Robot Interaction Rule Learning Through Behavior Imitation
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Solution Overview
Problem
Current robot interaction systems rely on manually designed rule collections, which are time-consuming and labor-intensive, resulting in stereotyped and inflexible interactions.
Innovation Solution
A method for robots to learn interaction rules by imitating the behavior of a trainer, collecting and processing data such as depth images, color images, and vocal utterances to determine interaction outputs, 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 respond to user inputs, but the process is time-consuming and labor-intensive
Solution Approach 1:
The robot performs self-learning by automatically collecting interaction data, generating interaction rules, and updating its own interaction model without human intervention. The system uses unsupervised learning to autonomously identify interaction patterns and create response rules, enabling the robot to improve its interaction capabilities independently over time
Solution Approach 2:
The patent replaces the manual mechanical process of rule creation with an automated computational learning system. Instead of humans manually designing interaction rules, the system uses machine learning algorithms to automatically generate rules from observed interaction data, substituting human cognitive work with computational processes
2Reliability
If manual methods are used to generate interaction rules, then the robot can respond to user inputs, but the interactions become stereotyped and inflexible
Solution Approach 1:
The interaction rules are made dynamic through continuous learning and updating. The system continuously collects new interaction data and automatically updates the interaction model, allowing the robot to adapt its response strategies over time. This dynamic update mechanism enables the robot to handle diverse and evolving interaction scenarios rather than relying on static pre-programmed rules
Solution Approach 2:
The robot autonomously adapts to different interaction styles and scenarios by self-learning from observed data. The system automatically identifies patterns in user behavior and adjusts its interaction rules accordingly, enabling flexible and personalized interactions without requiring manual reprogramming for each scenario
3Adaptability or versatility
If automated learning is used to generate interaction rules, then the robot achieves personalized and flexible interactions, but the system complexity increases
Solution Approach 1:
The patent extracts and separates the learning functionality into independent modular components: data collection module, data processing module, rule generation module, and model update module. This modular architecture allows each component to be developed and optimized independently, managing system complexity while maintaining comprehensive learning capabilities
Solution Approach 2:
The learning system is divided into distinct functional segments that process different aspects of interaction data. The system segments the learning process into data collection, processing, rule generation, and model updating stages, with each segment handling specific tasks. This segmentation reduces overall system complexity by breaking down the complex learning process into manageable, specialized components
Data Source
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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.