ONNX Model Zoo

xpuenabler/GR00T-N1.6-3B-SimplerEnv-BEHAVIOR-1K

By xpuenabler · ↓ 0 · ♥ 0 · Updated 2026-03-13

What's in this repo

This page lists the files in xpuenabler/GR00T-N1.6-3B-SimplerEnv-BEHAVIOR-1K, including action_head.onnx, eagle_backbone.onnx and more.

  • 5 ONNX models (.onnx files)
  • 3370 files, including 685 .bias, 174 .weight, 5 .onnx, 5 .py, 3 .0_norm1_norm_1_constant_1_attr__value, 3 .0_norm1_norm_1_constant_attr__value
  • Last updated 2026-03-13

ONNX files

File Size Download
onnx/behavior1k/action_head.onnx — Download
onnx/behavior1k/eagle_backbone.onnx — Download
onnx/bridge/action_head.onnx — Download
onnx/fractal/action_head.onnx — Download
onnx/fractal/eagle_backbone.onnx — Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

GR00T N1.6-3B SimplerEnv & BEHAVIOR-1K (ONNX)

ONNX-converted models of NVIDIA GR00T N1.6-3B for three embodiment configurations: Fractal (Google Robot), Bridge (WidowX), and BEHAVIOR-1K (R1 Pro). These models enable PyTorch-free inference via ONNX Runtime with CUDA execution provider.

Base PyTorch Models

Variant HuggingFace Source Embodiment Tag Embodiment ID
Fractal nvidia/GR00T-N1.6-fractal OXE_GOOGLE 0
Bridge nvidia/GR00T-N1.6-bridge OXE_WIDOWX 1
BEHAVIOR-1K nvidia/GR00T-N1.6-BEHAVIOR1k BEHAVIOR_R1_PRO 24

Model Architecture

The GR00T N1.6 model is split into two ONNX components:

  1. Eagle Backbone (eagle_backbone.onnx): Vision encoder (EagleX) that processes images into visual tokens
  2. Action Head (action_head.onnx): DiT-based diffusion transformer that generates action sequences

Key Dimensions

Parameter Value
SEQ_LEN 108
BACKBONE_DIM 2048
ACTION_DIM 128
ACTION_HORIZON 50
STATE_DIM 128

Directory Structure

onnx/
  fractal/
    eagle_backbone.onnx          # Vision backbone
    action_head.onnx             # Action head (DiT)
    <external_data_files>        # ~1200 weight tensor files
  bridge/
    action_head.onnx             # Bridge-specific action head
    <external_data_files>        # ~1000 weight tensor files
    # NOTE: Bridge uses the SAME eagle_backbone as Fractal.
    #       Download eagle_backbone.onnx + its external data from onnx/fractal/
  behavior1k/
    eagle_backbone.onnx          # Behavior1k backbone
    action_head.onnx             # Behavior1k action head
    <external_data_files>        # ~1100 weight tensor files
scripts/
  convert_to_onnx.py             # Conversion script (fractal/bridge)
  convert_behavior1k.py          # Conversion script (behavior1k)
  run_gr00t_onnx_server.py       # ZMQ ONNX inference server
  validate_onnx.py               # Cosine similarity validation
  run_simplerenv_onnx_eval.sh    # SimplerEnv evaluation (fractal + bridge)
  run_behavior1k_eval.sh         # BEHAVIOR-1K evaluation client
  run_behavior1k_thirdperson.py  # Third-person view rendering (Python)
  run_behavior1k_thirdperson.sh  # Third-person view rendering (launcher)

Bridge backbone note: The Bridge variant shares the same Eagle backbone as Fractal. When using Bridge, point to onnx/fractal/eagle_backbone.onnx or copy the fractal backbone files into the bridge directory.

