Robotics
LeRobot
Safetensors
act
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Upload policy weights, train config and readme

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  1. README.md +62 -0
  2. model.safetensors +1 -1
  3. train_config.json +4 -4
README.md ADDED
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+ ---
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+ datasets: mahmud8248/record-Robot422-v3
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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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+ - act
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+ - lerobot
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+ - robotics
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+ ---
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+
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+ # Model Card for act
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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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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+
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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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+ ---
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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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+
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+ ### Train from scratch
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+
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+ ```bash
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+ lerobot-train \
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+ --dataset.repo_id=${HF_USER}/<dataset> \
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+ --policy.type=act \
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+ --output_dir=outputs/train/<desired_policy_repo_id> \
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+ --job_name=lerobot_training \
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+ --policy.device=cuda \
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+ --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
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+ --wandb.enable=true
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+ ```
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+
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+ _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
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+
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+ ### Evaluate the policy/run inference
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+
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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=<hf_user>/<desired_policy_repo_id> \
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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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+ ---
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+
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+ ## Model Details
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+
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+ - **License:** apache-2.0
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train_config.json CHANGED
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  "optimizer_weight_decay": 0.0001,
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  "optimizer_lr_backbone": 1e-05
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  },
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- "output_dir": "/content/drive/MyDrive/Lerobot_Training_Aug9/outputs/train/SO101_test107",
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  "job_name": "SO101_test",
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  "resume": false,
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  "seed": 1000,
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  "num_workers": 2,
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  "batch_size": 8,
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- "steps": 50000,
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  "eval_freq": 20000,
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  "log_freq": 200,
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  "save_checkpoint": true,
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- "save_freq": 500,
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  "use_policy_training_preset": true,
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  "optimizer": {
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  "type": "adamw",
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  "project": "lerobot",
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  "entity": null,
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  "notes": null,
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- "run_id": "xmjrqbcw",
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  "mode": null
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  }
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  }
 
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  "optimizer_weight_decay": 0.0001,
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  "optimizer_lr_backbone": 1e-05
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  },
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+ "output_dir": "/content/drive/MyDrive/Lerobot_Training_Aug9/outputs/train/SO101_test108",
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  "job_name": "SO101_test",
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  "resume": false,
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  "seed": 1000,
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  "num_workers": 2,
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  "batch_size": 8,
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+ "steps": 1000,
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  "eval_freq": 20000,
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  "log_freq": 200,
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  "save_checkpoint": true,
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+ "save_freq": 400,
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  "use_policy_training_preset": true,
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  "optimizer": {
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  "type": "adamw",
 
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  "project": "lerobot",
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  "entity": null,
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  "notes": null,
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+ "run_id": "voiqzavv",
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  "mode": null
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  }
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  }