Robot Camera Control Learning From Manual Motion Corrections
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robotic camera control systems require extensive learning data and time to achieve accurate automation due to the infinite variations in photographic subject positions, leading to inefficiencies in machine learning processes.
Innovation Solution
A control device and method that incorporates a motion controller, correction controller, memory for operational and correctional information, and a learning part to facilitate machine learning using operational and correctional data, allowing for faster learning and improved accuracy by reflecting manipulational information from a manipulating device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a neural network is used to control the robot camera during automatic control, then the imaging operation can be automated, but an enormous amount of learning data is required and the learning time becomes excessively long
Solution Approach 1:
The system performs preliminary manual operations to generate teaching data before automatic control. The operator manually operates the robot camera to capture images of photographic subjects, and this manual operation data is stored as teaching data. This preliminary action allows the system to learn from real operational examples without requiring extensive synthetic learning data, significantly reducing the learning time while maintaining automation capability.
Solution Approach 2:
The system introduces teaching data as an intermediary between manual operation and automatic control. The teaching data, generated from manual operations, serves as a bridge that transfers human expertise to the automated system. This intermediary allows the neural network to learn from actual operational patterns rather than requiring direct training on enormous datasets, thus reducing learning time while achieving automation.
2Measurement precision
If the neural network learns from position data of photographic subjects, then the imaging quality can be improved, but the infinite variations in subject positions require an enormous amount of learning data
Solution Approach 1:
The system uses the robot camera's own manual operations to generate teaching data. When the operator manually operates the robot camera to image photographic subjects, the system automatically records the operational parameters and results as teaching data. This self-service approach eliminates the need for external data collection and processing, reducing the learning data volume required while maintaining high imaging quality through real operational examples.
Solution Approach 2:
The system performs preliminary manual imaging operations to collect teaching data covering various subject positions and conditions. By having the operator manually operate the robot camera through diverse scenarios beforehand, the system accumulates comprehensive teaching data that captures the complexity of real-world imaging situations, reducing the need for enormous synthetic learning datasets.
Data Source
AI summary
A control device includes a motion controller configured to control operation of a mechanical apparatus according to an operational command, a correction controller configured to correct the operation of the mechanical apparatus according to manipulational information outputted from a manipulating device, a memory part configured to store first operational information indicative of the operation of the mechanical apparatus, and correctional information indicative of the correction made by the correction controller, and a learning part configured to carry out machine learning using the first operational information and the correctional information corresponding to the first operational information. The motion controller controls the operation of the mechanical apparatus according to the operational command based on the command of the learning part, and the manipulating device outputs the manipulational information based on second operational information indicative of motion of the manipulating device.