Nvidia corporation (20240100694). AI-BASED CONTROL FOR ROBOTICS SYSTEMS AND APPLICATIONS simplified abstract
Contents
- 1 AI-BASED CONTROL FOR ROBOTICS SYSTEMS AND APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AI-BASED CONTROL FOR ROBOTICS SYSTEMS AND APPLICATIONS - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
AI-BASED CONTROL FOR ROBOTICS SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Ankur Handa of Seattle WA (US)
Arthur David Allshire of Toronto (CA)
Victor Makoviichuk of Santa Clara CA (US)
Aleksei Vladimirovich Petrenko of Cupertino CA (US)
AI-BASED CONTROL FOR ROBOTICS SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240100694 titled 'AI-BASED CONTROL FOR ROBOTICS SYSTEMS AND APPLICATIONS
Simplified Explanation
The abstract of the patent application describes systems techniques for controlling a robot using a machine learning model trained through population-based or reinforcement learning operations.
- Machine learning model trained for robot control:
* The machine learning model is trained based on population-based training operations or reinforcement learning operations. * Once trained, the model can be deployed to control a robot for task performance.
Potential Applications
The technology can be applied in various industries such as manufacturing, logistics, healthcare, and agriculture for automating tasks that require precise control and decision-making.
Problems Solved
1. Improved efficiency in task performance by robots. 2. Enhanced adaptability of robots to different environments and tasks.
Benefits
1. Increased productivity and accuracy in task execution. 2. Reduced human intervention in repetitive or dangerous tasks. 3. Flexibility in adapting to changing task requirements.
Potential Commercial Applications
Optimizing warehouse operations, automating assembly lines, enhancing medical procedures, and improving agricultural processes.
Possible Prior Art
One possible prior art could be the use of traditional control systems for robot automation before the introduction of machine learning models for control.
Unanswered Questions
How does the machine learning model adapt to new tasks or environments?
The article does not delve into the specific mechanisms through which the machine learning model can adapt to new tasks or environments.
What are the limitations of using population-based training operations versus reinforcement learning operations for training the machine learning model?
The article does not compare the effectiveness or limitations of using population-based training operations versus reinforcement learning operations for training the machine learning model.
Original Abstract Submitted
systems techniques to control a robot are described herein. in at least one embodiment, a machine learning model for controlling a robot is trained based at least on one or more population-based training operations or one or more reinforcement learning operations. once trained, the machine learning model can be deployed and used to control a robot to perform a task.
