Title: GR00T N1.5 — Finetuned on SO101 Table Cleanup
This is a finetuned GR00T N1.5 policy trained using LeRobot on the SO101 task: so101-table-cleanup.
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📦 Model Details
- Model Type: GR00T N1.5 (Diffusion Policy)
- Backbone: NVEagle/eagle_er-qwen3_1_7B-Siglip2_400M_stage1_5_128gpu_er_v7_1mlp_nops
- Action Horizon: 16 steps
- Action Dim: 32
- Input Embedding Dim: 1536
- Vision Tokens: 32
- Checkpoint Steps: 20000
- Format: safetensors (sharded)
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📁 Files
- config.json --> Model and backbone configuration
- model-00001-of-00002.safetensors
- model-00002-of-00002.safetensors
- model.safetensors.index.json --> Index for sharded weights
- training_args.bin --> Hugging Face Trainer arguments
- trainer_state.json --> Trainer state for resuming
- README.md --> This file
- experiment_cfg/ --> (optional) extra configs used during training
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🧠 Intended Use
This model is designed for manipulation in table cleanup tasks using 5 camera views and a GR00T diffusion policy. It is trained on the SO101 dataset and can be used for inference or further fine-tuning.
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🧪 How to Use (Python)
Load with Hugging Face Transformers
from transformers import AutoModel, AutoConfig
model_id = "tshiamor-none/so101-gr00t-n1_5"
config = AutoConfig.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id, config=config)
You can now use model(...) with the correct input dictionary
Alternatively, in LeRobot:
python scripts/load_dataset.py
--dataset-path ./demo_data/so101-table-cleanup
--model-path tshiamor-none/so101-gr00t-n1_5
--plot-state-action
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🔁 Resume Training
To resume training from step 10000 to 20000:
python scripts/gr00t_finetune.py
--dataset-path ./demo_data/so101-table-cleanup
--output-dir ./so101-checkpoints
--num-gpus 1
--max-steps 20000
--batch-size 1
--resume-from-checkpoint ./so101-checkpoints/checkpoint-10000
--data-config so101_5cam
--video-backend torchvision_av
--dataloader-num-workers 0
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🔒 License
MIT License
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✍️ Citation
If you use this model, consider citing:
@misc{lerobot2024,
title={LeRobot: Open Foundation Models for Robotics},
author={Hugging Face et al.},
year={2024},
url={
https://huggingface.co/lerobot}
}
