Policy 2026 07 15 Shakeitup4 Bench Dragonfly 10kHz Nfft 64

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=${HF_USER}/ \
--policy.type=act \
--output_dir=outputs/train/ \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/
--wandb.enable=true

Writes checkpoints to outputs/train/ /checkpoints/.

Evaluate the policy/run inference

lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id= /eval_ \
--policy.path= / \
--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

--- datasets: jogarulfop/2026-07-15_shakeitup4_bench_dragonfly_10kHz_nfft_64 library_name: lerobot license: apache-2.0 model_name: act pipeline_tag: robotics tags: - act - lerobot - robotics --- # Model Card for act <!-- Provide a quick summary of what the model is/does. --> [Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. 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). --- ## 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 - **License:** apache-2.0