Object State Vectors for Cross-View Robotic Object Association
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Solution Overview
Problem
Robotic systems face challenges in tracking and recognizing objects over time as they move in and out of the field of view, especially when objects are repositioned or viewed from different angles, leading to difficulties in maintaining accurate representations of their physical properties.
Innovation Solution
A system utilizing machine learning models, specifically a state vector generator and two ML models (comparator and object state ML models), to generate, maintain, and update state vectors representing objects' physical properties, allowing the robotic system to distinguish between new and previously seen objects and update their representations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object tracking methods are used, then the system can identify objects within the field of view, but it fails to maintain accurate object representations when objects move in and out of view or are viewed from different angles
Solution Approach 1:
The system performs preliminary actions by maintaining state vectors that encode object physical properties before the object is actually re-observed. When an object moves out of view, the state vector is preserved and updated with predicted information, allowing the system to quickly re-identify the object upon re-entry without requiring complete re-detection, thus improving reliability across different viewing conditions
Solution Approach 2:
The patent transitions from traditional 2D image-based object recognition to a multi-dimensional state vector representation that includes physical properties such as position, size, shape, and material characteristics. This dimensional expansion allows the system to identify objects across different views and time points by matching state vectors rather than relying solely on visual appearance, thereby improving both reliability and adaptability
2Measurement precision
If the system maintains detailed state vectors for all objects, then object recognition accuracy improves, but computational resources and data storage requirements increase
Solution Approach 1:
The system extracts only the essential physical properties of objects into state vectors (such as position, size, shape, and material characteristics) rather than maintaining complete object models or raw sensor data. This extraction approach preserves measurement precision for key identification features while significantly reducing computational complexity and data storage requirements compared to maintaining full object representations
Solution Approach 2:
The patent transforms complex object data into simplified state vectors with specific physical property parameters. By changing the representation from detailed images or point clouds to structured parameter vectors, the system achieves efficient computation and storage while maintaining sufficient precision for object identification and tracking across different viewing conditions
3Reliability
If the system updates state vectors frequently to track object movement, then tracking accuracy improves, but processing time and energy consumption increase
Solution Approach 1:
The system implements periodic updates to state vectors based on object movement detection rather than continuous updates. When an object is detected to have moved or changed view, the state vector is updated with new physical property information. This periodic approach maintains reliable tracking accuracy while reducing processing time and energy consumption compared to continuous update schemes
Data Source
AI summary
A method includes receiving sensor data representing a first object in an environment and generating, based on the sensor data, a first state vector that represents physical properties of the first object. The method also includes generating, by a first machine learning model and based on the first state vector and a second state vector that represents physical properties of a second object previously observed in the environment, a metric indicating a likelihood that the first object is the same as the second object. The method further includes determining, based on the metric, to update the second state vector and updating, by a second machine learning model configured to maintain the second state vector over time and based on the first state vector, the second state vector to incorporate into the second state vector information concerning physical properties of the second object as represented in the first state vector.