insert-mouse-battery-videomae-reward-v1

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

Full Held-Out Rollouts

A sustained trigger is at least three consecutive sampled predictions above the selected threshold.

Files

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.