giacomoran/so101_cube_dm_act_smooth_p4f2_0

1---2datasets: giacomoran/so101_cube_direct_manipulation3library_name: lerobot4license: apache-2.05model_name: act_smooth6pipeline_tag: robotics7tags:8- act_smooth9- robotics10- lerobot11---12 13# Model Card for act_smooth14 15<!-- Provide a quick summary of what the model is/does. -->16 17 18_Model type not recognized — please update this template._19 20 21This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).22See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).23 24---25 26## How to Get Started with the Model27 28For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).29Below is the short version on how to train and run inference/eval:30 31### Train from scratch32 33```bash34lerobot-train \35  --dataset.repo_id=${HF_USER}/<dataset> \36  --policy.type=act \37  --output_dir=outputs/train/<desired_policy_repo_id> \38  --job_name=lerobot_training \39  --policy.device=cuda \40  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>41  --wandb.enable=true42```43 44_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._45 46### Evaluate the policy/run inference47 48```bash49lerobot-record \50  --robot.type=so100_follower \51  --dataset.repo_id=<hf_user>/eval_<dataset> \52  --policy.path=<hf_user>/<desired_policy_repo_id> \53  --episodes=1054```55 56Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.57 58---59 60## Model Details61 62- **License:** apache-2.0