insert-mouse-battery-resnet18-reward-v1

Insert Mouse Battery ResNet18 Reward V1

This repository contains a binary ResNet18 reward model for the insert-mouse-battery task.

Input Format

• Image input: RGB three-view vertical stack in Wan world-model format.
• View order: cam_high + cam_left_wrist + cam_right_wrist.
• Image size: [3, 544, 320] as CHW tensor.
• Layout: each view is 180x320; the stacked image is 540x320; the full stack is resized to 544x320.
• Intended preprocessing: the same normalization/preprocessing path used by the RLinf ResNet reward model.

Output Format

The checkpoint is a binary reward model. For a single input image, the model outputs one scalar logit. Applying sigmoid gives the estimated full-task success probability.

Files

Test Results

Training Data

The dataset uses three-view videos from expert-data, success-and-hil-data, and failure-data.

• Positive samples: tail frames from expert and success/HIL episodes.
• Negative samples: early and middle frames from expert and success/HIL episodes, plus sampled frames from failure episodes.
• Train/validation splitting is done at the episode level.
• The train and validation sets are class-balanced.