Visual-Textual Co-Grounded Navigation Agent for Progress Tracking
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Solution Overview
Problem
In Vision-and-Language Navigation tasks, agents lack explicit representations of navigation goals, making it difficult to determine progress and complete instructions in unknown environments without explicit map or image representations.
Innovation Solution
A self-aware navigation agent is developed with visual-textual co-grounding capabilities, utilizing a sequence-to-sequence architecture with attention and neural networks to simultaneously ground instructions and monitor progress, enabling the agent to identify completed and needed actions, and estimate navigation progress through textual and visual grounding modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the agent uses explicit map or image representations to represent navigation goals, then the agent can easily determine progress and complete instructions, but the agent cannot operate in unknown environments without such representations
Solution Approach 1:
The patent introduces an intermediary mechanism (visual-textual co-grounding module) that bridges the gap between having explicit goal representations and operating in unknown environments. This module grounds natural language instructions in visual observations, creating an implicit representation system that works without pre-existing maps or explicit goal images, thus resolving the contradiction between measurement precision and environment adaptability
Solution Approach 2:
The patent replaces the traditional mechanical approach of using explicit map/image representations with a cognitive approach using visual-textual co-grounding. Instead of relying on pre-built spatial models (mechanical system), the agent uses neural networks to dynamically align language instructions with visual observations, substituting the representation mechanism while maintaining progress determination capability
2Device complexity
If the agent uses simple navigation methods, then the device complexity is low, but the navigation accuracy and success rate in unseen environments is poor
Solution Approach 1:
The patent merges multiple functionality into a unified visual-textual co-grounding module that simultaneously performs instruction grounding, progress monitoring, and navigation decision-making. This integration achieves high navigation success rates in unseen environments while managing system complexity through shared neural network components and unified architecture
Solution Approach 2:
The patent creates a universal navigation agent that can operate across both seen and unseen environments using the same visual-textual co-grounding mechanism. The system performs multiple functions (instruction understanding, progress tracking, goal localization) through a single multi-functional architecture, achieving high reliability without proportionally increasing complexity
3Measurement precision
If the agent monitors navigation progress continuously, then the navigation accuracy improves, but the computational overhead and processing time increases
Solution Approach 1:
The patent implements continuous feedback through the progress monitoring module that uses visual-textual co-grounding to assess navigation status at each step. This feedback mechanism maintains high navigation accuracy by continuously aligning current observations with instruction goals, while the efficient neural network architecture minimizes processing time overhead
Data Source
AI summary
An agent for navigating a mobile automated system is disclosed herein. The navigation agent receives a navigation instruction and visual information for one or more observed images. The navigation agent is provided or equipped with self-awareness, which provides or supports the following abilities: identifying which direction to go or proceed by determining the part of the instruction that corresponds to the observed images (visual grounding), and identifying which part of the instruction has been completed or ongoing and which part is potentially needed for the next action selection (textual grounding). In some embodiments, the navigation agent applies regularization to ensures that the grounded instruction can correctly be used to estimate the progress made towards the navigation goal (progress monitoring).