Robotic Sensing Validation With Real-Time Feedback Learning
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Solution Overview
Problem
Robotic sensing systems often fail to accurately detect and classify objects in real-life scenarios due to challenges like lighting, occlusion, and view angle, and consumers lack the ability to evaluate their accuracy and confidence, which is critical for safe operation.
Innovation Solution
A real-time interface that allows robots to interact with humans or other devices for feedback on object detection accuracy and confidence, using voice-based commands and natural language processing to improve their sensing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robotic sensing systems operate autonomously without human feedback, then operational speed and automation are improved, but object detection accuracy and reliability deteriorate in complex real-life scenarios
Solution Approach 1:
The system implements a feedback mechanism where humans provide corrections to robot detections in real-time. The robot presents detected objects to the human, receives feedback on accuracy, and uses this feedback to continuously improve its detection model, resolving the contradiction between autonomous operation and detection reliability.
Solution Approach 2:
The robot performs self-improvement by automatically updating its detection model with feedback received from humans. The system autonomously retrains its object detection algorithms using the feedback data, enabling continuous self-enhancement of detection accuracy while maintaining autonomous operation.
2Measurement precision
If robotic sensing systems use complex detection models to improve object classification accuracy, then detection precision is improved, but processing time and computational resources increase
Solution Approach 1:
The system uses a two-stage detection approach where a faster, less complex model performs initial detection, and a more accurate but slower model processes only uncertain or critical detections. This partial application of complex processing maintains high accuracy for important cases while minimizing overall processing time.
3Productivity
If robotic sensing systems deploy pre-trained detection models, then deployment speed is improved, but adaptability to new environments and objects deteriorates
Solution Approach 1:
The detection model transitions from a static pre-trained state to a dynamic, continuously adapting system. The model maintains its pre-trained foundation for rapid deployment but dynamically updates its parameters and knowledge base through continuous feedback from human interactions in the specific deployment environment, achieving both fast deployment and high adaptability.
Data Source
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AI summary
Disclosed herein are systems, devices, and apparatuses for improved perception/sensing systems in robots or other vehicles. The system receives sensor data representative of a field of view of a robot and determines, based on the sensor data and an object detection model, an identification of an object within the field of view and an accuracy metric of the identification of the object, wherein the object detection model relates the sensor data to the identification. The system also requests, based on the accuracy metric, an informational feedback from the identification of the object and updates the object detection model to an updated object detection model based on the informational feedback.