Self-Guided Vehicle Path Control From Natural Language Commands
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Solution Overview
Problem
Current systems for controlling self-guided vehicles using natural language commands are limited to discrete simulations and cannot operate in the continuous physical world, where an infinite number of configurations are possible, leading to ambiguity and inefficiency.
Innovation Solution
A system and method that receives natural language commands, determines absolute paths using a processor, and directs vehicles through a series of coordinates in the environment, incorporating image data from cameras, odometry, and IMU information for object detection, localization, clustering, and labeling, allowing for disambiguation of object references and generation of concise natural language descriptions.
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 simulation, allowing the robot to learn and practice language navigation commands in a discrete symbolic representation before deploying them in the continuous physical world. This copying approach enables transfer learning from simulation to reality without requiring the system to directly handle infinite continuous configurations during the learning phase.
Solution Approach 2:
The system introduces an intermediary layer that translates between discrete symbolic primitives used in simulation and continuous physical world coordinates. This intermediary translation mechanism allows the robot to operate in the continuous physical world by mediating between the simplified discrete representation and the complex continuous reality, resolving the contradiction between operational capability and system complexity.
2Measurement precision
If natural language commands are interpreted directly in discrete simulation, then language learning can proceed with limited configurations, but ambiguity arises when applying to continuous physical environments
Solution Approach 1:
The system adds a temporal dimension to the navigation process, using sequential natural language commands that describe paths through multiple time steps. This temporal dimension allows the system to disambiguate object references by considering the sequence of actions and their temporal relationships, rather than relying solely on spatial coordinates in the continuous physical world.
Solution Approach 2:
The system segments the continuous physical navigation task into discrete natural language command components, each describing a specific action or location reference. By segmenting the continuous path into discrete linguistic units, the system can process and disambiguate object references incrementally, maintaining precision while adapting to continuous configurations.
3Productivity
If a limited space of symbolic primitives is used, then learning is simpler and faster, but the system cannot handle the infinite number of configurations in the physical world
Solution Approach 1:
The system dynamically adapts its symbolic primitive representation based on the physical environment being navigated. Rather than using a fixed discrete set of primitives, the system dynamically generates or selects appropriate symbolic representations based on the current physical configuration, allowing it to handle infinite continuous configurations while maintaining the computational efficiency of symbolic processing.
Solution Approach 2:
The system changes the parameters of its symbolic representation to match the continuous physical world. Instead of using fixed discrete values, the symbolic primitives' parameters are continuously adjusted to reflect the actual physical configurations encountered during navigation, enabling the system to maintain productivity while adapting to unlimited physical world variations.
Data Source
AI summary
A system for directing the motion of a vehicle, comprising receiving commands in natural language using a processor, the commands specifying a relative path to be taken by the vehicle with respect to other objects in the environment; and determining an absolute path for the vehicle to follow based on the relative path using the processor, the absolute path comprising a series of coordinates in the environment; and directing the vehicle along the absolute path. Also provided is a system for training a lexicon of a natural language processing system, comprising receiving a data set containing a corpus of absolute paths driven by a vehicle annotated with natural language descriptions of the absolute paths using a processor, and determining parameters of the lexicon based on the data set.