ONNX Conversion Details

  • Opset version: 17
  • Precision: float32
  • Batch size: Static batch_size=1 (no dynamic axes)
  • Key modification: ScatterND eliminated via FixedEmbodimentLinear — replaces the dynamic embodiment selection (nn.Embedding + ScatterND) with a fixed linear layer for the target embodiment, enabling static graph export
  • External data: Weights stored as external data files (ONNX size_threshold=0) due to model size exceeding 2GB protobuf limit

Conversion Command (Fractal/Bridge)

PYTHONPATH="" python scripts/convert_to_onnx.py \
    --model-path nvidia/GR00T-N1.6-fractal \
    --embodiment-tag OXE_GOOGLE \
    --output-dir ./onnx/fractal

Conversion Command (BEHAVIOR-1K)

PYTHONPATH="" python scripts/convert_behavior1k.py \
    --model-path nvidia/GR00T-N1.6-BEHAVIOR1k \
    --embodiment-tag BEHAVIOR_R1_PRO \
    --output-dir ./onnx/behavior1k

Inference Usage

ONNX Inference Server (ZMQ)

The inference server loads ONNX models and serves action predictions over ZMQ (port 5555):

# Fractal (Google Robot)
PYTHONPATH="" CUDA_VISIBLE_DEVICES=0 python scripts/run_gr00t_onnx_server.py \
    --model-path nvidia/GR00T-N1.6-fractal \
    --embodiment-tag OXE_GOOGLE \
    --onnx-dir ./onnx/fractal \
    --port 5555

# Bridge (WidowX) — uses fractal backbone
PYTHONPATH="" CUDA_VISIBLE_DEVICES=0 python scripts/run_gr00t_onnx_server.py \
    --model-path nvidia/GR00T-N1.6-bridge \
    --embodiment-tag OXE_WIDOWX \
    --onnx-dir ./onnx/bridge \
    --backbone-onnx-dir ./onnx/fractal \
    --port 5555

# BEHAVIOR-1K
PYTHONPATH="" CUDA_VISIBLE_DEVICES=0 python scripts/run_gr00t_onnx_server.py \
    --model-path nvidia/GR00T-N1.6-BEHAVIOR1k \
    --embodiment-tag BEHAVIOR_R1_PRO \
    --onnx-dir ./onnx/behavior1k \
    --port 5555

SimplerEnv Evaluation Client

Connect a SimplerEnv evaluation script to the running ONNX server:

python simpler_env_eval.py \
    --task google_robot_pick_coke_can \
    --policy gr00t_zmq \
    --zmq-host localhost \
    --zmq-port 5555

Simulation Evaluation

SimplerEnv Evaluation (PyTorch + ONNX)

SimplerEnv evaluation runs as a two-process pipeline: a server (loads the model) and a client (runs the simulation).

Step 1: Start the inference server

PyTorch server (original GR00T pipeline):

# Fractal (Google Robot) on GPU 0, port 5556
PYTHONPATH="" CUDA_VISIBLE_DEVICES=0 python gr00t/eval/run_gr00t_server.py \
    --model_path nvidia/GR00T-N1.6-fractal \
    --embodiment_tag OXE_GOOGLE \
    --port 5556

# Bridge (WidowX) on GPU 0, port 5557
PYTHONPATH="" CUDA_VISIBLE_DEVICES=0 python gr00t/eval/run_gr00t_server.py \
    --model_path nvidia/GR00T-N1.6-bridge \
    --embodiment_tag OXE_WIDOWX \
    --port 5557

ONNX server (PyTorch-free, uses scripts/run_gr00t_onnx_server.py):

# Fractal on GPU 0, port 5556
PYTHONPATH="" CUDA_VISIBLE_DEVICES=0 python scripts/run_gr00t_onnx_server.py \
    --model-path nvidia/GR00T-N1.6-fractal \
    --embodiment-tag OXE_GOOGLE \
    --onnx-dir ./onnx/fractal \
    --port 5556

# Bridge on GPU 0, port 5557
PYTHONPATH="" CUDA_VISIBLE_DEVICES=0 python scripts/run_gr00t_onnx_server.py \
    --model-path nvidia/GR00T-N1.6-bridge \
    --embodiment-tag OXE_WIDOWX \
    --onnx-dir ./onnx/bridge \
    --backbone-onnx-dir ./onnx/fractal \
    --port 5557
Step 2: Run SimplerEnv evaluation

Once the server is ready, run the evaluation client:

bash scripts/run_simplerenv_onnx_eval.sh

This script runs all Fractal (Google Robot) tasks against port 5556 and collects results + videos. See the script for customizing tasks, episode counts, and output paths.

