Nvidia corporation (20240338598). TECHNIQUES FOR TRAINING MACHINE LEARNING MODELS USING ROBOT SIMULATION DATA simplified abstract

TECHNIQUES FOR TRAINING MACHINE LEARNING MODELS USING ROBOT SIMULATION DATA

Organization Name

nvidia corporation

Inventor(s)

Caelan Reed Garrett of Seattle WA (US)

Fabio Tozeto Ramos of Seattle WA (US)

Iretiayo Akinola of Seattle WA (US)

Alperen Degirmenci of Jersey City NJ (US)

Clemens Eppner of Seattle WA (US)

Dieter Fox of Seattle WA (US)

Tucker Ryer Hermans of Salt Lake City UT (US)

Ajay Uday Mandlekar of Cupertino CA (US)

Arsalan Mousavian of Seattle WA (US)

Yashraj Shyam Narang of Seattle WA (US)

Rowland Wilde O'flaherty of Seattle WA (US)

Balakumar Sundaralingam of Seattle WA (US)

Wei Yang of Lake Forest Park WA (US)

TECHNIQUES FOR TRAINING MACHINE LEARNING MODELS USING ROBOT SIMULATION DATA - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240338598 titled 'TECHNIQUES FOR TRAINING MACHINE LEARNING MODELS USING ROBOT SIMULATION DATA

The abstract describes a method for generating simulation data to train a machine learning model by creating simulation environments, tasks for a robot to perform within these environments, determining robot trajectories, and simulating the robot's movement according to these trajectories.

• Simulation data is generated based on user input for training a machine learning model.
• Multiple simulation environments are created, each with tasks for a robot to complete.
• Robot trajectories for these tasks are determined.
• The robot's movement within the simulation environments is simulated based on these trajectories.

Potential Applications: - Robotics training and development - Autonomous vehicle testing - Industrial automation optimization

Problems Solved: - Lack of diverse training data for machine learning models - Difficulty in generating realistic simulation environments for robots

Benefits: - Improved accuracy and efficiency of machine learning models - Cost-effective training data generation - Enhanced performance of robots in real-world scenarios

Commercial Applications: Title: "Advanced Simulation Data Generation for Machine Learning Training" This technology can be utilized in industries such as robotics, autonomous vehicles, and manufacturing for training and optimizing machine learning models to enhance operational efficiency and performance.

Questions about Simulation Data Generation for Machine Learning: 1. How does this method improve the training process for machine learning models? - This method enhances the training process by generating diverse and realistic simulation data for more accurate model training. 2. What are the potential cost savings associated with using simulated data for training machine learning models? - Using simulated data can significantly reduce the costs of data collection and labeling, making the training process more cost-effective.

Original Abstract Submitted

one embodiment of a method for generating simulation data to train a machine learning model includes generating a plurality of simulation environments based on a user input, and for each simulation environment included in the plurality of simulation environments: generating a plurality of tasks for a robot to perform within the simulation environment, performing one or more operations to determine a plurality of robot trajectories for performing the plurality of tasks, and generating simulation data for training a machine learning model by performing one or more operations to simulate the robot moving within the simulation environment according to the plurality of trajectories.