| --- |
| datasets: robometer/RBM-1M |
| library_name: lerobot |
| license: apache-2.0 |
| model_name: robometer |
| pipeline_tag: robotics |
| tags: |
| - reward-model |
| - lerobot |
| - qwen3-vl |
| - robotics |
| - zero-shot |
| - robometer |
| - vision-language |
| --- |
| |
| # Reward Model Card for robometer |
|
|
| <!-- Provide a quick summary of what the reward model is/does. --> |
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| Robometer is a zero-shot general-purpose robotic reward model built on a fine-tuned Qwen3-VL backbone with progress, preference, and success heads. Given a video and a task description it outputs a per-frame progress signal in [0, 1] and a per-frame success probability — suitable for offline reward labelling and for low-frequency reward signals during RL fine-tuning of robot policies. |
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| This reward model 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). |
|
|
| --- |
|
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| ## How to Get Started with the Reward Model |
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| ### Train from scratch |
|
|
| ```bash |
| lerobot-train \ |
| --dataset.repo_id=${HF_USER}/<dataset> \ |
| --reward_model.type=robometer \ |
| --output_dir=outputs/train/<desired_reward_model_repo_id> \ |
| --job_name=lerobot_reward_training \ |
| --reward_model.device=cuda \ |
| --reward_model.repo_id=${HF_USER}/<desired_reward_model_repo_id> \ |
| --wandb.enable=true |
| ``` |
|
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| _Writes checkpoints to `outputs/train/<desired_reward_model_repo_id>/checkpoints/`._ |
|
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| ### Load the reward model in Python |
|
|
| ```python |
| from lerobot.rewards import make_reward_model |
| |
| reward_model = make_reward_model(pretrained_path="<hf_user>/<reward_model_repo_id>") |
| reward = reward_model.compute_reward(batch) |
| ``` |
|
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| --- |
|
|
| ## Model Details |
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| - **License:** apache-2.0 |
