Self-Guided Vehicle Path Control From Relative Language Commands
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Solution Overview
Problem
Existing vehicle control systems that use natural language to direct self-guided vehicles are limited to discrete simulations and cannot operate in the continuous physical world, where an infinite number of configurations are possible.
Innovation Solution
A system and method that receive natural language commands to specify relative paths with respect to other objects in the environment, determine an absolute path using a processor, and direct the vehicle along the absolute path, while also training a lexicon and generating natural language descriptions of vehicle paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If discrete symbolic primitives are used to represent robot actions and positions, then the system can learn language meanings in simulation, but it cannot operate in the continuous physical world where infinite configurations are possible
Solution Approach 1:
The system creates a virtual copy of the physical environment through simulated views that mirror the camera's perspective. This virtual model allows the system to process and understand natural language commands about spatial relationships without directly handling the complexity of the continuous physical world. The virtual copy serves as an intermediary representation that preserves the essential spatial structure while being computationally manageable.
Solution Approach 2:
The system introduces an intermediary processing layer that translates natural language commands into spatial transformations applied to the virtual model. This intermediary layer includes components that parse language, identify spatial relationships, and compute transformations without requiring direct interpretation of continuous physical configurations. The intermediary handles the complexity by breaking down the problem into discrete linguistic and spatial processing steps.
2Reliability
If the system uses internal simulation representation to obtain discrete symbolic primitives, then it can learn word meanings in context, but it cannot generalize to the infinite configurations of the real world
Solution Approach 1:
The system changes the parameter representation from discrete symbolic values to continuous spatial parameters. Instead of using discrete symbolic primitives for position and orientation, the system uses continuous coordinates and transformation parameters that can represent any configuration in the physical world. This parameter change allows the system to maintain accurate language understanding while generalizing to infinite real-world configurations.
Solution Approach 2:
The system transitions from static discrete symbolic representations to dynamic continuous transformations. The virtual model and camera views are dynamically updated based on the robot's changing position and orientation in the real world. This dynamic approach allows the system to maintain reliable language understanding while adapting to any configuration that occurs in the continuous physical environment.
Data Source
AI summary
Training a lexicon of a natural language processing system may include receiving a data set containing a corpus of absolute paths driven by a vehicle annotated with natural language descriptions of said absolute paths and determining parameters of the lexicon based on the data set. The degree to which a path taken by the vehicle satisfies the annotated description may be specified by a scoring function. The lexicon may be determined by finding the lexicon parameters that optimize the degree to which the paths taken by the vehicle satisfy the annotated descriptions. Objects in the environment of the same class are disambiguated by specifying their position relative to other objects using prepositions.