Google llc (20240253215). TRAINING WITH HIGH FIDELITY SIMULATIONS AND HIGH SPEED LOW FIDELITY SIMULATIONS simplified abstract
Contents
- 1 TRAINING WITH HIGH FIDELITY SIMULATIONS AND HIGH SPEED LOW FIDELITY SIMULATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAINING WITH HIGH FIDELITY SIMULATIONS AND HIGH SPEED LOW FIDELITY SIMULATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about the Technology
- 1.11 Original Abstract Submitted
TRAINING WITH HIGH FIDELITY SIMULATIONS AND HIGH SPEED LOW FIDELITY SIMULATIONS
Organization Name
Inventor(s)
Matthew Bennice of San Jose CA (US)
Paul Bechard of Ogdensburg NY (US)
Joséphine Simon of San Francisco CA (US)
Jiayi Lin of Sunnyvale CA (US)
TRAINING WITH HIGH FIDELITY SIMULATIONS AND HIGH SPEED LOW FIDELITY SIMULATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240253215 titled 'TRAINING WITH HIGH FIDELITY SIMULATIONS AND HIGH SPEED LOW FIDELITY SIMULATIONS
Simplified Explanation
The patent application describes a method for training a robot control policy using simulated data of varying fidelities.
- The robot control policy is initially trained using data from simulations of different fidelities.
- When certain criteria are met, a second training phase commences with a new set of training data.
- The second set of data includes a mix of simulated data from both low and high fidelity simulations.
Key Features and Innovation
- Training a robot control policy using data from simulations of different fidelities.
- Transitioning to a second training phase based on specific criteria being met.
- Incorporating a mix of low and high fidelity simulated data in the second training phase.
Potential Applications
This technology can be applied in various industries such as robotics, automation, and artificial intelligence for enhancing robot control policies.
Problems Solved
- Efficient training of robot control policies using simulated data.
- Improving the performance of robot control policies by incorporating data from simulations of varying fidelities.
Benefits
- Enhanced performance and adaptability of robot control policies.
- Cost-effective training process using simulated data.
- Improved accuracy and reliability of robot control in real-world applications.
Commercial Applications
- "Enhanced Robot Control Policy Training Method" can be utilized in industries such as manufacturing, logistics, and healthcare for optimizing robot operations and increasing efficiency.
Questions about the Technology
What are the potential drawbacks of using simulated data for training robot control policies?
Simulated data may not fully capture the complexities of real-world scenarios, leading to potential performance gaps in actual robot operations.
How can the technology be further improved to enhance the accuracy of robot control policies?
Further research and development could focus on refining the fidelity of simulations and incorporating more diverse training scenarios to improve the accuracy of robot control policies.
Original Abstract Submitted
implementations are provided for training a robot control policy for controlling a robot. during a first training phase, the robot control policy is trained using a first set of training data that includes (i) training data generated based on simulated operation of the robot in a first fidelity simulation, and (ii) training data generated based on simulated operation of the robot in a second fidelity simulation, wherein the second fidelity is greater than the first fidelity. when one or more criteria for commencing a second training phase are satisfied, the robot control policy is further trained using a second set of training data that also include training data generate based on simulated operation of the robot in the first and second fidelity simulations, which has a ratio therebetween lower than that in the first set of training data.
