17767675. MITIGATING REALITY GAP THROUGH TRAINING A SIMULATION-TO-REAL MODEL USING A VISION-BASED ROBOT TASK MODEL simplified abstract (GOOGLE LLC)
Contents
- 1 MITIGATING REALITY GAP THROUGH TRAINING A SIMULATION-TO-REAL MODEL USING A VISION-BASED ROBOT TASK MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MITIGATING REALITY GAP THROUGH TRAINING A SIMULATION-TO-REAL MODEL USING A VISION-BASED ROBOT TASK MODEL - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MITIGATING REALITY GAP THROUGH TRAINING A SIMULATION-TO-REAL MODEL USING A VISION-BASED ROBOT TASK MODEL
Organization Name
Inventor(s)
Kanishka Rao of Santa Clara CA (US)
Chris Harris of Los Altos CA (US)
Julian Ibarz of Mountain View CA (US)
Alexander Irpan of Palo Alto CA (US)
Seyed Mohammad Khansari Zadeh of San Carlos CA (US)
Sergey Levine of Berkeley CA (US)
MITIGATING REALITY GAP THROUGH TRAINING A SIMULATION-TO-REAL MODEL USING A VISION-BASED ROBOT TASK MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 17767675 titled 'MITIGATING REALITY GAP THROUGH TRAINING A SIMULATION-TO-REAL MODEL USING A VISION-BASED ROBOT TASK MODEL
Simplified Explanation
Implementations disclosed herein relate to mitigating the reality gap through training a simulation-to-real machine learning model (Sim2Real model) using a vision-based robot task machine learning model. The vision-based robot task machine learning model can be, for example, a reinforcement learning (RL) neural network model (RL-network), such as an RL-network that represents a Q-function.
- Vision-based robot task machine learning model: A machine learning model used to bridge the reality gap by training a simulation-to-real model.
- Reinforcement learning (RL) neural network model: Specifically, an RL-network that represents a Q-function, used in the vision-based robot task machine learning model.
Potential Applications
The technology can be applied in various fields such as robotics, autonomous vehicles, industrial automation, and computer vision.
Problems Solved
1. Bridging the reality gap between simulation and real-world environments. 2. Improving the performance and generalization of machine learning models in real-world scenarios.
Benefits
1. Enhanced training of machine learning models. 2. Increased efficiency and accuracy in real-world applications. 3. Reduction in development time and costs.
Potential Commercial Applications
Optimizing robot performance in industrial settings using vision-based machine learning models.
Possible Prior Art
Prior research has been conducted on bridging the reality gap in machine learning through simulation-to-real transfer learning techniques. Some studies have explored the use of reinforcement learning in robotics and computer vision tasks.
Unanswered Questions
How does the Sim2Real model compare to other simulation-to-real transfer learning methods in terms of performance and efficiency?
The article does not provide a direct comparison between the Sim2Real model and other simulation-to-real transfer learning methods in terms of performance and efficiency.
What are the limitations of using a vision-based robot task machine learning model in real-world applications?
The article does not address the potential limitations or challenges of implementing a vision-based robot task machine learning model in real-world applications.
Original Abstract Submitted
Implementations disclosed herein relate to mitigating the reality gap through training a simulation-to-real machine learning model (“Sim2Real” model) using a vision-based robot task machine learning model. The vision-based robot task machine learning model can be, for example, a reinforcement learning (“RL”) neural network model (RL-network), such as an RL-network that represents a Q-function.
