GXKNOTIX β€” End-to-End NVIDIA Physical AI Flower-Knotting Workcell

A high-performance robotics automation system built on the complete NVIDIA Physical AI stack, combining NVIDIA Omniverse, Isaac Lab 3.0, Newton Physics, and NVIDIA TensorRT for sim-to-real robotic flower knotting.

Hugging Face Space Hugging Face Model


Architecture: Complete NVIDIA Physical AI Pipeline

 β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
 β”‚       1. SIMULATION & TRAINING (Isaac Lab + Newton)    β”‚
 β”‚  β€’ Multiphysics: Franka arms + Newton Cosserat rod     β”‚
 β”‚  β€’ Domain Randomization: thread elasticity, friction   β”‚
 β”‚  β€’ Environment: simulation/gxknotix_knotting_rl_env.py β”‚
 β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                             β”‚
                             β–Ό Export & Optimize
 β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
 β”‚       2. MODEL EXPORT & COMPILATION (TensorRT)         β”‚
 β”‚  β€’ ONNX Export: simulation/export_policy_tensorrt.py   β”‚
 β”‚  β€’ Output: gxknotix_policy.onnx -> policy.engine (FP16)β”‚
 β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                             β”‚
                             β–Ό Deploy to Edge Hardware
 β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
 β”‚       3. REAL-WORLD EDGE EXECUTION (TensorRT / ROS)    β”‚
 β”‚  β€’ Hardware Bus: deployment/hardware_interface.py      β”‚
 β”‚  β€’ 60 Hz Closed-Loop: deployment/nvidia_physical_ai_runner.py β”‚
 β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Traditional Knotting Process (14-Step Canonical Guide)

The workcell kinematics faithfully execute the 14-step opposite-direction jasmine knotting technique (reference: docs/final_guide.png):

  1. Thread Preparation: Thread held under 2-3 N tension between tensioner and indexer.
  2. Bud Pick: LeftPicker & RightPicker pick fresh Kundumalli buds from dedicated feed trays.
  3. Opposite-Direction Nesting: Bud A placed tip-up (+Y), Bud B inverted 180Β° tip-down (-Y). Stems touching with zero gap; thread passes over the center.
  4. Wrap: Spinner rotor carries thread around both stems together.
  5. Loop: Billhook mechanism opens jaw, forming a locking loop around the stems.
  6. Pull Through & Cinch: Jaw closes on thread, pulls through loop, tensioner cinches tight.
  7. Slide & Index: Pitch indexer advances the garland +9 mm, accumulating a dense double-sided floral rope.

Quick Start: One-Click Launchers

You can start and stop the entire workcell directly using the provided Windows batch scripts:

  • Launch: Double-click start.bat or run:

    start.bat
    

    (Presents a quick menu to launch with Omniverse Kit visualizer, headless verification, or edge runner).

  • Stop: Double-click stop.bat or run:

    stop.bat
    

    (Terminates all running Omniverse Kit, Isaac Lab, and policy runner background processes).


1. Newton Physics Digital Twin Simulation

Run the photorealistic 3D workcell in Omniverse Kit:

Set-Location "C:\Users\thiru\gx-workspace\gx-knotix\IsaacLab"
uv run isaaclab -p ..\simulation\gxknotix_knotting_workcell.py --viz kit

Headless / verification mode:

uv run isaaclab -p ..\simulation\gxknotix_knotting_workcell.py --num_steps 100

2. Policy Export to NVIDIA TensorRT

Export the trained PyTorch policy to ONNX and compile to TensorRT:

Set-Location "C:\Users\thiru\gx-workspace\gx-knotix\IsaacLab"
uv run python ..\simulation\export_policy_tensorrt.py --output_onnx ..\simulation\gxknotix_policy.onnx

On your edge target (Jetson Orin or RTX edge GPU), build the FP16 TensorRT engine:

trtexec --onnx=gxknotix_policy.onnx --saveEngine=gxknotix_policy.engine --fp16

3. Real-World Edge Deployment

Execute the sub-2ms TensorRT policy runner driving the physical actuators at 60 Hz:

Set-Location "C:\Users\thiru\gx-workspace\gx-knotix"
python deployment\nvidia_physical_ai_runner.py --model simulation\gxknotix_policy.onnx --hz 60.0

Directory Structure

gx-knotix/
β”œβ”€β”€ start.bat                                   # One-click interactive workcell launcher
β”œβ”€β”€ stop.bat                                    # One-click process terminator
β”œβ”€β”€ assets/
β”‚   └── thread_spool.usd                        # Procedural OpenUSD thread spool asset
β”œβ”€β”€ simulation/
β”‚   β”œβ”€β”€ gxknotix_knotting_workcell.py           # Interactive Newton multiphysics workcell
β”‚   β”œβ”€β”€ gxknotix_knotting_rl_env.py             # Isaac Lab RL task environment & MDP
β”‚   β”œβ”€β”€ export_policy_tensorrt.py               # ONNX & TensorRT policy compiler
β”‚   β”œβ”€β”€ gxknotix_policy.onnx                    # Exported neural policy
β”‚   └── README.md
β”œβ”€β”€ deployment/
β”‚   β”œβ”€β”€ nvidia_physical_ai_runner.py            # Closed-loop TensorRT policy runner (60 Hz)
β”‚   └── hardware_interface.py                   # Real-time hardware bus interface
β”œβ”€β”€ docs/
β”‚   └── final_guide.png                         # 14-step canonical opposite-direction knotting guide
β”œβ”€β”€ .gitignore
β”œβ”€β”€ README.md
└── LICENSE
Downloads last month
26
Video Preview
loading

Space using thirumalaivasank/gx-knotix 1