Indoor Robot Navigation From Natural Language Without Fine Maps
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Solution Overview
Problem
Current autonomous navigation systems for mobile robots are ineffective in new environments without pre-established fine-grained maps, as they rely on known coordinates and detailed geometry, limiting their ability to navigate immediately in GPS-denied or unfamiliar spaces.
Innovation Solution
A method using a pre-trained sequential prediction model that processes a navigation graph with undirected edges and natural language instructions to generate a sequence of single-step behaviors, allowing the robot to navigate from an initial location to a destination by representing the environment as a topological map with semantically meaningful locations and adaptive context generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fine-grained map is pre-established through scanning, then navigation accuracy is improved, but the system cannot navigate immediately in new environments without prior scanning
Solution Approach 1:
The system performs preliminary actions by pre-training a sequential prediction model on synthetic navigation data before deployment. This pre-trained model enables the robot to navigate new environments immediately without requiring prior scanning or fine-grained maps, resolving the contradiction between navigation accuracy and immediate adaptability to new environments
Solution Approach 2:
The system uses synthetic virtual environment data as a copy or approximation of real environments to train the navigation model. This allows the model to learn navigation patterns without requiring actual physical scanning, enabling immediate navigation in new environments while maintaining accuracy through learned spatial reasoning
2Productivity
If global path search algorithms are used, then complete path planning is achieved, but the system requires known destination coordinates and fine-grained maps
Solution Approach 1:
The system replaces traditional mechanical path search algorithms that require explicit map data and coordinate input with a learned sequential prediction model. This model directly predicts navigation actions based on natural language instructions and current observations, eliminating the dependency on pre-established fine-grained maps and known destination coordinates while maintaining complete path planning capability
Solution Approach 2:
The sequential prediction model acts as an intermediary between natural language instructions and navigation actions. Instead of requiring direct coordinate input and map data, the model translates high-level language commands into actionable navigation sequences, simplifying the system's dependencies while maintaining path planning completeness
Data Source
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AI summary
A scalable solution to robot behavioral navigation following natural language instructions is presented. An example of the solution includes: receiving, by a pre-trained sequential prediction model, a navigation graph of the task environment, instructions in natural language and an initial location of the robot in the navigation graph, wherein the navigation graph comprises nodes indicating locations in the task environment, coordinates of the nodes, and edges indicating connectivity between the locations; and predicting sequentially, by the pre-trained sequential prediction model, a sequence of single-step behaviors executable by the robot to navigate the robot from the initial location to a destination.