| --- |
| 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. |
|
|
