Robot Task Identification Using Mixed-Initiative Dialogue
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Solution Overview
Problem
Current human-robot interaction systems face challenges in interpreting natural language instructions due to ambiguity and the need for restricted language, which limits the usability and acceptability of robots in daily environments, especially for non-expert users.
Innovation Solution
A processor-implemented method and system for robotic task identification using natural language conversations, involving intent prediction, mixed-initiative dialogue, and context-aware task planning, which includes pre-processing utterances, identifying tasks, and generating task plans using a Knowledge Base and multiclass classifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If restricted natural language is used to address ambiguity, then task interpretation accuracy is improved, but usability and acceptability deteriorate
Solution Approach 1:
The system implements mixed-initiative dialogue where the robot actively seeks clarification by asking questions when task goals are ambiguous. This feedback mechanism allows the robot to resolve uncertainty about user intentions while maintaining natural conversation flow, thus achieving both accurate task interpretation and high usability
Solution Approach 2:
The patent introduces an intermediate clarification stage between natural language input and task execution. When ambiguity is detected, the system inserts a dialogue-based clarification process that mediates between the user's natural language and the robot's task interpretation, resolving the contradiction by allowing full natural language input while ensuring accurate understanding
2Adaptability or versatility
If programming for each and every task is implemented, then task execution capability is improved, but system complexity deteriorates
Solution Approach 1:
The patent implements a universal task identification framework that handles multiple task types through a single integrated system. The task identification module can recognize and process various task categories (navigation, manipulation, information seeking, etc.) using common NLP and reasoning techniques, eliminating the need for separate programming for each task type while maintaining high versatility
3Ease of operation
If natural language processing is used to enable robot interaction, then interaction naturalness is improved, but task goal interpretation accuracy deteriorates
Solution Approach 1:
The system uses feedback through mixed-initiative dialogue to resolve ambiguities in natural language interpretation. When the robot detects uncertainty about the user's task goal, it asks clarifying questions, allowing the user to provide additional context or correction, thus maintaining natural interaction while improving interpretation accuracy
Solution Approach 2:
The patent performs preliminary analysis of the natural language input to detect potential ambiguities before final task interpretation. By identifying ambiguous elements in advance, the system can proactively seek clarification rather than making incorrect assumptions, thereby maintaining both naturalness and accuracy
Data Source
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AI summary
This disclosure relates generally to human-robot interaction (HRI) to enable a robot to execute tasks that are conveyed in a natural language. The state-of-the-art is unable to capture human intent, implicit assumptions and ambiguities present in the natural language to enable effective robotic task identification. The present disclosure provides accurate task identification using classifiers trained to understand linguistic and semantic variations. A mixed-initiative dialogue is employed to resolve ambiguities and address the dynamic nature of a typical conversation. In accordance with the present disclosure, the dialogues are minimal and directed to the goal to ensure human experience is not degraded. The method of the present disclosure is also implemented in a context sensitive manner to make the task identification effective.