Insert Mouse Battery Videomae Reward v1
Binary VideoMAE reward model for detecting the completed state of the insert-mouse-battery task from an approximately one-second, three-view clip. It was newly fine-tuned from MCG-NJU/videomae-base-finetuned-kinetics...
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 338 MB
- Versión
- vRevision 1dc6535
- Actualizado
- 10 oct 2026
- Descargas
- 10
- Fuente
- Hugging Face
Descripción de la fuente
Insert Mouse Battery VideoMAE Reward
Binary VideoMAE reward model for detecting the completed state of the insert-mouse-battery task from an approximately one-second, three-view clip. It was newly fine-tuned from MCG-NJU/videomae-base-finetuned-kinetics and does not use weights from an earlier reward model.
Input Format
- Eight RGB frames sampled every four source frames at 30 FPS; covered duration is 29 frames (about 0.97 seconds).
- View order per time step:
top_head,hand_left,hand_right. - Each view is resized to
240x320, then vertically stacked to720x320. - Tensor shape:
[batch, 8, 3, 720, 320]. - Normalize with mean
[0.45, 0.45, 0.45]and standard deviation[0.225, 0.225, 0.225]. - VideoMAE tubelet/patch size:
[2, 24, 32].
Output Format
The model returns two logits: not_success and success. The reward is softmax(logits)[..., 1]. The selected validation threshold is 0.92.
Training Data
- Training clips: 2255 (735 dense failure clips added).
- Validation clips: 568 (184 dense failure clips added).
- Positive clips come only from the final two-second stable window of successful episodes, with the complete clip inside that window.
- Negatives include early, middle, hard-preterminal successful clips and dense clips from unsuccessful trajectories.
- Train and validation episodes are disjoint.
- Sources include the processed LeRobot success demonstrations and the 20-success/40-failure rollout dataset.
Validation Results
| metric | value |
|---|---|
| accuracy @ 0.5 | 0.9824 |
| precision @ 0.5 | 0.9505 |
| recall @ 0.5 | 1.0000 |
| F1 @ 0.5 | 0.9746 |
| ROC AUC | 0.9939 |
Full Held-Out Rollouts
| metric | VideoMAE | ResNet18 v4 baseline |
|---|---|---|
| successful episodes detected | 4/4 | 4/4 |
| failed episodes with sustained false trigger | 3/8 | 2/8 |
| success terminal positive rate | 0.9750 | 0.8824 |
| failure frame false-positive rate | 0.0406 | 0.0364 |
A sustained trigger is at least three consecutive sampled predictions above the selected threshold.
Files
| file | description |
|---|---|
model.safetensors | Best VideoMAE reward weights. |
config.json | Transformers model configuration and label mapping. |
preprocessor_config.json | Clip, view, resize, normalization, and threshold metadata. |
evaluation.json | Aggregate and per-episode validation results. |
training_history.json | Per-epoch metrics. |
inference.py | Minimal preprocessing and reward helper. |
Limitations
Completion time is represented by the final stable window because the source datasets provide episode-level success rather than an annotated insertion timestamp. Validate the threshold in the target policy and camera setup before using the score for RL optimization.
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 10 oct
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- library_name: transformers pipeline_tag: video-classification tags: - robotics - reward-model - videomae - posttraining-for-robotics - insert-mouse-battery --- # Insert Mouse Battery VideoMAE Reward Binary VideoMAE reward model for detecting the completed state of the `insert-mouse-battery` task from an approximately one-second, three-view clip. It was newly fine-tuned from `MCG-NJU/videomae-base-finetuned-kinetics` and does not use weights from an earlier reward model. ## Input Format - Eight RGB frames sampled every four source frames at 30 FPS; covered duration is 29 frames (about 0.97 seconds). - View order per time step: `top_head`, `hand_left`, `hand_right`. - Each view is resized to `240x320`, then vertically stacked to `720x320`. - Tensor shape: `[batch, 8, 3, 720, 320]`. - Normalize with mean `[0.45, 0.45, 0.45]` and standard deviation `[0.225, 0.225, 0.225]`. - VideoMAE tubelet/patch size: `[2, 24, 32]`. ## Output Format The model returns two logits: `not_success` and `success`. The reward is `softmax(logits)[..., 1]`. The selected validation threshold is `0.92`. ## Training Data - Training clips: 2255 (735 dense failure clips added). - Validation clips: 568 (184 dense failure clips added). - Positive clips come only from the final two-second stable window of successful episodes, with the complete clip inside that window. - Negatives include early, middle, hard-preterminal successful clips and dense clips from unsuccessful trajectories. - Train and validation episodes are disjoint. - Sources include the processed LeRobot success demonstrations and the 20-success/40-failure rollout dataset. ## Validation Results | metric | value | |---|---:| | accuracy @ 0.5 | 0.9824 | | precision @ 0.5 | 0.9505 | | recall @ 0.5 | 1.0000 | | F1 @ 0.5 | 0.9746 | | ROC AUC | 0.9939 | ### Full Held-Out Rollouts | metric | VideoMAE | ResNet18 v4 baseline | |---|---:|---:| | successful episodes detected | 4/4 | 4/4 | | failed episodes with sustained false trigger | 3/8 | 2/8 | | success terminal positive rate | 0.9750 | 0.8824 | | failure frame false-positive rate | 0.0406 | 0.0364 | A sustained trigger is at least three consecutive sampled predictions above the selected threshold. ## Files | file | description | |---|---| | `model.safetensors` | Best VideoMAE reward weights. | | `config.json` | Transformers model configuration and label mapping. |...
Source context: 10 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo qmddn/insert-mouse-battery-videomae-reward-v1
Source context: 17 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo qmddn/insert-mouse-battery-videomae-reward-v1
