18605464. Shared Dense Network with Robot Task-Specific Heads simplified abstract (Google LLC)
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 18605464 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 a camera, using a trained dense network and task-specific head to accomplish different robot vision tasks.
- The method involves receiving image data from a camera on the robotic device.
- A trained dense network is applied to the image data to generate a set of feature values for a first robot vision task.
- A trained task-specific head is then applied to the feature values to generate a task-specific output for a second robot vision task.
- The robotic device is controlled based on the task-specific output to operate in the environment effectively.
Key Features and Innovation:
- Utilization of a trained dense network and task-specific head for processing image data in robotic vision tasks.
- Ability to accomplish different robot vision tasks using the same set of feature values.
- Enhanced efficiency and accuracy in robotic device operations based on the processed image data.
Potential Applications: The technology can be applied in various industries such as manufacturing, healthcare, agriculture, and surveillance for tasks requiring advanced vision capabilities.
Problems Solved:
- Streamlining image data processing for robotic devices.
- Enhancing the adaptability of robotic devices to different vision tasks.
- Improving the overall performance and efficiency of robotic operations.
Benefits:
- Increased accuracy and precision in robotic vision tasks.
- Enhanced productivity and automation capabilities in various industries.
- Cost-effective solution for implementing advanced vision systems in robotic devices.
Commercial Applications: The technology can be utilized in automated manufacturing processes, medical imaging systems, agricultural robotics, and security surveillance systems, leading to improved efficiency and productivity in these sectors.
Prior Art: Readers can explore prior research on deep learning in robotics, computer vision algorithms, and artificial intelligence applications in robotic vision tasks to understand the background of this technology.
Frequently Updated Research: Stay updated on advancements in deep learning models for robotic vision tasks, new applications of computer vision in robotics, and the integration of artificial intelligence in robotic systems.
Questions about Robotic Vision Technology: 1. How does this technology improve the efficiency of robotic operations? 2. What are the potential challenges in implementing this technology in real-world robotic systems?
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.
