Robotics
LeRobot
Safetensors
vla0_smol
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@@ -2,18 +2,18 @@
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  datasets: HuggingFaceVLA/libero
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  library_name: lerobot
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  license: apache-2.0
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- model_name: vla0
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  pipeline_tag: robotics
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  tags:
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  - lerobot
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  - robotics
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- - vla0
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  ---
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- # Model Card for vla0
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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  _Model type not recognized — please update this template._
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@@ -26,31 +26,30 @@ See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot
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  ## How to Get Started with the Model
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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=${HF_USER}/<dataset> \
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- --policy.type=act \
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- --output_dir=outputs/train/<desired_policy_repo_id> \
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- --job_name=lerobot_training \
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- --policy.device=cuda \
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- --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
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- --wandb.enable=true
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  ```
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  _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/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=<hf_user>/<desired_policy_repo_id> \
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- --episodes=10
 
 
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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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  datasets: HuggingFaceVLA/libero
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  library_name: lerobot
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  license: apache-2.0
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+ model_name: vla0_smol
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  pipeline_tag: robotics
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  tags:
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  - lerobot
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  - robotics
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+ - vla0_smol
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  ---
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+ # Model Card for vla0-smol
 
 
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+ Read about the model here: https://robot-learning-collective.github.io/vla-0-smol
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+ This is VLA-0-Smol trained on the Libero dataset.
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  _Model type not recognized — please update this template._
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  ## How to Get Started with the Model
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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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+
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+ Support of the model is implemented in repo: https://github.com/Robot-Learning-Collective/lerobot-experiments
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+
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+
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  Below is the short version on how to train and run inference/eval:
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+ ### Train from scratch on PushT
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  ```bash
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+ lerobot-train --config_path=configs/vla0_smol.json
 
 
 
 
 
 
 
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  ```
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  _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
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+ ### Evaluate the policy on Libero
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  ```bash
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+ lerobot-eval \
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+ --policy.path="Robot-Learning-Collective/VLA-0-Smol" \
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+ --policy.n_action_steps=0 \
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+ --policy.ensemble_size=8 \
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+ --env.type=libero \
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+ --env.task=libero_object \
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+ --eval.batch_size=2
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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.