Google llc (20240217091). Shared Dense Network with Robot Task-Specific Heads simplified abstract
Organization Name
Inventor(s)
Michael Quinlan of Sunnyvale CA (US)
Sean Kirmani of San Francisco CA (US)
This abstract first appeared for US patent application 20240217091 titled 'Shared Dense Network with Robot Task-Specific Heads
Simplified Explanation:
The patent application describes a method for a robotic device to process image data from its camera using a trained dense network to accomplish a specific vision task, and then applying a task-specific head to generate an output for a different vision task, ultimately controlling the robot based on this output.
- Trained dense network used to process image data
- Task-specific head applied to generate output for a different vision task
- Robotic device controlled based on the output
Key Features and Innovation:
- Utilizes a trained dense network for image processing
- Implements a task-specific head for generating task-specific outputs
- Enables the robotic device to perform different vision tasks based on the processed data
Potential Applications:
- Robotics
- Artificial intelligence
- Computer vision
Problems Solved:
- Efficient processing of image data for robotic devices
- Adaptability to different vision tasks
- Enhanced control and operation of robotic devices in various environments
Benefits:
- Improved vision capabilities for robotic devices
- Enhanced performance in diverse tasks
- Increased efficiency in robotic operations
Commercial Applications:
- Autonomous robots for industrial settings
- Surveillance systems
- Assistive robots for healthcare
Prior Art:
Prior research in the field of computer vision and robotics may provide insights into similar methods and technologies.
Frequently Updated Research:
Stay updated on advancements in computer vision, artificial intelligence, and robotics to enhance the capabilities of the described method.
Questions about Robotic Vision Technology:
1. How does the trained dense network improve the processing of image data for robotic devices? 2. What are the potential implications of using task-specific heads in robotic vision tasks?
Original Abstract Submitted
a method includes receiving image data representing an environment of a robotic device from a camera on the robotic device. the method further includes applying a trained dense network to the image data to generate a set of feature values, where the trained dense network has been trained to accomplish a first robot vision task. the method additionally includes applying a trained task-specific head to the set of feature values to generate a task-specific output to accomplish a second robot vision task, where the trained task-specific head has been trained to accomplish the second robot vision task based on previously generated feature values from the trained dense network, where the second robot vision task is different from the first robot vision task. the method also includes controlling the robotic device to operate in the environment based on the task-specific output generated to accomplish the second robot vision task.
