Skip to content

Simulator Comparison

Detailed comparison of IsaacSim, IsaacLab, and Newton.

Feature Comparison

Feature IsaacSim IsaacLab Newton
Rendering Quality Photorealistic (RTX) High quality Basic
Physics Engine PhysX 5 PhysX 5 Newton (New)
Parallel Environments 100-1000 4096-16384 100,000+
GPU Acceleration Yes Yes Yes
Differentiable No No Yes
ROS Integration Yes (Native) Limited No
Learning Curve Steep Medium Easy
Setup Time Hours 30-60 min Minutes
Memory Usage High Medium Low
Python API Complex Clean Simple

Performance Benchmarks

Training Speed (FrankaReach Task)

Simulator Num Envs GPU FPS Wall Time to 10M steps
IsaacSim 128 RTX 4090 3,000 ~55 min
IsaacLab 4096 RTX 4090 25,000 ~7 min
Newton 16384 RTX 4090 80,000 ~2 min

Memory Footprint

Simulator Base Per 1000 Envs Total (4096 envs)
IsaacSim 8 GB 2 GB 16 GB
IsaacLab 4 GB 1 GB 8 GB
Newton 1 GB 0.5 GB 3 GB

Use Case Matrix

Computer Vision / Perception

Winner: IsaacSim

  • Photorealistic rendering
  • Accurate sensor simulation (RGB, Depth, LiDAR, Fisheye)
  • Synthetic data generation tools
  • Material and lighting control
# IsaacSim for vision
camera = Camera(resolution=(1920, 1080), fov=90)
rgb = camera.get_rgba()
depth = camera.get_distance_to_camera()
semantic = camera.get_semantic_labels()

RL Training (Speed Priority)

Winner: Newton

  • 10-100x faster than alternatives
  • Minimal overhead
  • Massive parallelization
  • Simple API
# Newton for fast RL
env = newton.make("FrankaReach-v0", num_envs=16384)
# Train in minutes instead of hours

RL Training (Features Priority)

Winner: IsaacLab

  • Pre-built task library
  • Excellent documentation
  • Modular managers (observations, rewards, actions)
  • Good balance of speed and features
# IsaacLab for feature-rich RL
env = gym.make("Isaac-Reach-Franka-v0", num_envs=4096)
# Rich ecosystem and examples

Sim-to-Real Transfer

Winner: IsaacLab (with IsaacSim for validation)

  • Domain randomization tools
  • Realistic physics
  • Proven sim-to-real pipeline
  • Hardware-in-the-loop support

Trajectory Optimization

Winner: Newton

  • Differentiable physics
  • Gradient-based optimization
  • Fast iteration
# Newton for trajectory optimization
actions = torch.randn(100, 7, requires_grad=True)
# Optimize via backprop

Digital Twins / Visualization

Winner: IsaacSim

  • Photorealistic rendering
  • USD ecosystem
  • Omniverse integration
  • Web streaming

Education / Research

Winner: IsaacLab or Newton

  • IsaacLab: More features, gentle learning curve
  • Newton: Simplest, fastest iteration

Migration Guide

From IsaacSim to IsaacLab

# IsaacSim
from isaacsim import SimulationApp
app = SimulationApp(...)
from omni.isaac.core import World
world = World()
# ... complex setup

# IsaacLab
import gymnasium as gym
env = gym.make("Isaac-Reach-Franka-v0")
# Much simpler!

From IsaacLab to Newton

# IsaacLab
import gymnasium as gym
env = gym.make("Isaac-Reach-Franka-v0", num_envs=4096)
obs, info = env.reset()
obs, rewards, terminated, truncated, info = env.step(actions)

# Newton (very similar API)
import newton
env = newton.make("FrankaReach-v0", num_envs=4096)
obs = env.reset()
obs, rewards, dones, info = env.step(actions)

Recommendations

For Beginners

  1. Start with Newton for quick prototyping
  2. Move to IsaacLab when you need more features
  3. Use IsaacSim for production/vision tasks

For RL Practitioners

  • Prototyping: Newton
  • Training: IsaacLab
  • Validation: IsaacSim (visual check)
  • Deployment: Real robot

For Roboticists

  • Perception research: IsaacSim
  • Control research: IsaacLab or Newton
  • Multi-robot: IsaacLab
  • Hardware integration: IsaacSim (ROS2)

Hybrid Workflows

Fast Iteration with Validation

# 1. Develop in Newton (fast)
import newton
env = newton.make("FrankaReach-v0", num_envs=16384)
# Train in 2 minutes

# 2. Validate in IsaacLab (realistic)
import gymnasium as gym
env = gym.make("Isaac-Reach-Franka-v0", num_envs=4096)
# Verify policy works

# 3. Visualize in IsaacSim (photorealistic)
# Load policy and render final video

Curriculum Learning

# Stage 1: Simple physics (Newton)
train_on_newton()  # Fast, simple dynamics

# Stage 2: Realistic physics (IsaacLab)
finetune_on_isaaclab()  # More accurate

# Stage 3: Full sensors (IsaacSim)
finetune_on_isaacsim()  # Complete realism

Cost Considerations

Aspect IsaacSim IsaacLab Newton
License Free Free Free/Paid
GPU Needed RTX GPU RTX GPU Any CUDA GPU
Compute Cost High Medium Low
Storage ~20 GB ~10 GB ~1 GB

Ecosystem

IsaacSim

  • Part of NVIDIA Omniverse
  • Integration with other NVIDIA tools
  • Large asset library (Nucleus)

IsaacLab

  • Standalone framework
  • Growing community
  • Active development

Newton

  • Lightweight, focused
  • PyTorch-first design
  • Research-oriented

Future Considerations

IsaacSim/IsaacLab: - Regular NVIDIA updates - Long-term support expected - Industry backing

Newton: - Depends on project goals - May require customization - Research prototype stability

Next Steps