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license: apache-2.0
library_name: lerobot
tags:
- lerobot
- diffusion-policy
- imitation-learning
- robotics
- ur3e
- bimanual
datasets:
- WetLabRoboData/lerobot-data-cap
pipeline_tag: robotics
base_model: WetLabRoboData/diffusion-multitask_12task_mix-multitask
---
# diffusion-cap-finetuned
LeRobot diffusion policy for the **cap** task.
| Field | Value |
|--------------------|-------|
| Policy family | diffusion (LeRobot) |
| Variant | Finetuned (multitask pretrain, then finetuned on this task's finetune pool) |
| Training dataset | [`WetLabRoboData/lerobot-data-cap`](https://huggingface.co/datasets/WetLabRoboData/lerobot-data-cap) |
| Base model | [`WetLabRoboData/diffusion-multitask_12task_mix-multitask`](https://huggingface.co/WetLabRoboData/diffusion-multitask_12task_mix-multitask) |
| Target task | cap |
| Eval episodes | 20 |
| Eval successes | 6 / 20 |
| Robot | UR3e bimanual (3 cameras) |
## Evaluation
Rollout videos + per-episode outcomes: [`WetLabRoboData/eval-diffusion-cap-finetuned`](https://huggingface.co/datasets/WetLabRoboData/eval-diffusion-cap-finetuned).
## Load and deploy
```python
from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
policy = DiffusionPolicy.from_pretrained("WetLabRoboData/diffusion-cap-finetuned")
```
## Provenance
Reorganized from `WetLabRoboData/lerobot-data-rama-lbm_finetune_cap` on 2026-10-04. The archived
training outputs (checkpoints, train_config.json, wandb/) are preserved in
the `old/` subfolder of that source repo for traceability.
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