Surgical Robot Instrument Control With Task-Specific Data Selection
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Solution Overview
Problem
Existing robotic manipulation systems face challenges in performing real-time, closed-loop control due to high processing demands and data bandwidth requirements, limiting their practical use in surgical environments.
Innovation Solution
A computer-assisted system that includes repositionable structures and a control system capable of analyzing multiple data streams to identify tasks, selecting instruments and structures, and generating action tokens for real-time control using a Robotics Transformer model by dynamically adjusting input data modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a Robotic Transformer model is used to process multiple data streams for automated control, then task identification accuracy and automation capability are improved, but inference time increases quadratically with each input parameter, making real-time control impractical
Solution Approach 1:
The patent segments the processing of multiple data streams by selecting and processing only task-specific data modalities rather than all available data streams. This divides the computational workload into manageable portions based on current task requirements, preventing quadratic inference time growth while maintaining automated control capability.
Solution Approach 2:
The system performs partial processing by analyzing only the necessary subset of data streams required for the current task rather than processing all available data streams. This partial action approach reduces computational burden and inference time while still achieving effective automated control for the specific task at hand.
2Measurement precision
If all data streams are processed to ensure comprehensive task analysis, then measurement precision and reliability are improved, but processing time and computational resources increase beyond real-time requirements
Solution Approach 1:
The patent applies local quality by tailoring the data processing approach to each specific task's requirements. Different data modalities are selected and processed based on the particular task being performed, ensuring high measurement precision for task identification while avoiding unnecessary processing of irrelevant data streams that would reduce productivity.
3Adaptability or versatility
If multiple data modalities are ingested to improve task understanding, then adaptability and task coverage are improved, but data bandwidth requirements and processing complexity increase beyond practical limits
Solution Approach 1:
The system dynamically adjusts which data modalities are processed based on the current task requirements. This dynamic selection approach maintains high adaptability and task coverage capability while avoiding the fixed complexity of processing all possible data modalities simultaneously, as the processing configuration changes adaptively with each task.
Data Source
AI summary
Systems and methods are described for determining task based utilization of instruments and repositionable structures. The system may include one or more repositionable structures operatively coupled to one or more instruments, and a control system operably coupled to the one or more repositionable structures, the control system configured to receive a plurality of data streams from one or more data sources and analyze the data streams to identify a task to be performed; determine, based on the task to be performed and via an actor selection machine learning model, one or more selected instruments for performing the task, or a selected repositionable structure of the repositionable structures; generate, via a robotic action machine learning model, one or more action tokens for controlling the repositionable structures based on the task and the selected instrument or selected repositionable structure; and control the selected instrument or selected repositionable structure to perform the task.