Tidying Robot Panoptic Segmentation for Custom Object Categories
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Solution Overview
Problem
Conventional tidying robots organize objects into standard categories based on type and attributes, failing to meet users' needs for non-standard categorization, such as organizing objects into custom categories that match their preferences.
Innovation Solution
A method using a panoptic segmentation model to assign semantic labels, instance identifiers, and movability attributes to each pixel in a video feed, enabling detection and reidentification of static, movable, and tidyable objects, and integrating these into a global database for efficient navigation and operation by a tidying robot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional classification methods are used to organize objects, then the system is simple and fast, but it cannot meet users' needs for non-standard categories
Solution Approach 1:
The patent segments the object recognition process into multiple stages: initial classification for standard categories, followed by panoptic segmentation for detailed attribute analysis, and finally custom category assignment based on user preferences. This multi-stage segmentation allows the system to handle both simple and complex categorization needs without overwhelming complexity at any single stage.
Solution Approach 2:
The system dynamically adapts its categorization approach based on the situation. It begins with conventional classification for speed, then transitions to more complex panoptic segmentation when non-standard categories are needed. The system also dynamically updates object attributes and reclassifies objects as movable versus static based on detected changes, providing flexibility without permanent complexity.
2Adaptability or versatility
If panoptic segmentation model is used to detect object attributes, then non-standard categorization is enabled, but data processing time increases
Solution Approach 1:
The system performs preliminary classification using conventional methods to quickly identify standard categories. Only objects that require further differentiation undergo panoptic segmentation. This preliminary action filters out most objects that don't need complex analysis, reducing overall processing time while maintaining the ability to detect non-standard categories when necessary.
Solution Approach 2:
The system applies panoptic segmentation selectively to specific regions and objects rather than uniformly processing all images. It focuses computational resources on objects that exhibit attributes suggesting non-standard categorization needs, while using simpler processing for routine objects, thereby reducing total processing time.
3Measurement precision
If continuous monitoring of object positions is performed, then movable objects are detected accurately, but power consumption increases
Solution Approach 1:
The system uses periodic monitoring with variable intervals rather than continuous monitoring. It initially monitors object positions at regular intervals to establish baseline locations, then extends intervals when objects remain stationary. This periodic approach maintains detection accuracy for movable objects while significantly reducing power consumption compared to continuous monitoring.
Solution Approach 2:
The system leverages the objects' own characteristics to guide monitoring intensity. Objects that show movement patterns or attributes indicating they might be moved are monitored more closely, while stationary objects with no movement indicators are monitored less frequently. This self-service approach uses object behavior to automatically adjust monitoring resources, balancing accuracy and power consumption.
Data Source
AI summary
A method and computing apparatus are disclosed for allowing a tidying robot to organize objects into non-standard categories that match a user's needs. The tidying robot navigates an environment using cameras to map the type, size, and location of toys, clothing, obstacles, furniture, structural elements, and other objects. The robot comprises a neural network to determine the type, size, and location of objects based on input from a sensing system. An augmented reality view allows user interaction to refine and customize areas within the environment to be tidied, object categories, object home locations, and operational task rules controlling robot operations.