README.md

---
library_name: lerobot
pipeline_tag: robotics
base_model: nvidia/GR00T-N1.5-3B
base_model_relation: finetune
datasets:
- CoRL2026-CSI/Isaaclab-so101_11task_baseCaP_3300epi
license: other
license_name: nvidia-license
license_link: https://developer.download.nvidia.com/licenses/NVIDIA-OneWay-Noncommercial-License-22Mar2022.pdf
tags:
- robotics
- lerobot
- groot
- gr00t-n1.5
- so101
- imitation-learning
- flow-matching
- safetensors
---

# CoRL2026-CSI/IsaacLab-so101-multi-gr00t

This is a LeRobot GR00T N1.5 policy fine-tuned from
[`nvidia/GR00T-N1.5-3B`](https://huggingface.co/nvidia/GR00T-N1.5-3B) on
[`CoRL2026-CSI/Isaaclab-so101_11task_baseCaP_3300epi`](https://huggingface.co/datasets/CoRL2026-CSI/Isaaclab-so101_11task_baseCaP_3300epi).

The model is intended for SO-101 style manipulation experiments using RGB observations,
robot proprioception, language instructions, and continuous action chunks. It is uploaded
as a LeRobot policy checkpoint and should be loaded through the matching LeRobot GR00T
implementation used for training.

## Model Details

- **Policy type:** GR00T N1.5
- **Base model:** `nvidia/GR00T-N1.5-3B`
- **Tokenizer assets:** `lerobot/eagle2hg-processor-groot-n1p5`
- **Embodiment tag:** `new_embodiment`
- **Observation steps:** `1`
- **Action chunk size:** `16`
- **Action steps:** `16`
- **Max state/action dims:** `64` / `32`

## Fine-Tuning Setup

- **Training steps:** `110000`
- **Approx. epochs:** `7.99`
- **Final training samples:** `28160000`
- **Final training loss:** `0.010196`
- **Runtime:** `76.72 hours`
- **Micro batch size:** `64`
- **Gradient accumulation steps:** `2`
- **Effective batch size:** `256`
- **Optimizer:** `adamw`, lr `0.0001`, weight decay `1e-05`
- **Scheduler:** `cosine_decay_with_warmup`
- **Mixed precision:** bf16 policy weights/config enabled: `true`
- **VLM/LLM backbone fine-tuned:** `false`
- **Vision tower fine-tuned:** `false`
- **Action projector fine-tuned:** `true`
- **Action DiT fine-tuned:** `true`

The GR00T vision-language backbone was frozen for this run. The action head projector
and flow-matching diffusion transformer were fine-tuned.

## Inputs

- `observation.ee_pos.robot_xyzrpy`: `STATE`, shape `[6]`
- `observation.gripper_binary`: `STATE`, shape `[1]`
- `observation.images.left_wrist`: `VISUAL`, shape `[3, 480, 640]`
- `observation.images.top`: `VISUAL`, shape `[3, 480, 640]`
- `observation.state`: `STATE`, shape `[6]`
- `observation.state.radian_urdf0`: `STATE`, shape `[6]`

## Outputs

- `action`: `ACTION`, shape `[6]`
- `action.radian_urdf0`: `ACTION`, shape `[6]`

## Usage

Install and use the same LeRobot checkout/environment that contains the GR00T policy
implementation, then point `policy.path` to this Hub repo.

```bash
lerobot-record \
  --robot.type=<your_robot> \
  --dataset.repo_id=<your_eval_dataset_repo> \
  --policy.path=CoRL2026-CSI/IsaacLab-so101-multi-gr00t \
  --episodes=10
```

For local Python usage, load the policy with LeRobot's policy factory or GR00T policy
loader from the training checkout.

## Evaluation

This upload records the offline training run metrics only. No rollout success rate is
claimed here unless a separate real/sim evaluation is added later.

Final logged training metrics:

- loss: `0.010196`
- grad norm: `0.193945`
- update time: `1.2521 s/step`
- dataloading time: `0.0046 s/step`

## Limitations and Safety

This model is a robot control policy and can produce unsafe actions if deployed on
hardware without appropriate validation, workspace limits, emergency stop handling, and
task-specific safety checks. Test in simulation or a constrained setup before any
physical deployment.

The model is specialized to the training dataset and embodiment configuration. It may
not transfer reliably to different cameras, calibration, action spaces, robot hardware,
or tasks without further validation or fine-tuning.

## License and Terms

This model is a fine-tune of `nvidia/GR00T-N1.5-3B`; users are responsible for complying
with the NVIDIA model license and any dataset/license constraints. See the base model
card and NVIDIA license terms linked in the metadata.

## Files

- `model.safetensors`: fine-tuned policy weights
- `config.json`: LeRobot GR00T policy config
- `train_config.json`: training configuration
- `policy_preprocessor.json` and `policy_postprocessor.json`: LeRobot processor pipelines
- `policy_*_step_*.safetensors`: normalization/statistics state used by processors