Object Detection in Discontinuous Space With Physics-Aware Learning
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Solution Overview
Problem
Existing reinforcement learning systems fail to effectively learn intuitive physics from limited demonstrations and do not consider human-like ability to predict outcomes of physical interactions with macroscopic objects, limiting their ability to generalize and adapt to new situations.
Innovation Solution
A system and method for object detection in discontinuous spaces using a multi-sensory approach, incorporating deep reinforcement learning, visual attention models, and meta-learning to facilitate learning from limited demonstrations, enabling adaptive sampling and robust object detection through curiosity-driven exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing reinforcement learning systems are used to learn from demonstrations, then the system can perform basic learning tasks, but the system fails to effectively learn intuitive physics from limited demonstrations and cannot predict outcomes of physical interactions
Solution Approach 1:
The patent introduces an intermediary component that bridges the gap between limited demonstrations and intuitive physics understanding. This intermediary processes visual observations and physical interactions to extract and store physics knowledge, which then informs object detection and prediction tasks, enabling the system to learn intuitive physics from limited data while maintaining accurate object detection.
2Adaptability or versatility
If the system explores complex physical interactions to learn intuitive physics, then the system gains better prediction capability, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex system into distinct modular components: an observation module that captures physical interactions, a knowledge base that stores physics principles, and a prediction module that applies learned knowledge. This segmentation allows the system to achieve high adaptability for predicting physical outcomes while managing complexity through organized, separable functional units.
3Reliability
If the system uses traditional object detection methods, then the detection process is simple and fast, but the system cannot handle discontinuous space and complex physical interactions
Solution Approach 1:
The patent implements a dynamic object detection system that adapts its complexity based on the task requirements. The system uses a physics-informed model that can switch between simple detection modes for straightforward cases and complex physics-based reasoning modes for scenarios involving discontinuous spaces or complex interactions, thereby achieving high reliability across diverse scenarios while managing computational complexity.
Data Source
AI summary
The present disclosure provides system and method for object detection in a discontinuous space. The system receives at least one captured image from one or more computing devices associated with one or more users. The at least one captured image comprises one or more objects in the discontinuous space, and the one or more objects are associated with at least one attribute. The system computes a score corresponding to each of at least one attribute of the one or more objects. The system detects the one or more objects in the discontinuous space based on the computed score. Further, the system determines a similarity grade for the one or more detected objects, where the similarity grade corresponds to an accuracy of inference for the one or more detected objects. Finally, the system updates a database based on the accuracy of inference to facilitate object detection in the discontinuous space.