Indoor Robot Navigation From Natural Language Using Topological Maps
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Solution Overview
Problem
Current autonomous navigation systems for mobile robots rely on pre-established fine-grained maps and known coordinates, making it difficult for robots to navigate in new environments without a pre-existing map or known destination.
Innovation Solution
A method using a pre-trained sequential prediction model that receives a navigation graph and natural language instructions to predict a sequence of single-step behaviors for a robot to navigate from an initial location to a destination, employing a topological map representation and adaptive context generation to adapt to changing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fine-grained map and search algorithm are used for autonomous navigation, then navigation accuracy is improved, but the system cannot operate in new environments without pre-established maps
Solution Approach 1:
The system performs preliminary scanning to build a coarse-grained map of the environment before navigation. This pre-established topological structure enables the robot to navigate new environments without requiring pre-existing fine-grained maps, resolving the contradiction between navigation accuracy and environment adaptability
Solution Approach 2:
The navigation system segments the environment into coarse-grained topological structures (nodes and edges) rather than using continuous fine-grained maps. This segmentation allows the system to capture essential navigation information while adapting to new environments efficiently, balancing accuracy and adaptability
2Measurement precision
If natural language instructions are processed sequentially to generate navigation paths, then instruction following accuracy is improved, but navigation time increases
Solution Approach 1:
The natural language instruction is segmented into individual action tokens that can be processed and executed sequentially. This segmentation allows the system to maintain high instruction following accuracy while enabling parallel processing of individual tokens, thereby reducing overall navigation time
Solution Approach 2:
The system processes only the necessary portion of the navigation path at each step rather than computing the entire path in advance. By generating navigation actions token-by-token based on current context, the system achieves accurate instruction following without the computational overhead of complete path planning, reducing navigation time
Data Source
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.