Mobile Navigation Control Using Open-Vocabulary Language Embeddings
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Solution Overview
Problem
Existing navigation systems often require rigid user input paradigms for destination specification, leading to potential misinterpretation of user intentions and inefficiencies in navigation.
Innovation Solution
A multi-modal model trained on image-language pairs is used to generate a map of an environment based on image inputs and to process language inputs, allowing for open vocabulary navigation by converting inputs into embeddings in a shared embedding space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rigid user input paradigms are used for destination specification, then navigation system operation is simplified, but user intention interpretation accuracy deteriorates
Solution Approach 1:
The system changes the parameter of input processing from rigid structured formats to flexible natural language by transforming user inputs into embedding representations. This allows the system to interpret diverse language expressions (different parameters of user intent) while maintaining consistent navigation functionality, resolving the contradiction between operational simplicity and interpretation accuracy.
Solution Approach 2:
The patent introduces an embedding-based intermediate representation layer between user language input and navigation processing. This intermediary transforms diverse natural language inputs into a unified embedding space, enabling accurate interpretation of user intentions without requiring rigid input paradigms, thus resolving the contradiction.
2Measurement precision
If semantic understanding of the environment is implemented, then navigation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential spatial and navigational information from environmental data, representing it through embeddings rather than full semantic understanding. This extraction approach maintains navigation accuracy by preserving key spatial relationships while removing unnecessary computational complexity associated with comprehensive semantic processing.
Solution Approach 2:
The system replaces complex semantic understanding mechanisms with embedding-based representations. Instead of implementing full semantic parsing and environmental comprehension, the patent uses learned embedding vectors to capture essential navigational information, significantly reducing computational complexity while maintaining accuracy.
3Adaptability or versatility
If open vocabulary navigation is enabled, then user input flexibility is improved, but processing complexity increases
Solution Approach 1:
The patent implements a universal embedding space that can handle diverse vocabulary and language expressions through a single processing framework. This multi-functional approach allows the system to process various types of natural language inputs (different vocabularies and expressions) using the same embedding-based mechanism, achieving flexibility without proportionally increasing processing complexity.
Data Source
AI summary
Aspects of the present disclosure relate to systems and methods for mobile device control using language input.