Hierarchical Multimodal RL Control for Oracle-Guided Navigation
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Solution Overview
Problem
Robotic agents navigating realistic virtual worlds face challenges in localizing audio sources due to their inability to hear audio cues, leading to unreliable navigation and increased time and errors in completing tasks, especially when instructions are noisy and complex, and they struggle to decide when to query an oracle for help.
Innovation Solution
An audio-visual-language (AVL) embodied navigation method using a hierarchical reinforcement learning framework that allows the agent to decide when to query an oracle and navigate based on audio-visual cues, implicitly modeling uncertainty and selecting the appropriate input modality from audio, visual, or natural language to efficiently reach a goal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entity queries the oracle frequently to get navigation instructions, then the navigation accuracy improves, but the agent autonomy deteriorates and time consumption increases
Solution Approach 1:
The system dynamically adjusts the querying frequency based on the entity's current state and uncertainty level. The first level controller learns optimal querying strategies through reinforcement learning, adapting the frequency of oracle queries to balance autonomy and accuracy requirements in different situational contexts.
Solution Approach 2:
The system changes the parameter of query frequency based on learned policies. The reinforcement learning framework optimizes the parameter of how often to query the oracle, transforming it from a fixed high-frequency approach to a dynamically optimized frequency that balances autonomy and navigation accuracy.
2Measurement precision
If the entity queries the oracle frequently to get navigation instructions, then the navigation accuracy improves, but the time consumption increases
Solution Approach 1:
The system dynamically adjusts query frequency based on real-time navigation needs and learned policies. The reinforcement learning controller optimizes when to query the oracle, adapting the timing and frequency to minimize time loss while maintaining navigation accuracy requirements.
Solution Approach 2:
Instead of continuously querying the oracle, the system applies partial action by querying only when necessary based on uncertainty thresholds and learned strategies. This reduces excessive querying and associated time consumption while maintaining sufficient navigation accuracy.
3Extent of automation
If the entity relies solely on audio-visual cues without querying, then the agent autonomy is maintained, but the navigation reliability deteriorates in complex environments
Solution Approach 1:
The oracle serves as an intermediary that provides additional navigation guidance when audio-visual cues are insufficient. The system introduces this intermediary selectively to improve reliability in complex environments while maintaining autonomy by not relying on it continuously.
Solution Approach 2:
The system changes the parameter of oracle utilization from binary (always/never query) to a continuous optimized frequency based on environmental complexity and task difficulty. The reinforcement learning framework learns optimal parameters for when and how much to rely on the oracle.
4Reliability
If the entity uses multiple modalities of input, then the navigation reliability improves, but the device complexity increases
Solution Approach 1:
The controller is segmented into two hierarchical levels: a first level controller that handles high-level decision-making about when to query the oracle, and second level controllers that handle specific navigation tasks using different modalities. This segmentation manages complexity by distributing processing responsibilities across hierarchical layers.
Solution Approach 2:
The hierarchical controller architecture provides multi-functionality by handling multiple modalities (audio, visual, language) through a unified framework. The same hierarchical structure manages diverse input types, reducing overall system complexity through universal processing principles.
Data Source
AI summary
A controller for controlling an entity is provided. The controller comprises a memory to store a hierarchical multimodal reinforcement learning (RL) neural network, and a processor. The hierarchical multimodal RL neural network includes a first level controller and two second level controllers. Each of the second level controllers comprise a first sub level controller relating to a first modality and a second sub level controller relating to a second modality. The first modality is different from the second modality. The processor is configured to select one of the two second level controllers to perform a first sub-task relating to a task, using the first level controller, based on input data and a state of the hierarchical multimodal RL neural network. The selected second level controller is configured to determine a set of control actions to perform the first sub-task, and control the entity based on the set of control actions.