Semantic Parsing System for Robotic Arm Control
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Solution Overview
Problem
Current systems for controlling robotic arms with spoken commands face challenges in accurately interpreting complex natural language commands, leading to misinterpretation and potential dangerous or nonsensical executions, due to the limitations of generic semantic parsers in processing varied and complex command types.
Innovation Solution
A supervised semantic parsing system that employs a tag and parse approach, separating semantic tagging, parsing, reference resolution, and spatial validation stages, using machine learning models to improve the accuracy of command interpretation and execution, such as training maximum entropy sequence taggers and parsers to generate Robot Control Language (RCL) parses for robotic arm control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generic semantic parsers are used to process natural language commands, then the system is simple and easy to implement, but the accuracy of command interpretation deteriorates leading to misinterpretation
Solution Approach 1:
The system segments the natural language processing task into distinct stages: semantic tagging, parsing, reference resolution, and spatial validation. Each stage handles specific aspects of command interpretation, improving overall accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent introduces intermediate processing components including semantic taggers that assign structured tags to words, parsers that convert tagged sentences into parse trees, and reference resolution modules that disambiguate pronouns and references. These intermediaries transform unstructured natural language into structured representations that can be accurately executed.
2Measurement precision
If the system processes complex natural language commands with multiple stages, then the accuracy of command interpretation improves, but the device complexity increases
Solution Approach 1:
The complex processing task is divided into four manageable stages: semantic tagging, parsing, reference resolution, and spatial validation. This segmentation allows each component to be optimized independently while working together to achieve high overall accuracy.
Solution Approach 2:
The system performs preliminary semantic tagging and parsing before executing commands. By pre-processing natural language into structured representations with assigned semantic roles and relationships, the system prepares commands in advance for accurate execution and validation.
3Ease of operation
If spoken language commands are used for controlling robotic arms, then the ease of operation improves, but the reliability deteriorates due to misinterpreted commands
Solution Approach 1:
The system incorporates feedback mechanisms including confidence scoring for semantic tags and parse results, reference resolution verification, and spatial validation that checks whether interpreted commands are physically feasible. This feedback loop identifies and corrects potential misinterpretations before execution.
Solution Approach 2:
The system performs preliminary validation of spoken commands through multiple processing stages before execution. Semantic tags are assigned, parse trees are constructed, references are resolved, and spatial feasibility is validated in advance, ensuring that only reliable commands are executed.
4Ease of operation
If the system abstracts from grammatical and spelling errors, then the ease of operation improves, but the manufacturing precision of command interpretation worsens
Solution Approach 1:
The system prepares for potential errors by implementing robust error handling at each processing stage. Semantic taggers handle grammatical variations, parsers manage structural inconsistencies, and validation routines catch semantic errors before execution, cushioning against the impact of user errors.
Solution Approach 2:
The system converts the challenge of handling erroneous input into an opportunity to demonstrate robustness. By designing the multi-stage processing pipeline to gracefully handle grammatical and spelling errors, the system maintains high usability while preserving interpretation accuracy through contextual analysis and validation.
Data Source
AI summary
A system, method and computer-readable storage devices are for processing natural language commands, such as commands to a robotic arm, using a Tag & Parse approach to semantic parsing. The system first assigns semantic tags to each word in a sentence and then parses the tag sequence into a semantic tree. The system can use statistical approach for tagging, parsing, and reference resolution. Each stage can produce multiple hypotheses, which are re-ranked using spatial validation. Then the system selects a most likely hypothesis after spatial validation, and generates or outputs a command. In the case of a robotic arm, the command is output in Robot Control Language (RCL).