Hierarchical Multimodal RL Robot Control for Audio Navigation
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Solution Overview
Problem
Robots struggle to navigate reliably in realistic environments due to sporadic and intermittent audio cues, leading to increased time and errors in navigation tasks, and interactions with oracles often compromise autonomy or result in inefficient exploration.
Innovation Solution
A bi-directional audio-visual-language embodied navigation (BI-AVLEN) technique using a hierarchical multimodal reinforcement learning neural network that allows robots to decide when to use audio-visual cues or seek oracle guidance, enabling seamless interaction with humans for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot frequently interacts with the oracle for navigation guidance, then navigation reliability improves, but robot autonomy deteriorates
Solution Approach 1:
The patent introduces an intermediary communication system between the robot and oracle that enables selective information exchange. The robot can request navigation guidance when needed while maintaining autonomous operation during confident navigation phases. This intermediary layer resolves the contradiction by allowing reliability improvement through oracle interaction without compromising overall autonomy, as the robot decides when to seek guidance rather than continuously依赖ing on the oracle.
2Measurement precision
If the robot frequently interacts with the oracle, then navigation accuracy improves, but time efficiency deteriorates
Solution Approach 1:
The patent implements partial interaction with the oracle by selecting specific moments when navigation guidance is truly necessary rather than seeking guidance continuously. The robot assesses its confidence level in autonomous navigation and only engages the oracle when uncertainty exceeds a threshold. This partial action approach improves navigation accuracy at critical decision points while avoiding time loss from unnecessary oracle interactions during confident navigation phases.
3Device complexity
If the robot restricts input modalities for navigation, then system complexity reduces, but navigation productivity deteriorates
Solution Approach 1:
The patent implements a multi-modal input system that universally accepts multiple types of input (audio cues, visual information, natural language instructions, oracle guidance) through a unified processing framework. Rather than requiring separate systems for each modality, the patent creates a universal navigation system that can process diverse inputs and select the most appropriate modality based on the current situation. This multi-functionality approach improves navigation productivity by leveraging multiple input sources while keeping system complexity manageable through integrated processing.
Data Source
AI summary
A controller for controlling a robot is provided. The controller comprises a hierarchical multimodal reinforcement learning (RL) neural network including a first level controller and three second level controllers. The second level controllers comprise a first sub level controller to receive input data based on predefined questions, a second sub level controller to receive the input data by generating a validation question based on state of the RL neural network and a third sub level controller to determine the input data based on state of the RL neural network. The controller is configured to select one of the second level controllers using the first level controller to perform a first interaction relating to a task based on the state of the RL neural network; generate a control command using the selected second level controller based on the corresponding input data; and control operation of the robot by executing control command.