Robot Control Learning From Operator Force and Sensor Feedback
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Solution Overview
Problem
Conventional robot operation control methods require extensive programming and adjustment, making them time-consuming and inflexible for adapting to various situations, and lack a suitable model for integrating operator input and environmental data effectively.
Innovation Solution
A robot system configuration that includes a robot, motion sensor, surrounding environment sensor, operation apparatus, learning control section, and relay apparatus, which uses machine learning to construct a model that converts operator-operating force and environmental data into operation commands, allowing the robot to autonomously perform tasks and adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a program for operating a robot is created by human understanding and manual programming, then the robot can perform predetermined tasks, but it takes a long time to create and adjust the program
Solution Approach 1:
The patent replaces manual programming mechanics with machine learning-based automatic program generation. The learning control section automatically generates control programs by learning from operation data, sensor data, and operation commands, eliminating the need for manual program creation and adjustment while maintaining execution reliability
Solution Approach 2:
The robot system performs self-learning and self-programming through the learning control section. By automatically generating control programs from collected operation data and sensor data, the system serves itself without requiring external programmers, thereby reducing program creation time while maintaining reliability
2Reliability
If conventional programming methods are used to control the robot, then the robot can execute predetermined operations, but it lacks flexibility to adapt to various situations
Solution Approach 1:
The patent introduces dynamic adaptability through machine learning. The learning control section continuously learns from operation data, sensor data, and operation commands, enabling the robot to dynamically adjust its control program to adapt to various situations while maintaining reliable operation execution through the learned models
Solution Approach 2:
The system implements feedback mechanisms where sensor data and operation data are fed back to the learning control section. This feedback enables the system to learn from actual operations and environmental conditions, improving adaptability to various situations while maintaining execution reliability through continuous learning and model refinement
3Ease of operation
If a robot system with operation apparatus and control section is configured, then operator input can be integrated, but a suitable model for converting operator-operating force and environmental data into operation commands was not known
Solution Approach 1:
The patent creates a universal learning control section that handles multiple functions: learning from operation data, processing sensor data, generating operation commands, and adapting to various operating conditions. This multi-functional approach integrates operator input effectively while managing complexity through a single unified learning model rather than separate models for each function
Data Source
AI summary
A robot system (1) includes the robot (10), a motion sensor (11), a surrounding environment sensor (12, 13), an operation apparatus (21), a learning control section (41), and a relay apparatus (30). The robot (10) performs work based on an operation command. The operation apparatus (21) detects and outputs an operator-operating force applied by the operator. The learning control section (41) outputs a calculation operating force. The relay apparatus (30) outputs the operation command based on the operator-operating force and the calculation operating force. The learning control section (41) estimates and outputs the calculation operating force by using a model constructed by performing the machine learning of the operator-operating force, the surrounding environment data, the operation data, and the operation command based on the operation data and the surrounding environment data outputted by the sensors (11 to 13), and the operation command outputted by the relay apparatus (30).