Nvidia corporation (20240300099). TECHNIQUES FOR TRAINING AND IMPLEMENTING REINFORCEMENT LEARNING POLICIES FOR ROBOT CONTROL simplified abstract
Contents
TECHNIQUES FOR TRAINING AND IMPLEMENTING REINFORCEMENT LEARNING POLICIES FOR ROBOT CONTROL
Organization Name
Inventor(s)
Bingjie Tang of Los Angeles CA (US)
Yashraj Shyam Narang of Seattle WA (US)
Fabio Tozeto Ramos of Seattle WA (US)
TECHNIQUES FOR TRAINING AND IMPLEMENTING REINFORCEMENT LEARNING POLICIES FOR ROBOT CONTROL - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240300099 titled 'TECHNIQUES FOR TRAINING AND IMPLEMENTING REINFORCEMENT LEARNING POLICIES FOR ROBOT CONTROL
The abstract describes a method for training a machine learning model to control a robot by simulating the robot's movements, computing errors, rewards, and observations, and updating the model based on these inputs.
- Simulation-based training method for machine learning model to control a robot
- Movement of robot model within simulation based on model outputs
- Computation of error, reward, and observation within simulation
- Updating machine learning model parameters based on rewards and observations
- Enhancing robot control through iterative training process
Potential Applications: - Robotics industry for developing advanced control systems - Autonomous vehicles for improving navigation and decision-making - Industrial automation for optimizing manufacturing processes
Problems Solved: - Enhances robot control capabilities through machine learning - Improves efficiency and accuracy of robot movements - Facilitates training of complex robotic systems
Benefits: - Increased precision and adaptability in robot control - Faster learning and optimization of robot behaviors - Potential for real-world applications in various industries
Commercial Applications: Title: "Advanced Robot Control System Training Method" This technology can be used in industries such as manufacturing, logistics, and healthcare for developing intelligent robotic systems. It can also be applied in research institutions for studying robot behaviors and capabilities.
Questions about the technology: 1. How does this method improve the efficiency of robot control systems? 2. What are the potential challenges in implementing this training method in real-world robotic applications?
Frequently Updated Research: Researchers are continuously exploring new algorithms and techniques to enhance the performance of machine learning models in controlling robots. Stay updated on advancements in reinforcement learning and robotic control systems for the latest developments in this field.
Original Abstract Submitted
one embodiment of a method for training a machine learning model to control a robot includes causing a model of the robot to move within a simulation based on one or more outputs of the machine learning model, computing an error within the simulation, computing at least one of a reward or an observation based on the error, and updating one or more parameters of the machine learning model based on the at least one of a reward or an observation.
