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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. -->
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.
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).
---
## How to Get Started with the Reward Model
### 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
```
_Writes checkpoints to `outputs/train/<desired_reward_model_repo_id>/checkpoints/`._
### 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)
```
---
## Model Details
- **License:** apache-2.0 |