PYTHONPATH note: The SimplerEnv eval script explicitly sets PYTHONPATH="" and PYTHONNOUSERSITE=1 to avoid conflicts between the SimplerEnv virtualenv and the Isaac-GR00T environment. The SimplerEnv Python binary is invoked directly from its own .venv.

BEHAVIOR-1K Evaluation

BEHAVIOR-1K evaluation requires OmniGibson and the BEHAVIOR-1K environment installed separately (not bundled in this repo). The server and client run on different GPUs to avoid VRAM conflicts.

Prerequisites
Step 1: Start GR00T inference server (GPU 0)
PYTHONPATH="" CUDA_VISIBLE_DEVICES=0 python gr00t/eval/run_gr00t_server.py \
    --model_path nvidia/GR00T-N1.6-BEHAVIOR1k \
    --embodiment_tag BEHAVIOR_R1_PRO \
    --port 5555 \
    --use_sim_policy_wrapper
Step 2: Run BEHAVIOR-1K eval client (GPU 1)
bash scripts/run_behavior1k_eval.sh

The script runs 5 tasks × 5 episodes each against the server on port 5555.

Required environment variables (already set in the script):

export PYTHONPATH=/path/to/Isaac-GR00T
export PYTHONNOUSERSITE=1
export OMNI_KIT_ACCEPT_EULA=YES
export OMNIGIBSON_HEADLESS=1    # Run without display (headless)

Tasks evaluated:

  • turning_on_radio
  • picking_up_trash
  • picking_up_toys
  • putting_shoes_on_rack
  • clean_up_your_desk

Note: OmniGibson may produce a segfault on cleanup — this is expected behavior and does not affect results. The script does not use set -e for this reason.

BEHAVIOR-1K Third-Person View Rendering

For visualization, a third-person camera can be added that tracks the robot and produces a high-resolution 1280×720 video using Path Tracing with the OptiX denoiser.

Quick start (automated launcher)
bash scripts/run_behavior1k_thirdperson.sh

This script automatically:

  1. Starts the GR00T inference server on GPU 0 (port 5555)
  2. Waits for server readiness
  3. Runs one episode of picking_up_trash with third-person rendering on GPU 1
  4. Outputs videos to /path/to/gr00t_behavior1k_thirdperson_videos/
Manual usage
# Terminal 1 — Start GR00T inference server (GPU 0)
PYTHONPATH="" CUDA_VISIBLE_DEVICES=0 python gr00t/eval/run_gr00t_server.py \
    --model_path nvidia/GR00T-N1.6-BEHAVIOR1k \
    --embodiment_tag BEHAVIOR_R1_PRO \
    --port 5555 \
    --use_sim_policy_wrapper

# Terminal 2 — Run third-person rendering client (GPU 1)
PYTHONPATH=/path/to/Isaac-GR00T \
PYTHONNOUSERSITE=1 \
OMNIGIBSON_HEADLESS=1 \
OMNI_KIT_ACCEPT_EULA=YES \
CUDA_VISIBLE_DEVICES=1 \
python scripts/run_behavior1k_thirdperson.py \
    --task picking_up_trash \
    --n_episodes 1 \
    --policy_client_host 127.0.0.1 \
    --policy_client_port 5555 \
    --output_dir ./thirdperson_videos \
    --max_episode_steps 720 \
    --n_action_steps 8 \
    --n_render_iterations 20 \
    --spp 1
Key renderer settings (noise-free rendering on H100)

The run_behavior1k_thirdperson.py script configures the RTX renderer to eliminate noise artifacts:

