Nvidia corporation (20240300100). TECHNIQUES FOR DEPLOYING TRAINED MACHINE LEARNING MODELS FOR ROBOT CONTROL simplified abstract
Contents
TECHNIQUES FOR DEPLOYING TRAINED MACHINE LEARNING MODELS FOR ROBOT CONTROL
Organization Name
Inventor(s)
Yashraj Shyam Narang of Seattle WA (US)
Ankur Handa of Seattle WA (US)
Karl Van Wyk of Issaquah WA (US)
Michael Andres Lin of San Mateo CA (US)
Fabio Tozeto Ramos of Seattle WA (US)
TECHNIQUES FOR DEPLOYING TRAINED MACHINE LEARNING MODELS FOR ROBOT CONTROL - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240300100 titled 'TECHNIQUES FOR DEPLOYING TRAINED MACHINE LEARNING MODELS FOR ROBOT CONTROL
The abstract of the patent application describes a method for controlling a robot using sensor data, machine learning models, and target states.
- Receiving sensor data indicating the robot's state
- Generating an action based on the sensor data and a machine learning model
- Computing a target state based on the action and a previous target state
- Causing the robot to move based on the target state
Potential Applications: - Industrial automation - Autonomous vehicles - Robotics in healthcare - Surveillance systems - Agricultural robotics
Problems Solved: - Enhancing robot control accuracy - Improving efficiency in robot movements - Adapting to changing environments - Reducing human intervention in robot operations
Benefits: - Increased productivity - Enhanced safety - Cost savings - Scalability in operations - Real-time decision-making capabilities
Commercial Applications: Title: "Advanced Robot Control System for Enhanced Efficiency" This technology can be utilized in manufacturing plants, warehouses, logistics companies, and research institutions to streamline operations, improve accuracy, and reduce manual labor costs.
Questions about Robot Control Systems: 1. How does this technology improve the efficiency of robot movements? - The technology uses sensor data and machine learning models to optimize actions and target states, resulting in more precise and efficient robot movements.
2. What are the potential applications of this advanced robot control system? - The system can be applied in various industries such as manufacturing, healthcare, agriculture, and surveillance for automation and improved operational efficiency.
Original Abstract Submitted
one embodiment of a method for controlling a robot includes receiving sensor data indicating a state of the robot, generating an action based on the sensor data and a trained machine learning model, computing a target state of the robot based on the action and a previous target state of the robot, and causing the robot to move based on the target state of the robot.
