# Autonomous Robotic Drilling System for Mice Cranial Window Creation

arXiv: https://arxiv.org/abs/2406.14135 · HTML: https://mlsanity.com/paper/2406.14135/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 54 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 19 of 19)

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- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://mlsanity.com/paper/2004.02147/) (2020)
- [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://mlsanity.com/paper/1704.02971/) (2017)
- [UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory](https://mlsanity.com/paper/1609.02132/) (2016)
- [DSSD : Deconvolutional Single Shot Detector](https://mlsanity.com/paper/1701.06659/) (2017)
- [Semantic Flow for Fast and Accurate Scene Parsing](https://mlsanity.com/paper/2002.10120/) (2020)
- [Learning to Diagnose with LSTM Recurrent Neural Networks](https://mlsanity.com/paper/1511.03677/) (2015)
- [PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers](https://mlsanity.com/paper/2206.02066/) (2022)
- [PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model](https://mlsanity.com/paper/2204.02681/) (2022)
- [BlitzNet: A Real-Time Deep Network for Scene Understanding](https://mlsanity.com/paper/1708.02813/) (2017)
- [Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation](https://mlsanity.com/paper/1506.07452/) (2015)
- [Real-time Semantic Segmentation with Context Aggregation Network](https://mlsanity.com/paper/2011.00993/) (2020)
- [DQ Robotics: a Library for Robot Modeling and Control](https://mlsanity.com/paper/1910.11612/) (2019)
- [A Multi-Arm Robotic Platform for Scientific Exploration](https://mlsanity.com/paper/2210.11877/) (2022)
- [Autonomous Robotic Drilling System for Mice Cranial Window Creation: An Evaluation with an Egg Model](https://mlsanity.com/paper/2303.12265/) (2023)

## Abstract (arXiv)

Robotic assistance for experimental manipulation in the life sciences is expected to enable favorable outcomes, regardless of the skill of the scientist. Experimental specimens in the life sciences are subject to individual variability and hence require intricate algorithms for successful autonomous robotic control. As a use case, we are studying the cranial window creation in mice. This operation requires the removal of an 8-mm circular patch of the skull, which is approximately 300 um thick, but the shape and thickness of the mouse skull significantly varies depending on the strain of the mouse, sex, and age. In this work, we develop an autonomous robotic drilling system with no offline planning, consisting of a trajectory planner with execution-time feedback with drilling completion level recognition based on image and force information. In the experiments, we first evaluate the image-and-force-based drilling completion level recognition by comparing it with other state-of-the-art deep learning image processing methods and conduct an ablation study in eggshell drilling to evaluate the impact of each module on system performance. Finally, the system performance is further evaluated in postmortem mice, achieving a success rate of 70% (14/20 trials) with an average drilling time of 9.3 min.