18489713. ROBOT SYSTEM, LEARNING APPARATUS, INFORMATION PROCESSING APPARATUS, LEARNED MODEL, CONTROL METHOD, INFORMATION PROCESSING METHOD, METHOD FOR MANUFACTURING PRODUCT, AND RECORDING MEDIUM simplified abstract (CANON KABUSHIKI KAISHA)
Contents
- 1 ROBOT SYSTEM, LEARNING APPARATUS, INFORMATION PROCESSING APPARATUS, LEARNED MODEL, CONTROL METHOD, INFORMATION PROCESSING METHOD, METHOD FOR MANUFACTURING PRODUCT, AND RECORDING MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ROBOT SYSTEM, LEARNING APPARATUS, INFORMATION PROCESSING APPARATUS, LEARNED MODEL, CONTROL METHOD, INFORMATION PROCESSING METHOD, METHOD FOR MANUFACTURING PRODUCT, AND RECORDING MEDIUM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does the robot system ensure the accuracy of the learned model over time?
- 1.11 What are the limitations of the robot system in terms of the types of tasks it can perform?
- 1.12 Original Abstract Submitted
ROBOT SYSTEM, LEARNING APPARATUS, INFORMATION PROCESSING APPARATUS, LEARNED MODEL, CONTROL METHOD, INFORMATION PROCESSING METHOD, METHOD FOR MANUFACTURING PRODUCT, AND RECORDING MEDIUM
Organization Name
Inventor(s)
KAZUHIKO Shinagawa of Tokyo (JP)
YUICHIRO Kudo of Kanagawa (JP)
MOTOHIRO Horiuchi of Kanagawa (JP)
ROBOT SYSTEM, LEARNING APPARATUS, INFORMATION PROCESSING APPARATUS, LEARNED MODEL, CONTROL METHOD, INFORMATION PROCESSING METHOD, METHOD FOR MANUFACTURING PRODUCT, AND RECORDING MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18489713 titled 'ROBOT SYSTEM, LEARNING APPARATUS, INFORMATION PROCESSING APPARATUS, LEARNED MODEL, CONTROL METHOD, INFORMATION PROCESSING METHOD, METHOD FOR MANUFACTURING PRODUCT, AND RECORDING MEDIUM
Simplified Explanation
The robot system described in the patent application is designed to learn force information, position information, and workpiece information from a worker and then control the robot based on this learned model.
- The information processing portion of the robot system learns force information, position information, and workpiece information from a worker.
- The robot is controlled based on the output data of the learned model.
Potential Applications
This technology could be applied in industries such as manufacturing, construction, and healthcare where robots need to interact with human workers and workpieces.
Problems Solved
1. Improved safety in human-robot collaboration by allowing the robot to adapt to the force applied by the worker. 2. Enhanced efficiency and accuracy in tasks where the robot needs to work in close proximity to humans.
Benefits
1. Increased productivity by enabling seamless collaboration between humans and robots. 2. Reduced risk of accidents and injuries in workplaces where robots are used alongside human workers.
Potential Commercial Applications
Optimizing manufacturing processes, improving construction efficiency, and enhancing healthcare services through the use of collaborative robots.
Possible Prior Art
One possible prior art could be robotic systems that use force sensors to detect and respond to external forces, but the specific learning model described in this patent application may be a novel approach.
Unanswered Questions
How does the robot system ensure the accuracy of the learned model over time?
The patent application does not provide details on how the system maintains the accuracy of the learned model as conditions change or evolve. This could be crucial for the long-term effectiveness of the technology.
What are the limitations of the robot system in terms of the types of tasks it can perform?
The patent application does not address the specific tasks or scenarios where the robot system may not be suitable or effective. Understanding these limitations could help potential users make informed decisions about implementing the technology.
Original Abstract Submitted
A robot system includes a robot, and an information processing portion. The information processing portion is configured to obtain a learned model by learning first force information about a force applied by a worker to a workpiece, first position information about a position of a first portion of the worker, and first workpiece information about a state of the workpiece, and control the robot on a basis of output data of the learned model.
