Robot Task Identification Using Mixed-Initiative Dialogue
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Solution Overview
Problem
Current human-robot interaction systems face challenges in accurately interpreting natural language instructions due to ambiguity and the need for domain-specific knowledge, limiting the usability and acceptability of robots in daily environments.
Innovation Solution
A processor-implemented method and system for robotic task identification using natural language conversations, which predicts the intent behind an utterance, identifies tasks, and generates context-aware inputs for a task planner through a mixed-initiative dialogue, leveraging a Knowledge Base and multiclass classifiers for accurate task understanding and execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If restricted natural language based interaction is used to address ambiguity, then task interpretation accuracy is improved, but usability and acceptability of the robot deteriorates
Solution Approach 1:
The system dynamically adapts the level of language restriction based on context and confidence levels. When ambiguity is detected, the system engages in clarifying dialogue rather than rigidly enforcing restricted language, allowing flexible interpretation while maintaining accuracy.
Solution Approach 2:
The system uses confidence scores from task type prediction to determine whether to engage in mixed-initiative dialogue for clarification. This feedback mechanism allows the system to selectively apply restricted language protocols only when necessary, balancing accuracy with usability.
2Adaptability or versatility
If programming the robot for each and every task is done comprehensively, then task execution capability is improved, but device complexity increases
Solution Approach 1:
The system employs a universal task type prediction framework that can handle multiple task categories (motion, manipulation, device control, etc.) through a single unified approach. The multiclass classifier and template-based system provide multi-functional capability without requiring separate programming for each task type.
Solution Approach 2:
The patent introduces an intermediary layer (task type prediction module with confidence scoring) between natural language input and task execution. This intermediary translates diverse task descriptions into standardized task types, reducing the complexity of direct programming for each specific task.
3Measurement precision
If mixed-initiative dialogue is initiated to confirm identified tasks with low confidence scores, then task identification accuracy is improved, but interaction time increases
Solution Approach 1:
The system changes the parameter of confidence threshold to determine when mixed-initiative dialogue should be initiated. By dynamically adjusting the threshold and using confidence scores, the system balances the need for accuracy with the desire to minimize interaction time for high-confidence predictions.
Data Source
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.