Setting Value Why
Render mode Path Tracing (mode=2) Higher quality than RTX Real-Time
optixDenoiser/enabled True Enable OptiX denoiser
optixDenoiser/blendFactor 0.0 Critical: default is 1.0 (= no denoising!)
optixDenoiser/temporalMode/enabled True Prevents flickering between video frames
pathtracing/clampSpp 0 Critical: default clamps SPP to 32!
pathtracing/spp 1 (fast) or 128 (quality) Samples per pixel per render call
n_render_iterations 20 (fast) or 5 (with high SPP) Extra render passes per frame
adaptiveSampling/enabled True Concentrates samples on noisy regions
fireflyFilter/enabled True Clamps bright noise speckles

Recommended settings for H100: --spp 1 --n_render_iterations 20 (fast, low memory) or --spp 128 --n_render_iterations 5 (higher quality, slower). The default blendFactor=1.0 bug means denoising is silently disabled unless explicitly set to 0.0.

Output

Two video streams are recorded per episode:

  • Third-person view (thirdperson/thirdperson_<uuid>_s{0,1}.mp4): 1280×720, follows robot with configurable offset
  • Robot view (robotview/): Standard wrist/head camera view from observation dict

The filename suffix _s1.mp4 indicates a successful episode; _s0.mp4 indicates failure.

Validation Results

Cosine similarity between PyTorch and ONNX outputs (higher is better, 1.0 = identical):

Model Backbone Cosine Sim Action Head Cosine Sim
Fractal 1.000000 1.000000
Bridge 1.000000 1.000000
BEHAVIOR-1K 1.000000 1.000000

SimplerEnv Evaluation Results

Fractal (Google Robot)

Task PyTorch ONNX
pick_coke_can 100% 100%
pick_object 100% 100%
move_near 100% 100%
open_drawer partial 0%
close_drawer 50% 20%
place_in_closed_drawer 0% 0%

Bridge (WidowX)

Task PyTorch ONNX
spoon_on_towel 100% 0%
carrot_on_plate 100% 0%
put_eggplant_in_basket 100% 0%
stack_cube 0% 0%
put_eggplant_in_sink 72.7% 0%
close_drawer 100% 20%
open_drawer 100% 0%

BEHAVIOR-1K

Task PyTorch
turning_on_radio 0%
picking_up_trash 0%
picking_up_toys 0%
putting_shoes_on_rack 0%
clean_up_your_desk 0%

Note: BEHAVIOR-1K tasks show 0% for both PyTorch and ONNX, indicating these tasks are inherently challenging for the base model in the SimplerEnv simulation environment.

Requirements

  • onnxruntime-gpu>=1.17.0
  • numpy
  • pyzmq (for inference server)
  • Pillow (for image processing)
  • CUDA-capable GPU with sufficient VRAM (~8GB)

License

This repository contains ONNX-converted models derived from NVIDIA's GR00T N1.6 under the Apache 2.0 License.

Model info

robotics
onnx robotics gr00t nvidia simpler-env behavior-1k license:apache-2.0 region:us
↓ 0 ♥ 0

Created: 2026-03-13

Updated: 2026-03-13

View on Hugging Face

Files (3370)

