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qmddn/
insert-mouse-battery-resnet18-reward-v2

posttraining-for-robotics

insert-mouse-battery

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Insert Mouse Battery ResNet18 Reward V2

This repository contains a binary ResNet18 reward model for the insert-mouse-battery task, retrained from scratch on the local processed LeRobot data.

Input Format

Image input: RGB three-view vertical stack from the processed LeRobot videos.
View order: observation.images.top_head + observation.images.hand_left + observation.images.hand_right.
Image size: [3, 1440, 640] as CHW tensor.
Layout: each view is 480x640; the stacked image is 1440x640.
Preprocessing: RGB conversion, CHW float tensor in [0, 1], ImageNet mean/std normalization.

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

File	Description
`full_weights.pt`	ResNet18 reward checkpoint trained from scratch.
`model_metadata.json`	Input/output format and dataset construction metadata.
`eval_summary.json`	Train/validation metrics from the training run.

Test Results

split	samples	positives	negatives	accuracy	precision	recall	F1	loss
validation	192	96	96	0.9948	0.9897	1.0000	0.9948	0.0063
best validation	192	96	96	1.0000	1.0000	1.0000	1.0000	0.0174

Training Data

The dataset uses three-view videos from the existing processed LeRobot splits.

Positive samples: tail frames from successful episodes.
Negative samples: head, early, and middle frames from the same successful episodes.
Train/validation splitting follows the existing processed split directories and stays episode-level.
The train and validation sets are class-balanced.
No pretrained weights from qmddn/insert-mouse-battery-resnet18-reward-v1 were used.

qmddn/insert-mouse-battery-resnet18-reward-v2