Nvidia corporation (20240095527). TRAINING MACHINE LEARNING MODELS USING SIMULATION FOR ROBOTICS SYSTEMS AND APPLICATIONS simplified abstract

TRAINING MACHINE LEARNING MODELS USING SIMULATION FOR ROBOTICS SYSTEMS AND APPLICATIONS

Organization Name

nvidia corporation

Inventor(s)

Ankur Handa of San Jose CA (US)

Gavriel State of Toronto (CA)

Arthur David Allshire of Toronto (CA)

Dieter Fox of Seattle WA (US)

Jean-Francois Victor Lafleche of Toronto (CA)

Jingzhou Liu of Oakville (CA)

Viktor Makoviichuk of Santa Clara CA (US)

Yashraj Shyam Narang of Seattle WA (US)

Aleksei Vladimirovich Petrenko of Cupertino CA (US)

Ritvik Singh of Toronto (CA)

Balakumar Sundaralingam of Seattle WA (US)

Karl Van Wyk of Issaquah WA (US)

Alexander Zhurkevich of San Jose CA (US)

TRAINING MACHINE LEARNING MODELS USING SIMULATION FOR ROBOTICS SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240095527 titled 'TRAINING MACHINE LEARNING MODELS USING SIMULATION FOR ROBOTICS SYSTEMS AND APPLICATIONS

Simplified Explanation

The abstract describes systems and techniques for training machine learning models for controlling a robot, based on simulations and renderings using ray tracing algorithms.

• Machine learning models are trained for robot control based on simulations and renderings.
• Simulations of the robot are performed using ray tracing algorithms.
• Training is done using one or more machine learning models.

Potential Applications

This technology can be applied in various fields such as robotics, automation, and artificial intelligence.

Problems Solved

This technology helps in improving the control and decision-making capabilities of robots, leading to more efficient and effective performance in various tasks.

Benefits

The use of machine learning models trained on simulations can enhance the accuracy and adaptability of robot control systems.

Potential Commercial Applications

This technology can be utilized in industries such as manufacturing, logistics, healthcare, and defense for automating processes and improving productivity.

Possible Prior Art

Prior art may include research papers or patents related to machine learning in robotics, simulation-based training of robots, and ray tracing algorithms in computer graphics.

=== What are the specific ray tracing algorithms used in the simulations? The abstract mentions the use of ray tracing algorithms, but it does not specify which specific algorithms are employed in the simulations.

=== How does the training process differ when using simulations versus real-world data? The abstract highlights training based on simulations, but it does not elaborate on the differences in the training process compared to using real-world data.

Original Abstract Submitted

systems and techniques are described related to training one or more machine learning models for use in control of a robot. in at least one embodiment, one or more machine learning models are trained based at least on simulations of the robot and renderings of such simulations—which may be performed using one or more ray tracing algorithms, operations, or techniques.