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project_chiron
ilessio-aiflowlab/project_chiron
project_chiron is a robotics model from ilessio-aiflowlab. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as apache-2.0.
CRISP-Compliant ROS2 VLA Controllers — Production-grade robot control stack bridging Vision-Language-Action (VLA) models to physical manipulators via real-time compliant controllers.
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Public
Updated Apr 1, 2026
Repo size
8.9 MB
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Repo makeup
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.pth4.3 MB · 49%
At a glance
- Task
- Robotics
- Library
- pytorch
- License
- apache-2.0
- Access
- Public
- Created
- Mar 28, 2026
- Updated
- Apr 1, 2026
- SHA
- 7ba2ca5a
Model card
From the Hugging Face model README
CHIRON — ANIMA Module
CRISP-Compliant ROS2 VLA Controllers — Production-grade robot control stack bridging Vision-Language-Action (VLA) models to physical manipulators via real-time compliant controllers.
Part of the ANIMA Perception Suite by Robot Flow Labs.
Overview
CHIRON is an orchestration layer for robot manipulation. It provides:
- Cartesian impedance, operational space, joint impedance, and hybrid force-position controllers
- VLA policy bridge for deploying learned policies to real hardware
- Safety monitoring with velocity, torque, workspace, and singularity checks
- Support for Franka FR3, KUKA IIWA14, and Kinova Gen3 manipulators
The policy network (ResidualMLP) maps robot state (8D) to action commands (8D), trained on the DROID 1.0.1 dataset (25.4M frames, 95K episodes).
Architecture
- Model: ResidualMLP (input projection → 2 residual blocks → output projection)
- State: 8D (7 joint positions + gripper)
- Action: 8D (7 joint commands + gripper)
- Parameters: 269,064
- Hidden: [256, 256, 128] with LayerNorm + GELU + Dropout(0.1)
Exported Formats
| Format | File | Size | Use Case |
|---|---|---|---|
| PyTorch (.pth) | pytorch/project_chiron_v2.pth | 1.1MB | Training, fine-tuning |
| SafeTensors | pytorch/project_chiron_v2.safetensors | 1.1MB | Fast loading, safe |
| ONNX | onnx/project_chiron_v2.onnx | 1.1MB | Cross-platform inference |
| TensorRT FP32 | tensorrt/project_chiron_v2_fp32.trt | 1.2MB | Full precision (L4/A6000) |
| TensorRT FP16 | tensorrt/project_chiron_v2_fp16.trt | 1.2MB | Edge deployment (Jetson) |
Usage
import torch
from safetensors.torch import load_file
# Load model
state_dict = load_file("pytorch/project_chiron_v2.safetensors")
# Model: Linear(8, 256) → ResidualBlock×2 → Linear(256, 8)
# Or ONNX
import onnxruntime as ort
sess = ort.InferenceSession("onnx/project_chiron_v2.onnx")
action = sess.run(None, {"state": robot_state})[0]
Training
| Parameter | Value |
|---|---|
| Dataset | DROID 1.0.1 (25.4M frames, 95K episodes) |
| Hardware | NVIDIA L4 (23GB VRAM) |
| Precision | bf16 mixed |
| Optimizer | AdamW (lr=3e-4, wd=0.01) |
| Scheduler | Cosine with 5% warmup |
| Batch size | 2048 |
| Epochs | 30 (early stopped, patience=20) |
| Best val_loss | 0.0041 |
| Training time | 44 minutes |
| Seed | 42 |
See configs/train_droid.yaml for full config and logs/training_history.json for loss curves.
Simulation
CHIRON includes MuJoCo simulation backends for:
- Franka FR3 (7-DOF)
- KUKA IIWA14 (7-DOF)
- Kinova Gen3 (7-DOF)
License
Apache 2.0 — Robot Flow Labs / AIFLOW LABS LIMITED
- Task
- Robotics
- Library
- pytorch
- License
- apache-2.0
- Created
- Mar 28, 2026
- Updated
- Apr 1, 2026