Ontology-Guided Scene Graphs for Reliable Indoor Robot Understanding
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Solution Overview
Problem
Existing cognitive robotic systems lack a dedicated knowledge base that can be updated and expanded using external knowledge and observations, leading to probability estimate errors and safety issues, and they struggle with semantic interoperability and human-robot interaction, especially in dynamic environments where ontologies need to be continuously updated and refined.
Innovation Solution
An ontology-guided indoor scene understanding system that updates ontologies online using external knowledge and observed information, enabling semantic navigation and human-robot interaction by generating scene graphs that are grounded in shared knowledge, allowing for enrichment of the knowledge base and its sharing across robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning systems are used for robotic decision making, then automation and adaptability improve, but reliability and safety deteriorate due to probability estimate errors
Solution Approach 1:
The patent introduces an ontology as an intermediary layer between machine learning perception and robotic decision-making. The ontology provides a structured knowledge base with predefined concepts, relationships, and constraints that guide and validate ML outputs, ensuring reliability while maintaining adaptability through the ability to update ontological knowledge.
Solution Approach 2:
The system performs preliminary action by pre-defining ontological frameworks, knowledge bases, and semantic structures before robotic operations begin. This pre-established semantic groundwork enables consistent and reliable interpretation of sensor data and decision-making scenarios, reducing errors while allowing flexibility through ontological extensions.
2Adaptability or versatility
If ontologies are updated continuously to reflect changing environments, then adaptability improves, but system complexity and consistency maintenance worsen
Solution Approach 1:
The patent implements feedback mechanisms where the robotic system continuously monitors environmental changes and updates the ontology accordingly. The feedback loop includes consistency checking and validation processes that ensure ontological updates maintain logical coherence, managing complexity through automated reasoning and conflict resolution protocols.
Solution Approach 2:
The ontology is segmented into modular components (classes, properties, relationships) that can be independently updated and managed. This segmentation allows selective refinement of specific ontological aspects without requiring complete system reconfiguration, reducing the burden of continuous updates while maintaining adaptability.
3Ease of operation
If a dedicated knowledge base is implemented for semantic interoperability, then human-robot interaction improves, but system complexity and data management burden increase
Solution Approach 1:
The patent creates a universal ontology framework that serves multiple functions simultaneously: semantic annotation of sensor data, decision-making guidance, human-robot communication bridge, and knowledge sharing across robotic systems. This multi-functionality reduces the need for separate specialized systems, managing complexity while improving ease of operation.
Solution Approach 2:
The system uses scene graphs as simplified copies or representations of the physical environment, encoded in ontological formats. These scene graphs capture essential semantic information without requiring complete environmental modeling, reducing data management complexity while enabling effective human-robot interaction through intuitive scene descriptions.
Data Source
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AI summary
Existing cognitive robotic applications follow a practice of building specific applications for specific use cases. However, the knowledge of the world and the semantics are common for a robot for multiple tasks. In this disclosure, to enable usage of knowledge across multiple scenarios, a method and system for ontology guided indoor scene understanding for cognitive robotic tasks is described where in scenes are processed based on techniques filtered based on querying ontology with relevant objects in perceived scene to generate a semantically rich scene graph. Herein, an initially manually created ontology is updated and refined in online fashion using external knowledge-base, human robot interaction and perceived information. This knowledge helps in semantic navigation, aids in speech, and text based human robot interactions. Further, in the process of performing the robotic tasks, the knowledgebase gets enriched, and the knowledge can be shared and used by other robots and services.