Robot Interaction Parameter Adaptation via Regression Analysis
Solution Overview
Problem
Existing robots struggle to perform interactions suitable for specific user contexts due to reliance on preset interaction parameters, which may not adapt effectively to varying user situations, and require extensive interaction performance histories for context-based adjustments.
Innovation Solution
A robot system that identifies interaction parameters based on stored history information, using regression analysis or alternative history sources when thresholds are met, and updates history information based on user responses to improve interaction relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If interaction parameters are adjusted based on user context using machine learning, then interaction suitability is improved, but a plurality of interaction performance histories are required which increases data requirements
Solution Approach 1:
The robot performs regression analysis on stored interaction performance histories in advance to identify optimal interaction parameters for different user contexts. By pre-processing and analyzing historical data, the robot builds a knowledge base that enables context-appropriate interactions without requiring extensive real-time data collection, thus reducing the immediate data requirements while maintaining adaptability
Solution Approach 2:
The robot autonomously identifies and updates interaction parameters by performing regression analysis on its own stored history information. The system self-adjusts interaction parameters based on accumulated performance data and user responses without requiring external retraining or additional data inputs, enabling continuous improvement with minimal external data requirements
2Ease of operation
If preset interaction parameters are used, then the robot can perform interactions immediately, but the interactions are not suitable for varying user contexts
Solution Approach 1:
The robot dynamically adjusts interaction parameters based on identified user contexts by performing regression analysis on stored history information. Instead of using fixed preset parameters, the system continuously adapts parameters according to the specific user context, enabling both immediate interaction execution and context-appropriate behavior through real-time parameter identification
Solution Approach 2:
The robot changes interaction parameters based on regression analysis results from stored history information and identified user contexts. By dynamically modifying parameters such as interaction timing, intensity, and type according to user context, the system maintains ease of operation while achieving context suitability through parameter adaptation
Data Source
AI summary
A robot is provided. The robot includes at least one sensor, a driver, memory storing one or more computer programs, and one or more processors operatively coupled with the at least one sensor, the driver, and the memory, wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors, cause the robot to detect the occurrence of an event through the at least one sensor, identify an interaction corresponding to the event, identify user context, identify an interaction parameter based on history information stored in the memory, the interaction, and the user context, control the driver to perform the interaction based on the identified interaction parameter, obtain information on a user's response according to the performed interaction, and update the history information based on information on the identified interaction parameter and the response.