README.md

---
library_name: lerobot
pipeline_tag: robotics
base_model: lerobot/smolvla_base
base_model_relation: finetune
datasets:
- CoRL2026-CSI/UR7e-CaP_arrange_block_100epi_10fps
license: other
tags:
- lerobot
- smolvla
- vision-language-action
- imitation-learning
- robotics
- ur7e
- arrange-block
- 10fps
- safetensors
---

# CoRL2026-CSI/SmolVLA-UR7e-ArrangeBlock_50epoch

This is a LeRobot SmolVLA policy fine-tuned from
[`lerobot/smolvla_base`](https://huggingface.co/lerobot/smolvla_base) on
[`CoRL2026-CSI/UR7e-CaP_arrange_block_100epi_10fps`](https://huggingface.co/datasets/CoRL2026-CSI/UR7e-CaP_arrange_block_100epi_10fps).

The model is intended for UR7e ArrangeBlock 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 SmolVLA implementation used for training.

## Model Details

- **Policy type:** SmolVLA
- **Base policy:** `lerobot/smolvla_base`
- **Vision-language model:** `HuggingFaceTB/SmolVLM2-500M-Video-Instruct`
- **Action chunk size:** `50`
- **Action steps:** `50`
- **Max state/action dims:** `32` / `32`
- **Vision encoder frozen:** `true`
- **Train expert only:** `true`
- **Train state projection:** `true`

## Fine-Tuning Setup

- **Dataset:** `CoRL2026-CSI/UR7e-CaP_arrange_block_100epi_10fps`
- **Training steps:** `9250`
- **Approx. epochs:** `50.26`
- **Final training samples:** `2368000`
- **Final training loss:** `0.010289`
- **Runtime:** `6.12 hours`
- **Per-GPU batch size:** `128`
- **Gradient accumulation steps:** `1`
- **Number of GPUs:** `2`
- **Effective batch size:** `256`
- **Optimizer lr:** `0.0001`
- **Optimizer betas:** `[0.9, 0.95]`
- **Weight decay:** `1e-10`
- **Scheduler warmup/decay:** `1000` / `30000`
- **Final decay lr:** `2.5e-06`
- **DataLoader workers:** `8`
- **DataLoader prefetch factor:** `1`

## Camera Mapping

- `observation.images.realsense_topview` -> `observation.images.camera2`
- `observation.images.realsense_wrist` -> `observation.images.camera1`

## Image Augmentation

- `affine`: `RandomAffine` `{'degrees': [-5.0, 5.0], 'translate': [0.05, 0.05]}`
- `brightness`: `ColorJitter` `{'brightness': [0.8, 1.2]}`
- `contrast`: `ColorJitter` `{'contrast': [0.8, 1.2]}`
- `hue`: `ColorJitter` `{'hue': [-0.05, 0.05]}`
- `saturation`: `ColorJitter` `{'saturation': [0.5, 1.5]}`
- `sharpness`: `SharpnessJitter` `{'sharpness': [0.5, 1.5]}`

## Inputs

- `observation.images.camera1`: `VISUAL`, shape `[3, 256, 256]`
- `observation.images.camera2`: `VISUAL`, shape `[3, 256, 256]`
- `observation.images.camera3`: `VISUAL`, shape `[3, 256, 256]`
- `observation.state`: `STATE`, shape `[6]`

## Outputs

- `action`: `ACTION`, shape `[7]`

## Usage

Install and use the same LeRobot checkout/environment that contains the SmolVLA 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/SmolVLA-UR7e-ArrangeBlock_50epoch \
  --episodes=10
```

For local Python usage, load the policy with LeRobot's policy factory 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: `0.010289`
- grad norm: `0.101402`
- learning rate: `2.5000801319183248e-06`
- update time: `1.1543 s/step`
- dataloading time: `1.0103 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, camera mapping, calibration, action
space, and embodiment configuration. It may not transfer reliably to different robots,
camera placements, object layouts, or tasks without further validation or fine-tuning.

## License and Terms

The training dataset is marked `apache-2.0`, and the SmolVLM2 component is marked
`apache-2.0`. The `lerobot/smolvla_base` model card does not currently declare a license
field, so this fine-tuned model is conservatively marked as `other`. Users are responsible
for checking the applicable base model, dataset, and deployment terms before use.

## Files

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