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license: apache-2.0
library_name: lerobot
pipeline_tag: robotics
base_model: CoRL2026-CSI/smolvla_isaaclab_so101_11task_basecap_3300epi_8ep
datasets:
- CoRL2026-CSI/SO101-cap_stack_RGBblock_on_bluedish_10fps
tags:
- lerobot
- robotics
- smolvla
- vla
- so101
- code-as-policies
- cap
- imitation-learning
- 50epochs
- single-arm
- dual-camera
- stack-block
- rgb-blocks
- blue-dish
---
# SmolVLA-CaP-StackBlock-50epochs
This repository contains a SmolVLA policy fine-tuned with LeRobot for the SO101 CAP task **Stack RGB Blocks on a Blue Dish**. The policy was initialized from `CoRL2026-CSI/smolvla_isaaclab_so101_11task_basecap_3300epi_8ep` and trained for 50 epochs on `CoRL2026-CSI/SO101-cap_stack_RGBblock_on_bluedish_10fps`.
## Model Details
| Field | Value |
|---|---|
| Policy type | `smolvla` |
| Task | stack red, green, and blue blocks on the blue dish from bottom to top |
| Robot | SO101 follower |
| Dataset | `CoRL2026-CSI/SO101-cap_stack_RGBblock_on_bluedish_10fps` |
| Base model | `CoRL2026-CSI/smolvla_isaaclab_so101_11task_basecap_3300epi_8ep` |
| Training steps | `17100` |
| Completed step | `17100` |
| Batch size | `128` per GPU |
| Effective batch size | `256` |
| Action chunk size | `50` |
| Action horizon | `50` |
| Observation steps | `1` |
| Inference denoising steps | `50` |
| Model weights | `model.safetensors` (864.7 MiB) |
## Training Setup
The run used two CUDA processes with `batch_size=128` per process, image augmentation enabled, and camera key remapping from the dataset's raw cameras to the SmolVLA camera names:
```text
observation.images.left_wrist -> observation.images.camera1
observation.images.top -> observation.images.camera2
```
The checkpoint was saved locally at step `17100` with LeRobot's preprocessor and postprocessor artifacts included in this repository.
## Files
```text
model.safetensors
config.json
train_config.json
policy_preprocessor.json
policy_preprocessor_step_5_normalizer_processor.safetensors
policy_postprocessor.json
policy_postprocessor_step_0_unnormalizer_processor.safetensors
```
## Usage
```python
from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
policy = SmolVLAPolicy.from_pretrained("CoRL2026-CSI/SmolVLA-CaP-StackBlock-50epochs")
```
For robot deployment, use the same camera mapping, normalization pipeline, and SO101 action/state conventions used by the training dataset.
## Intended Use
This model is intended for imitation-learning experiments and SO101 tabletop manipulation research on the specified CAP task. It is not a general-purpose robot policy and should be validated in a controlled workspace before any hardware deployment.
## Limitations
The model was trained on a single task dataset with fixed camera views, object set, action space, and workspace assumptions. No official evaluation success rate is included in this repository.
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