Talk-to-Resolve System for Human-Robot Interaction Dialogue
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional human-robot interaction systems face challenges in resolving task ambiguities due to unclear or insufficient natural language instructions and difficulties in identifying desired objects in dynamic environments, leading to the need for continuous and inefficient dialogue with humans to clarify intentions.
Innovation Solution
The Talk-to-Resolve (TTR) system initiates a continuous dialogue between the robot and user, using visual uncertainty analysis to formulate questions and incrementally generate a semi-grounded execution plan, allowing the robot to ask specific questions to resolve ambiguities and adapt to changing scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous dialogue is initiated to resolve ambiguities, then task understanding accuracy is improved, but interaction time and complexity increase
Solution Approach 1:
The system performs preliminary visual analysis and caption generation before initiating dialogue, preparing potential ambiguity points in advance. This allows the robot to proactively identify and address uncertainties before they become blockers, reducing the need for extended back-and-forth conversations.
Solution Approach 2:
Visual captions serve as an intermediary between the robot's perception and the human user. Instead of directly questioning the user about ambiguous objects, the system generates descriptive captions that highlight uncertainties, allowing the user to clarify intentions more efficiently through natural conversation about the captured scene.
2Measurement precision
If visual uncertainty analysis and caption comparison are performed, then object identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The visual analysis process is segmented into distinct stages: caption generation, argument extraction, and ambiguity detection. Each stage processes specific aspects of the visual scene independently, allowing the system to manage computational complexity by breaking down the overall task into smaller, more tractable sub-tasks that can be executed sequentially.
Solution Approach 2:
The system performs visual analysis and caption generation only when necessary to resolve specific ambiguities, rather than continuously analyzing all scenes. By applying partial action only where needed, the system achieves high object identification accuracy for critical objects while minimizing overall computational overhead.
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
The disclosure herein relates to methods and systems for enabling human-robot interaction (HRI) to resolve task ambiguity. Conventional techniques that initiates continuous dialogue with the human to ask a suitable question based on the observed scene until resolving the ambiguity are limited. The present disclosure use the concept of Talk-to-Resolve (TTR) which initiates a continuous dialogue with the user based on visual uncertainty analysis and by asking a suitable question that convey the veracity of the problem to the user and seek guidance until all the ambiguities are resolved. The suitable question is formulated based on the scene understanding and the argument spans present in the natural language instruction. The present disclosure asks questions in a natural way that not only ensures that the user can understand the type of confusion, the robot is facing; but also ensures minimal and relevant questioning to resolve the ambiguities.