Robot Object Placement via Visual Cue Correlation
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Solution Overview
Problem
Existing robotic systems face challenges in placing objects in dynamic environments, as they often rely on predetermined coordinates, stability analysis, or human data, which may not be effective in unpredictable settings.
Innovation Solution
A method that uses visual cues from multiple images to determine a common cue's correlation with object placement, allowing a robotic device to adaptively place objects in dynamic environments by identifying relevant visual cues and adjusting placement based on machine learning models and human feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predetermined coordinates or stability analysis are used for object placement, then the placement process is simple and deterministic, but the system cannot adapt to dynamic or unpredictable environments
Solution Approach 1:
The system captures images of the environment, identifies visual cues, and uses their spatial relationships to dynamically determine object placement locations. This feedback loop allows the robot to adapt to different environments without predetermined coordinates, resolving the contradiction between adaptability and complexity by using environmental feedback rather than complex predictive models
Solution Approach 2:
The patent replaces traditional mechanical positioning systems (predetermined coordinates, stability analysis algorithms) with a vision-based system that identifies visual cues and uses machine learning to determine placement locations. This substitution reduces the need for complex mechanical positioning while improving adaptability to dynamic environments
2Measurement precision
If human data about object placement is analyzed, then placement accuracy may improve, but the system requires extensive data collection and processing time
Solution Approach 1:
The system pre-identifies visual cues in the environment and establishes their spatial relationships before object placement is needed. By preparing this visual landmark map in advance, the system can quickly determine placement locations without extensive real-time data processing, thus improving both accuracy and reducing time loss
Solution Approach 2:
Instead of analyzing extensive human placement data, the system creates a simplified representation by copying and utilizing natural visual cues already present in the environment. This approach achieves placement accuracy through environmental imitation rather than statistical analysis of human behavior, significantly reducing data processing requirements
3Adaptability or versatility
If visual cues from multiple images are analyzed to determine placement locations, then adaptability to new configurations improves, but the complexity of image processing and cue identification increases
Solution Approach 1:
The system uses a universal approach by identifying common visual cues (tables, chairs, countertops) that appear across multiple images and environments. By focusing on these universal landmarks rather than environment-specific features, the system achieves broad adaptability while keeping the image processing complexity manageable through pattern recognition of common objects
Data Source
AI summary
A method may include obtaining first images, each image including an object, and determining a set of one or more visual cues for each. The method may include selecting a common visual cue of the one or more visual cues that is common to each set of one or more visual cues determined for each corresponding image and determining a correlation between a location of the common visual cue in each image of the first images and a location of the object in each image of the first images. The method may include obtaining a second image of an environment and identifying the common visual cue in the second image. The method may include determining a placement location for the object in the environment based on the correlation and a location of the common visual cue in the second image.