Skill Transfer Motion Control for Fast Robot Camera Automation
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Solution Overview
Problem
Existing mechanical apparatuses face challenges in quickly automating tasks due to the need for extensive learning data and time, especially in industries with aging workforces and decreasing skilled labor, as they require significant training to maintain imaging quality and adapt to varying object positions.
Innovation Solution
A skill transfer mechanical apparatus that includes an operating part, a controller, a motion information detector, and a manual motion correcting data generator, allowing for manual corrections to be integrated into automatic learning, reducing the need for extensive training data and enabling faster automation by accumulating expert skills through machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a neural network is used to control a robot camera during automatic control, then automation is achieved, but an enormous amount of learning data and a long start period are required to maintain imaging quality at the same level as a skilled cameraman
Solution Approach 1:
The system performs preliminary actions by having the skilled cameraman manually operate the robot camera to collect teaching data before automation. The motion information detector records the cameraman's operations, and this data is stored in advance for the neural network to learn, eliminating the need for lengthy on-the-job training periods.
Solution Approach 2:
The system creates a copy of the skilled cameraman's operations through motion information detection and recording. The neural network learns from these copied motion patterns, replicating the expert's imaging skills without requiring the expert to continuously train the system through extensive practical operation.
2Extent of automation
If a neural network is used to control a robot camera during automatic control, then automation is achieved, but an enormous amount of learning data is required to appropriately control the operating state according to actual situations
Solution Approach 1:
The system focuses on capturing and learning specific local qualities of the skilled cameraman's operations rather than requiring comprehensive data across all possible scenarios. The motion information detector records precise operational details, and the neural network learns from these targeted motion patterns, reducing the overall data volume needed.
Solution Approach 2:
The motion information detector and teaching data collection system serve multiple functions: they record operational data, analyze motion patterns, and provide training data for the neural network. This multi-functional approach consolidates data collection efforts and reduces the total volume of learning data required.
3Extent of automation
If the robot camera is controlled only by the neural network during automatic control, then automation is achieved, but it is difficult to transfer skilled worker's skills in the industry in a short period of time
Solution Approach 1:
The system performs preliminary skill transfer by having the skilled worker manually operate the mechanical apparatus to collect teaching data before full automation is implemented. This preliminary phase captures expert knowledge in structured data format, enabling rapid neural network training and quick transition to automation without lengthy skill transfer periods.
Solution Approach 2:
The motion information detector provides feedback on the skilled worker's operations, allowing the neural network to learn from actual expert performance. This feedback mechanism accelerates skill transfer by enabling the system to quickly understand and replicate expert techniques through analyzed motion data rather than prolonged observation and training.
Data Source
AI summary
A skill transfer mechanical apparatus includes an operating part, a controller, a motion information detector and an operation apparatus. The controller includes a basic motion instructing module, a learning module, a motion correcting instruction generator, a motion correcting instruction, and a motion information storing module. The learning module carries out machine learning of the motion correcting instruction stored in the motion correcting instruction storing module by using the motion information stored in the motion information storing module, and after the machine learning is finished, accepts an input of the motion information during the operation of the operating part, and outputs the automatic motion correcting instruction. The operating part moves the working part according to an automatic motion instruction based on the basic motion instruction and the automatic motion correcting instruction, and the manual motion correction.