Shared Dense Network with Task-Specific Heads for Robot Vision
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Solution Overview
Problem
Current robotic systems require separate networks for each task, leading to inefficient training and resource usage, as well as increased complexity and latency in processing image data for various robot vision tasks.
Innovation Solution
A shared dense network, such as a Feature Pyramid Network (FPN), is combined with task-specific heads to generate feature values for different robot vision tasks, allowing for efficient computation and quick adaptation to new tasks by leveraging previously trained models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate networks are used for each robot vision task, then each task can be specialized, but training time and computational resources increase significantly
Solution Approach 1:
The patent merges multiple task-specific networks into a single shared dense network that processes image data for multiple robot vision tasks simultaneously. This shared network is trained once to learn common features across different tasks, eliminating the need for separate training of multiple independent networks while maintaining task-specific performance through shared feature extraction.
Solution Approach 2:
The shared dense network is designed with multi-functionality to handle various robot vision tasks including object detection, segmentation, and classification. By creating a universal network that can perform multiple tasks, the system reduces training time and computational overhead compared to specialized single-task networks.
2Reliability
If separate networks are used for each robot vision task, then each task can be optimized, but device complexity increases
Solution Approach 1:
The patent combines multiple separate task-specific networks into a single shared dense network architecture. This consolidation reduces device complexity by eliminating redundant components while maintaining the ability to perform multiple vision tasks through a unified network structure that shares feature extraction capabilities.
3Adaptability or versatility
If separate networks are used for each robot vision task, then each task can be independently trained, but computational resources and processing latency increase
Solution Approach 1:
The patent merges multiple independent network processing streams into a single shared dense network that processes image data once for multiple tasks. This approach reduces computational resource usage and energy consumption by eliminating redundant processing while maintaining task independence through separate task-specific output layers that branch from the shared feature extraction network.
Solution Approach 2:
The shared dense network performs preliminary feature extraction and processing that benefits all subsequent tasks. By pre-processing the image data once to extract common features, the system reduces the computational burden on individual task processing stages, thereby lowering overall energy consumption and processing latency.
4Reliability
If separate networks are used for each robot vision task, then each task can be specialized, but processing latency increases
Solution Approach 1:
The patent combines multiple task processing operations into a single shared dense network that processes image data in one pass. This merging of operations reduces processing latency by eliminating the sequential execution of multiple separate networks, allowing parallel task processing from shared features while maintaining high accuracy through task-specific output layers.
Data Source
AI summary
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.