Google llc (20240118667). MITIGATING REALITY GAP THROUGH TRAINING A SIMULATION-TO-REAL MODEL USING A VISION-BASED ROBOT TASK MODEL simplified abstract

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MITIGATING REALITY GAP THROUGH TRAINING A SIMULATION-TO-REAL MODEL USING A VISION-BASED ROBOT TASK MODEL

Organization Name

google llc

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 20240118667 titled 'MITIGATING REALITY GAP THROUGH TRAINING A SIMULATION-TO-REAL MODEL USING A VISION-BASED ROBOT TASK MODEL

Simplified Explanation

The patent application relates to training a simulation-to-real machine learning model, specifically a "sim2real" model, using a vision-based robot task machine learning model, such as a reinforcement learning neural network model.

  • The vision-based robot task machine learning model could be a reinforcement learning (RL) neural network model, like an RL-network representing a Q-function.
    • Potential Applications:
  - Robotics: Improving the performance of robots in real-world scenarios by training them in simulations first.
  - Autonomous Vehicles: Enhancing the capabilities of self-driving cars through simulated training.
    • Problems Solved:
  - Bridging the reality gap between simulated environments and real-world applications.
  - Improving the transferability of machine learning models from simulation to reality.
    • Benefits:
  - Increased efficiency in training machine learning models for robotics applications.
  - Enhanced performance and adaptability of robots in real-world tasks.
    • Potential Commercial Applications:
  - Manufacturing: Optimizing robotic systems for production lines.
  - Healthcare: Improving robotic assistance in medical procedures.
    • Possible Prior Art:
  - Prior research on sim-to-real transfer learning in robotics.
  - Existing methods for training reinforcement learning models in simulated environments.
      1. Unanswered Questions:
        1. How does the sim2real model handle variations in real-world conditions that were not present in the simulation?

The abstract does not specify how the sim2real model adapts to unforeseen circumstances or changes in the environment during deployment.

        1. What are the computational requirements for training and deploying the vision-based robot task machine learning model?

The abstract does not provide information on the computational resources needed to train and implement the proposed machine learning model in practical 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.

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