Interactive Behavior Model for Robot Interaction Services
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Solution Overview
Problem
Existing Human Robot Interaction (HRI) methods rely on pre-defined scenarios and sensors, leading to limited and unpredictable robot behaviors due to measurement errors, resulting in suboptimal communication between users and robots.
Innovation Solution
An apparatus and method utilizing a statistical inference-based interactive behavior model that constructs behavior and policy models to enable robots to manage input and circumstance recognition errors, allowing flexible output behaviors and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-defined scenarios and sensors are used for HRI, then the robot can perform basic interaction tasks, but the robot behavior becomes limited and unpredictable due to measurement errors
Solution Approach 1:
The patent transforms discrete pre-defined scenarios into continuous probabilistic parameters. The behavior model engine uses probability distributions to represent uncertain sensor measurements and generates behavior parameters through statistical inference, allowing the robot to adapt its behavior continuously based on input likelihoods rather than being constrained to fixed scenario responses
Solution Approach 2:
The behavior model engine acts as an intermediary between sensors and robot actuators. It receives observation parameters from sensors, processes them through behavior and policy models to generate behavior parameters, and outputs control signals. This intermediary layer filters and interprets sensor data through probabilistic models, making the system robust to measurement errors while maintaining behavioral adaptability
2Measurement precision
If sensors are used to recognize circumstances, then the robot can perceive external environment, but measurement errors cause the robot to make strange behaviors
Solution Approach 1:
The system implements feedback through the behavior model engine that continuously processes observation parameters and adjusts behavior parameters based on probabilistic inference. The engine compares observed circumstances against learned behavior models and policy models, using feedback from the likelihood calculations to generate appropriate behaviors even when sensor measurements contain errors
Solution Approach 2:
The patent applies beforehand cushioning by pre-training behavior and policy models with statistical distributions that account for expected sensor measurement errors. The behavior model engine uses these pre-established probabilistic models to compensate for measurement errors in real-time, cushioning the system against the impact of inaccurate sensor data before it can cause strange behaviors
3Ease of operation
If the robot outputs predefined expressions according to specific input signals, then the robot can respond to user inputs, but the robot does not communicate with user but just simply outputs instructed expressions or behaviors
Solution Approach 1:
The patent transforms static predefined expression output into dynamic probabilistic behavior generation. Instead of mapping specific input signals to fixed expressions, the behavior model engine dynamically generates behavior parameters by sampling from probability distributions based on observation parameters and learned models, enabling the robot to communicate more naturally and adaptively while maintaining ease of operation
Solution Approach 2:
The patent replaces the mechanical signal-mapping system with a statistical inference system. Instead of direct mapping from input signals to predefined expressions, the system uses probability distributions and statistical models to infer the most likely intended meaning from sensor inputs and generate appropriate behavioral responses, enriching the communication quality
4Ease of manufacture
If role-based database is built to map input and output data, then the robot can output expressions more easily, but the robot behavior remains limited to prearranged scenarios
Solution Approach 1:
The patent creates a universal behavior model engine that can handle multiple types of inputs and generate diverse behaviors through a single probabilistic framework. Instead of separate mappings for different scenarios, the engine uses unified behavior and policy models that can process various observation parameters and generate appropriate behaviors for unseen situations, maintaining ease of implementation while greatly increasing behavioral flexibility
Data Source
AI summary
An apparatus for providing robot interaction services using an interactive behavior model for interaction between a user and a robot includes: a control module having a behavior model engine for receiving an observation signal from the outside and determining and outputting an interactive behavior signal based on previously stored behavior and policy models; a robot application module for executing a robot application service and applying the behavior signal to provide the service; a robot function operating module having sensors for observing an external circumstance and a function operating means for performing behavior or function of the robot; and a middleware module for extracting external circumstance observation information and service history information and inputting the information to the control module as the observation signal, and for analyzing the behavior signal to generate and provide motion and function operating signals to the robot function operating module.