18157919. OPTIMIZING POLICY CONTROLLERS FOR ROBOTIC AGENTS USING IMAGE EMBEDDINGS simplified abstract (Google LLC)
OPTIMIZING POLICY CONTROLLERS FOR ROBOTIC AGENTS USING IMAGE EMBEDDINGS
Organization Name
Inventor(s)
YEVGEN Chebotar of Los Angeles CA (US)
Pierre Sermanet of Palo Alto CA (US)
Harrison Lynch of San Franciscco CA (US)
OPTIMIZING POLICY CONTROLLERS FOR ROBOTIC AGENTS USING IMAGE EMBEDDINGS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18157919 titled 'OPTIMIZING POLICY CONTROLLERS FOR ROBOTIC AGENTS USING IMAGE EMBEDDINGS
Simplified Explanation
The patent application describes systems, methods, and apparatus for optimizing a policy controller to control a robotic agent in performing a task in an environment. The optimization is achieved using a neural network that generates numeric embeddings of images of the environment and a demonstration sequence of images showing another agent performing a similar task.
- The patent application focuses on optimizing a policy controller for a robotic agent.
- The optimization is done using a neural network.
- The neural network generates numeric embeddings of images of the environment.
- The neural network also uses a demonstration sequence of images showing another agent performing a similar task.
- The goal is to improve the performance of the robotic agent in interacting with the environment and completing the task.
Potential Applications
This technology has potential applications in various fields, including:
- Robotics: Optimizing policy controllers for robotic agents can enhance their performance in tasks such as object manipulation, navigation, and assembly.
- Industrial Automation: Improved control of robotic agents can lead to more efficient and accurate automation processes in industries like manufacturing and logistics.
- Healthcare: Robotic agents can be optimized to assist in medical procedures, patient care, and rehabilitation exercises.
- Agriculture: Optimized policy controllers can enhance the capabilities of robotic agents in tasks like crop monitoring, harvesting, and pest control.
Problems Solved
The technology addresses the following problems:
- Suboptimal performance: By optimizing the policy controller, the technology aims to improve the performance of robotic agents in completing tasks in various environments.
- Lack of adaptability: The use of neural networks and demonstration sequences allows the policy controller to adapt to different environments and tasks.
- Efficiency and accuracy: The technology aims to enhance the efficiency and accuracy of robotic agents by optimizing their control mechanisms.
Benefits
The technology offers several benefits:
- Enhanced performance: Optimizing the policy controller can lead to improved task completion rates and overall performance of robotic agents.
- Adaptability: The use of neural networks and demonstration sequences enables the policy controller to adapt to different environments and tasks.
- Efficiency and accuracy: By optimizing the control mechanisms, the technology aims to increase the efficiency and accuracy of robotic agents.
- Versatility: The technology can be applied to various fields, allowing for a wide range of applications in robotics and automation.
Original Abstract Submitted
There are provided systems, methods, and apparatus, for optimizing a policy controller to control a robotic agent that interacts with an environment to perform a robotic task. One of the methods includes optimizing the policy controller using a neural network that generates numeric embeddings of images of the environment and a demonstration sequence of demonstration images of another agent performing a version of the robotic task.
