Instructions to use Leobinus/act_smooth_cleanup_plate_stack_clean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use Leobinus/act_smooth_cleanup_plate_stack_clean with LeRobot:
- Notebooks
- Google Colab
- Kaggle
metadata
datasets: Leobinus/cleanup_plate_stack_clean
library_name: lerobot
license: apache-2.0
model_name: act_smooth
pipeline_tag: robotics
tags:
- robotics
- lerobot
- act_smooth
Model Card for act_smooth
Model type not recognized — please update this template.
This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.
How to Get Started with the Model
For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:
Train from scratch
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
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
- License: apache-2.0