18448049. TRAINING MACHINE LEARNING MODELS USING SIMULATION FOR ROBOTICS SYSTEMS AND APPLICATIONS simplified abstract (NVIDIA Corporation)

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 18448049 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 and renderings are performed using ray tracing algorithms or techniques.

Potential Applications

The technology can be applied in various industries such as manufacturing, logistics, healthcare, and agriculture for autonomous robot control systems.

Problems Solved

1. Improved efficiency and accuracy in robot control. 2. Enhanced adaptability of robots to different environments and tasks.

Benefits

1. Increased productivity and cost-effectiveness. 2. Reduced human intervention and errors in robot operations.

Potential Commercial Applications

Optimizing warehouse operations with autonomous robots for inventory management.

Possible Prior Art

One possible prior art could be the use of traditional control systems for robot operations, which may not be as adaptive or efficient as machine learning-based approaches.

How does this technology compare to traditional control systems for robot operations?

The article does not directly compare the technology to traditional control systems in terms of performance, efficiency, or adaptability.

What are the specific industries or use cases where this technology can have the most significant impact?

The article does not provide detailed information on the specific industries or use cases where the technology can be most beneficial.

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.