| ---
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| datasets: adrrrobo/Hackathon2_20260606_210708
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| library_name: lerobot
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| license: apache-2.0
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| model_name: act
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| pipeline_tag: robotics
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| tags:
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| - robotics
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| - lerobot
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| - act
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| ---
|
|
|
| # Model Card for act (sorting_ACT)
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|
|
| <!-- Provide a quick summary of what the model is/does. -->
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|
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| [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.
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|
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| This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
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| See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
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|
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| This repository is a re-trained copy of [`adrrrobo/sorting_ACT`](https://huggingface.co/adrrrobo/sorting_ACT),
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| using the same architecture and dataset. It was trained for **15,000 steps** on a single
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| **NVIDIA A100 40GB** GPU (final training loss ≈ 0.22).
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|
|
| - Base / reference model: [`adrrrobo/sorting_ACT`](https://huggingface.co/adrrrobo/sorting_ACT)
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| - Dataset: [`adrrrobo/Hackathon2_20260606_210708`](https://huggingface.co/datasets/adrrrobo/Hackathon2_20260606_210708) (99 episodes, ~106k frames)
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| - Policy: ACT, ~51.6M parameters
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| - Training: 15,000 steps, batch size 8
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|
|
| ---
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|
|
| ## How to Get Started with the Model
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|
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| For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
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| Below is the short version on how to train and run inference/eval:
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|
|
| ### Train from scratch
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|
|
| ```bash
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| lerobot-train \
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| --dataset.repo_id=adrrrobo/Hackathon2_20260606_210708 \
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| --policy.type=act \
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| --output_dir=outputs/train/sorting_act \
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| --job_name=sorting_act \
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| --policy.device=cuda \
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| --steps=15000 \
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| --batch_size=8 \
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| --wandb.enable=false
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| ```
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|
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| _Writes checkpoints to `outputs/train/sorting_act/checkpoints/`._
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|
|
| ### Evaluate the policy/run inference
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|
|
| ```bash
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| lerobot-record \
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| --robot.type=so100_follower \
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| --dataset.repo_id=<hf_user>/eval_<dataset> \
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| --policy.path=kennytsu/sorting_ACT \
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| --episodes=10
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| ```
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|
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| Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
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|
|
| ---
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|
|
| ## Model Details
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|
|
| - **License:** apache-2.0
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| - **Trained on:** NVIDIA A100 40GB, 15,000 steps
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| - **Final training loss:** ~0.22
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| |
