18283228. Method for Generating Training Data for a Machine Learning (ML) Model simplified abstract (Siemens Aktiengesellschaft)
Contents
Method for Generating Training Data for a Machine Learning (ML) Model
Organization Name
Inventor(s)
Method for Generating Training Data for a Machine Learning (ML) Model - A simplified explanation of the abstract
This abstract first appeared for US patent application 18283228 titled 'Method for Generating Training Data for a Machine Learning (ML) Model
Simplified Explanation
The invention relates to a method for generating training data for an ML model designed for ascertaining control data for a gripping device for gripping an object.
- Selecting an object
- Selecting starting data of the object above a flat surface
- Generating a falling movement of the object towards the flat surface
- Capturing an image of the object after it comes to a standstill on the flat surface
- Assigning an identifier to the captured image representing a stable position of the object
- Storing the training data comprising the captured image and the assigned identifier
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- Potential Applications
This technology can be applied in robotics for object manipulation tasks, such as picking and placing objects in manufacturing processes.
- Problems Solved
This technology solves the problem of accurately training a machine learning model to control a gripping device for handling objects of various shapes and sizes.
- Benefits
The benefits of this technology include improved efficiency in object manipulation tasks, increased accuracy in gripping objects, and reduced errors in handling delicate or irregularly shaped objects.
- Potential Commercial Applications
One potential commercial application of this technology is in the automation industry for optimizing production processes by implementing robotic systems with advanced gripping capabilities.
- Possible Prior Art
Prior art in this field may include methods for training machine learning models for object recognition and manipulation tasks, as well as techniques for controlling robotic gripping devices in industrial settings.
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- Unanswered Questions
- How does this method compare to traditional approaches for training ML models in robotics applications?
This article does not provide a direct comparison between this method and traditional approaches for training ML models in robotics applications. It would be beneficial to understand the specific advantages and limitations of this new method compared to existing techniques.
- What are the potential limitations or challenges in implementing this technology in real-world robotic systems?
The article does not address the potential limitations or challenges in implementing this technology in real-world robotic systems. It would be important to explore factors such as scalability, adaptability to different environments, and integration with existing robotic systems.
Original Abstract Submitted
The invention relates to a method for generating training data for an ML model. The training data is designed to configure the ML model using a machine learning method. In particular, the ML model is designed to be used as part of a method for ascertaining control data for a gripping device for gripping an object. The invention is characterized by the steps of: —selecting an object, —selecting starting data of the object above a flat surface, —generating a falling movement of the object in the direction of the flat surface beginning with the starting data, —capturing an image of the object after the movement of the object has come to a standstill on the flat surface, —assigning an identifier to the captured image, said identifier comprising ID information for a stable position assumed by the object, wherein the stable position assumed by the object is designed and configured such that all of the object position data that can be converted into one another by means of a movement and/or rotation about a surface normal of the flat surface is assigned to the assumed stable position, and—storing the training data comprising the captured image and the identifier assigned thereto.
