Audio-Visual Navigation Scene Priors for Unseen Indoor Environments
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Solution Overview
Problem
Existing semantic audio-visual navigation systems struggle to generalize to unseen indoor visual scenes and unheard sounding objects, lacking explicit mechanisms for incorporating domain knowledge about object and region semantics, which limits their ability to navigate effectively in novel environments.
Innovation Solution
Incorporating knowledge-driven scene priors in the form of a knowledge graph that encodes object-region relations and using dual Graph Encoder Networks (GENs) within a reinforcement learning framework, combined with pre-training tasks, to enhance the navigation system's ability to reason about spatial relationships and navigate to novel sounding objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing semantic audio-visual navigation systems are used, then navigation in familiar environments is achieved, but generalization to unseen indoor visual scenes and unheard sounding objects fails
Solution Approach 1:
The system performs pre-training tasks before actual navigation, including learning object-region associations and spatial relationships from training data. This preliminary learning phase enables the model to acquire generalizable knowledge about indoor environments and sound sources, which is then applied during test-time navigation in unseen environments
Solution Approach 2:
The patent introduces an intermediate representation layer that maps audio-visual observations to semantic concepts through a learned embedding space. This intermediate representation acts as a mediator that bridges raw sensory inputs and navigation decisions, enabling generalization to novel environments by relying on semantic relationships rather than direct pixel-to-action mappings
2Adaptability or versatility
If domain knowledge about object and region semantics is not incorporated, then system complexity remains low, but ability to reason about spatial relationships is limited
Solution Approach 1:
The patent employs a unified Graph Encoder Network that handles multiple functions simultaneously: encoding spatial relationships, learning object-region associations, and reasoning about sound source locations. This single multi-functional component replaces what would otherwise require separate modules for each function, achieving high adaptability without proportionally increasing complexity
Solution Approach 2:
The system transforms the representation of spatial and semantic information by learning optimal embedding dimensions and graph structure parameters from data. Rather than hardcoding complex spatial reasoning rules, the model learns parameterized representations of relationships between objects, regions, and sound sources, enabling flexible adaptation to different environment types
3Adaptability or versatility
If more training data from diverse environments is used, then generalization improves, but training time and computational resources increase
Solution Approach 1:
The patent uses a curated subset of training data that focuses on diverse indoor environment types and sound source categories rather than exhaustively training on all possible scenarios. The pre-training phase uses this partial dataset to learn generalizable patterns, which then transfer effectively to unseen environments during test-time evaluation
Solution Approach 2:
The system performs preliminary pre-training on diverse indoor environments and sound sources before the actual navigation task. This two-stage approach separates general knowledge acquisition from task-specific learning, allowing the model to efficiently generalize to unseen houses without requiring extensive training time on every possible environment configuration
Data Source
AI summary
A method of controlling navigation of a device in an environment using machine learning (ML) models includes receiving visual and audio observation data of the environment as sensed by the device, determining classification scores for objects and regions in the environment based on the visual and audio observation data, encoding visual information based on the classification scores, determining audio-semantic feature embeddings based at least in part on the classification scores, the audio-semantic feature embeddings indicating spatial relationships between objects in the environment, between regions in the environment, and between objects and regions in the environment, and determining and outputting, based on the encoded visual information and the audio-semantic feature embeddings, a state representation corresponding to a state of the device within the environment.