (robots can explore dangerous maneuvers without hardware damage), reproducibility (deterministic environments for comparing algorithms), and scale (run hundreds of experiments in parallel). A policy that requires millions of training steps would be impractical or impossible to train on physical hardware due to time, cost, and safety constraints.
The optimal number depends on task complexity and GPU memory. For locomotion tasks, 4096 environments typically achieve good GPU saturation on modern GPUs (24GB+ VRAM). Complex manipulation tasks might use 2048-4096, while physics-heavy scenarios might need 1024-2048. Start with 4096 for quick iteration, monitor GPU utilization, and adjust based on whether you're hitting memory limits or leaving compute unused.
Training mode creates new policies from scratch by running millions of simulation steps and updating the neural network weights. It uses high parallelism (4096+ environments) for throughput and outputs model checkpoints. Evaluation mode loads an existing trained policy and assesses its performance over specific episodes, using fewer environments (4-8) and outputting videos and metrics. Use training to learn behaviors; use evaluation to measure how well learned behaviors perform.
Create a Python package following the standard template structure: environment implementation, configuration classes, and RL algorithm config. Package it as a tarball (tar -czf my_env.tar.gz my_env/), upload to your asset management system, and reference it in the training configuration with a customEnvironmentPath field. The pipeline automatically downloads and installs the custom environment before training begins.
For best price/performance, start with L4 GPU instances (g6 family). These offer 24GB VRAM and handle most locomotion and manipulation training efficiently. For larger models or higher environment counts, L40S instances (g6e family) provide 48GB VRAM. A10G instances (g5 family) serve as fallback. For massive experiments, enable multi-node training across multiple instances using distributed PyTorch (torchrun) with shared file systems for checkpoint synchronization.