Fine-Tuning the GR00T N1.6-3B for Precision Actuation

The Goal: From "Generalist" to "Specialist" ​While the base GR00T N1.6-3B model is a powerful Vision-Language-Action (VLA) foundation, it is trained on diverse humanoid data that doesn't always account for the specific torque curves of NEMA 17 steppers. To achieve sub-millimeter precision in our pallet-handling tasks, we must perform a targeted fine-tuning run using a custom dataset collected from our own hardware. Dataset Preparation: The "Aura-Collect" Method ​High-quality demonstrations are the lifeblood of fine-tuning. For Project Aura, we collected 40 high-fidelity "Success" trajectories. ​Demonstration Quality: We avoided jerky movements and long pauses, as the model will learn those inefficiencies as intentional behaviors. ​Modality Mapping: We updated our modality.json to map the Pi 5's camera stream to the observation.images.main key, ensuring the model's visual transformer identifies the pallet correctly. . Technical Implementation: LoRA Fine-Tuning ​To run this on a single GPU node (like an L40 or H100 in the cloud), we utilize Low-Rank Adaptation (LoRA). This allows us to freeze the original 3B parameters and only train a small "adapter" layer (roughly 0.5% of the total weights), drastically reducing VRAM requirements

Post 25: LoRA Fine-Tuning for GR00T-N1.6

To optimize the Aura Sentinel for specific pallet-handling tasks, we utilize Low-Rank Adaptation (LoRA) with a rank of 16. This allows for precision training without the high compute cost of full-parameter tuning.

# aura_finetune_config.py
from gr00t.experiment.trainer import Gr00tTrainer

# Initialize the trainer with LoRA rank 16
trainer = Gr00tTrainer(
    model_name="nvidia/GR00T-N1.6-3B",
    dataset_path="./demo_data/aura_pallet_v1",
    output_dir="./checkpoints/aura_precision_v1",
    lora_rank=16,
    batch_size=16,
    max_steps=5000
)

# Begin the post-training flight
trainer.train()

Note: Batch size is optimized for 24GB VRAM environments during the simulation phase.