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README.md

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+---
+datasets: robometer/RBM-1M
+library_name: lerobot
+license: apache-2.0
+model_name: robometer
+pipeline_tag: robotics
+tags:
+- lerobot
+- zero-shot
+- robotics
+- robometer
+- qwen3-vl
+- reward-model
+- 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

config.json

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+{
+    "type": "robometer",
+    "input_features": {
+        "observation.images.top": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                224,
+                224
+            ]
+        }
+    },
+    "output_features": {
+        "progress": {
+            "type": "REWARD",
+            "shape": [
+                1
+            ]
+        },
+        "success": {
+            "type": "REWARD",
+            "shape": [
+                1
+            ]
+        }
+    },
+    "device": "cpu",
+    "pretrained_path": "robometer/Robometer-4B",
+    "push_to_hub": false,
+    "repo_id": null,
+    "license": "apache-2.0",
+    "tags": [
+        "reward-model",
+        "vision-language",
+        "qwen3-vl",
+        "zero-shot"
+    ],
+    "private": null,
+    "image_key": "observation.images.top",
+    "task_key": "task",
+    "default_task": null,
+    "max_frames": 8,
+    "reward_output": "progress",
+    "success_threshold": 0.5,
+    "base_model_id": "Qwen/Qwen3-VL-4B-Instruct",
+    "torch_dtype": "bfloat16",
+    "use_multi_image": true,
+    "use_per_frame_progress_token": true,
+    "average_temporal_patches": true,
+    "frame_pooling": "mean",
+    "frame_pooling_attn_temperature": 1.0,
+    "progress_loss_type": "discrete",
+    "progress_discrete_bins": 10,
+    "normalization_mapping": {
+        "VISUAL": "IDENTITY",
+        "REWARD": "IDENTITY"
+    }
+}

model.safetensors

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+size 8894103800