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isaac-patterns

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NVIDIA Isaac Sim and Isaac ROS integration patterns for Physical AI applications.

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Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/testing/isaac-patterns-uneezaismail-physical-ai-humanoid/SKILL.md

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Isaac Sim USD Scene Structure

Asset Organization (Universal Scene Description)

/World
├── /Environment
│   ├── /Ground_Plane (physics material: friction 0.8)
│   ├── /Obstacles (procedurally generated for domain randomization)
│   └── /Lighting
│       ├── /DomeLight (HDRI environment map)
│       └── /DistantLight (sun simulation)
├── /Robot
│   ├── /Humanoid (USD reference to robot asset)
│   ├── /Sensors
│   │   ├── /Camera_RGB (1920x1080, 60 FPS)
│   │   ├── /Camera_Depth (640x480, 30 FPS)
│   │   └── /Lidar (360°, 0.1° resolution)
│   └── /ActionGraph (OmniGraph for ROS 2 bridge)
└── /PhysicsScene (gravity: -9.81 m/s², time step: 1/60s)

Isaac ROS VSLAM Pipeline

Hardware-Accelerated Workflow

RealSense D435i → isaac_ros_visual_slam → nav2_map_server → nav2_planner → cmd_vel
                     ↓ (CUDA accelerated)
                  Pose Estimation (30-60 FPS on Jetson Orin)

Key Isaac ROS Nodes

  • visual_slam: isaac_ros_visual_slam (NOT ORB-SLAM2, NOT RTAB-Map)
  • stereo_matching: isaac_ros_ess (Enhanced Semi-Global Matching, GPU-based)
  • object_detection: isaac_ros_dnn_inference (TensorRT optimized)

Performance Expectations (Jetson Orin Nano)

  • VSLAM: 30-60 FPS (vs 5-10 FPS on CPU-based SLAM)
  • Object Detection: 30 FPS (YOLOv5 with TensorRT)
  • Depth Estimation: 30 FPS (ESS model)

Isaac Sim Python API Patterns

Create Scene Programmatically

import omni.isaac.core.utils.stage as stage_utils
from omni.isaac.core.objects import DynamicCuboid

# Add ground plane
stage_utils.add_ground_plane()

# Spawn robot
robot_prim_path = "/World/Robot"
stage_utils.add_reference_to_stage(
    usd_path="omniverse://localhost/NVIDIA/Assets/Isaac/2023.1.1/Unitree/unitree_g1.usd",
    prim_path=robot_prim_path
)

# Add camera sensor
from omni.isaac.sensor import Camera
camera = Camera(prim_path=f"{robot_prim_path}/camera", position=[0, 0, 1.5])
camera.initialize()

Domain Randomization for Sim-to-Real

from omni.isaac.core.utils.stage import randomize_shader_properties

# Randomize material properties every episode
randomize_shader_properties(
    prim_path="/World/Environment",
    parameters=["inputs:roughness", "inputs:metallic"],
    ranges=[(0.1, 0.9), (0.0, 1.0)]
)

RTX VRAM Allocation (Isaac Sim 4.x)

  • Base Sim: 4-6 GB
  • Robot Asset (Humanoid): 1-2 GB
  • Ray Tracing: 3-4 GB
  • Physics: 2-3 GB
  • Total: 10-15 GB → 12GB minimum, 24GB recommended