Robot Interaction Rule Learning From User Behavior Imitation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robot interaction systems require manual design of interaction rules, making them time-consuming, labor-intensive, and inflexible, leading to stereotyped responses.
Innovation Solution
A method and device that allow robots to learn interaction rules by imitating user behaviors, collecting and analyzing data on interaction output behaviors to determine corresponding robot interaction output information, and storing interaction trigger information for personalized interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to generate interaction rules, then the robot can provide standardized interaction responses, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The robot automatically generates interaction rules by observing and imitating user behaviors without requiring manual programming. The system self-learns interaction patterns through continuous data collection and analysis, eliminating the need for time-consuming manual rule creation while maintaining response standardization through systematic learning processes
Solution Approach 2:
The patent replaces the mechanical manual programming process with an automated machine learning system. Instead of manually creating interaction rules, the robot uses algorithms to automatically observe, analyze, and generate rules from user interactions, substituting human labor with computational processes that are faster and more efficient
2Stability of the object's composition
If manual methods are used to generate interaction rules, then the robot can provide consistent interaction responses, but the interactions become stereotyped and inflexible
Solution Approach 1:
The interaction rules are made dynamic and adaptable rather than static and fixed. The robot continuously learns from user behaviors and automatically updates its interaction rules in real-time, allowing the system to adapt to different users and situations while maintaining consistency through systematic learning rather than rigid programming
Solution Approach 2:
The system incorporates continuous feedback loops where the robot observes user responses to its interactions and uses this information to refine and adjust its behavior. This feedback mechanism allows the robot to maintain consistent interaction patterns while adapting to user preferences and changing contexts, eliminating the stereotyped nature of manual rule-based systems
3Productivity
If manual interaction rules are programmed in advance, then the robot can execute predefined responses, but the system becomes overly programmed and inflexible
Solution Approach 1:
The robot autonomously generates and updates its own interaction rules through continuous observation and learning from user behaviors. This self-service approach eliminates the need for pre-programming while maintaining execution efficiency, as the robot automatically processes and applies learned rules in real-time interactions without requiring manual intervention or rigid predefined scripts
Data Source
AI summary
Embodiments of the disclosure provide a method and device for robot interactions. In one embodiment, a method comprises: collecting to-be-processed data reflecting an interaction output behavior; determining robot interaction output information corresponding to the to-be-processed data; controlling a robot to execute the robot interaction output information to imitate the interaction output behavior; collecting, in response to an imitation termination instruction triggered when the imitation succeeds, interaction trigger information corresponding to the robot interaction output information; and storing the interaction trigger information in relation to the robot interaction output information to generate an interaction rule.