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datasets: airshop/Lego_task_complex
library_name: lerobot
license: apache-2.0
model_name: ACT
pipeline_tag: robotics
tags:
- ACT
- lerobot
- robotics
---
# Complex Lego Task
## Description
Pick up small lego block (pink) using left arm and place onto 2 x 1 (white), then right arm picks up combined block, places on 2 x 2 block (blue). In this setup, blue block is closer to right robot arm and pink block is closer to left robot arm, white block in between both.
## Dataset
- Repo: airshop/Lego_task_complex
- Image augmentations: Disabled.
## Training
- Model / Policy Type: ACT
- Steps: 50000
- Batch size: 8
- Vision backbone: resnet18
- VAE enabled: True (latent dim: 32)
- Training time: 18 hours
- Machine: Spark (NVIDIA GB10)
## Inputs
4 visual streams, 1 state inputs
## Results
[Insert results here]
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=ACT \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
- **License:** apache-2.0
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