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onnx/fractal/onnx__MatMul_4933
onnx/fractal/onnx__MatMul_4934
onnx/fractal/onnx__MatMul_4935
onnx/fractal/onnx__MatMul_4936
onnx/fractal/onnx__MatMul_4937
onnx/fractal/onnx__MatMul_4938
onnx/fractal/onnx__MatMul_4939
onnx/fractal/onnx__MatMul_4952
onnx/fractal/onnx__MatMul_4953
onnx/fractal/onnx__MatMul_4955
onnx/fractal/onnx__MatMul_4956
onnx/fractal/onnx__MatMul_5004
onnx/fractal/onnx__MatMul_5005
onnx/fractal/onnx__MatMul_5006
onnx/fractal/onnx__MatMul_5007
onnx/fractal/onnx__MatMul_5008
onnx/fractal/onnx__MatMul_5009
onnx/fractal/onnx__MatMul_5010
onnx/fractal/onnx__MatMul_5011
onnx/fractal/onnx__MatMul_5024
onnx/fractal/onnx__MatMul_5025
onnx/fractal/onnx__MatMul_5027
onnx/fractal/onnx__MatMul_5028
onnx/fractal/onnx__MatMul_5076
onnx/fractal/onnx__MatMul_5077
onnx/fractal/onnx__MatMul_5078
onnx/fractal/onnx__MatMul_5079
onnx/fractal/onnx__MatMul_5080
onnx/fractal/onnx__MatMul_5081
onnx/fractal/onnx__MatMul_5082
onnx/fractal/onnx__MatMul_5083
onnx/fractal/onnx__MatMul_5096
onnx/fractal/onnx__MatMul_5097
onnx/fractal/onnx__MatMul_5099
onnx/fractal/onnx__MatMul_5100
onnx/fractal/onnx__MatMul_5148
onnx/fractal/onnx__MatMul_5149
onnx/fractal/onnx__MatMul_5150
onnx/fractal/onnx__MatMul_5151
onnx/fractal/onnx__MatMul_5152
onnx/fractal/onnx__MatMul_5153
onnx/fractal/onnx__MatMul_5154
onnx/fractal/onnx__MatMul_5155
onnx/fractal/onnx__MatMul_5168
onnx/fractal/onnx__MatMul_5169
onnx/fractal/onnx__MatMul_5171
onnx/fractal/onnx__MatMul_5172
onnx/fractal/onnx__MatMul_5220
onnx/fractal/onnx__MatMul_5221
onnx/fractal/onnx__MatMul_5222
onnx/fractal/onnx__MatMul_5223
onnx/fractal/onnx__MatMul_5224
onnx/fractal/onnx__MatMul_5225
onnx/fractal/onnx__MatMul_5226
onnx/fractal/onnx__MatMul_5227
onnx/fractal/onnx__MatMul_5240
onnx/fractal/onnx__MatMul_5241
onnx/fractal/onnx__MatMul_5243
onnx/fractal/onnx__MatMul_5244
onnx/fractal/onnx__MatMul_5292
onnx/fractal/onnx__MatMul_5293
onnx/fractal/onnx__MatMul_5294
onnx/fractal/onnx__MatMul_5295
onnx/fractal/onnx__MatMul_5296
onnx/fractal/onnx__MatMul_5297
onnx/fractal/onnx__MatMul_5298
onnx/fractal/onnx__MatMul_5299
onnx/fractal/onnx__MatMul_5312
onnx/fractal/onnx__MatMul_5313
onnx/fractal/onnx__MatMul_5315
onnx/fractal/onnx__MatMul_5316
onnx/fractal/onnx__MatMul_5364
onnx/fractal/onnx__MatMul_5365
onnx/fractal/onnx__MatMul_5366
onnx/fractal/onnx__MatMul_5367
onnx/fractal/onnx__MatMul_5368
onnx/fractal/onnx__MatMul_5369
onnx/fractal/onnx__MatMul_5370
onnx/fractal/onnx__MatMul_5371
onnx/fractal/onnx__MatMul_5384
onnx/fractal/onnx__MatMul_5385
onnx/fractal/onnx__MatMul_5387
onnx/fractal/onnx__MatMul_5388
onnx/fractal/onnx__MatMul_5436
onnx/fractal/onnx__MatMul_5437
onnx/fractal/onnx__MatMul_5438
onnx/fractal/onnx__MatMul_5439
onnx/fractal/onnx__MatMul_5440
onnx/fractal/onnx__MatMul_5441
onnx/fractal/onnx__MatMul_5442
onnx/fractal/vlln.bias
onnx/fractal/vlln.weight
scripts/convert_behavior1k.py
scripts/convert_to_onnx.py
scripts/run_behavior1k_eval.sh
scripts/run_behavior1k_thirdperson.py
scripts/run_behavior1k_thirdperson.sh
scripts/run_gr00t_onnx_server.py
scripts/run_simplerenv_onnx_eval.sh
scripts/validate_onnx.py

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