Robot Behavior Selection Using Probability Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robot systems are limited in expressing varied human feelings as they map one behavior to one feeling one-to-one, leading to inefficient and unnatural behavior expressions, as they require manual definition processes and cannot reflect composite feelings effectively.
Innovation Solution
An apparatus and method that utilize a generative probability model to set probabilities for each feeling expression behavior, generate and select behavior combinations that approximate human-like feeling expressions by calculating averages and determining optimal behavior combinations using a random extraction process, allowing for more natural and varied behavior expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If one behavior is mapped to one feeling one-to-one, then the behavior expression is simple, but the robot cannot show various behaviors reflecting composite feelings
Solution Approach 1:
The patent segments the feeling expression into multiple independent expression elements (e.g., facial features, body parts), where each element can independently select behaviors based on probability values. This allows composite feelings to be expressed through combinations of multiple elements rather than requiring a single complex behavior mapping.
Solution Approach 2:
The patent introduces dynamic probability values for each expression element that can be adjusted in real-time based on the current feeling state. This dynamic adjustment enables the robot to flexibly vary its behavior expressions while maintaining a structured framework, resolving the contradiction between simplicity and versatility.
2Manufacturing precision
If manual definition process is used to show various behaviors, then the behavior definitions are precise, but the process is inefficient and requires manual intervention
Solution Approach 1:
The patent enables the robot to automatically generate and adjust its own behavior probabilities through the probability value calculation unit, which computes optimal probability distributions based on the current feeling state and expression element characteristics. This self-service mechanism eliminates manual definition processes while maintaining precise behavior selection.
Solution Approach 2:
The system incorporates feedback loops where the robot continuously evaluates its current feeling state, adjusts probability values for expression elements, and selects behaviors accordingly. This automated feedback mechanism replaces manual definition processes with an efficient self-regulating system that maintains precision without human intervention.
3Device complexity
If few definitions are used for feeling expressions, then the system is simple, but the robot cannot generate behaviors reflecting various feelings
Solution Approach 1:
The patent adds a probability dimension to the traditional feeling-behavior mapping. Instead of directly mapping feelings to discrete behaviors, the system maps feelings to probability distributions across multiple expression elements, creating a continuous space of possible behaviors. This dimensional transformation allows few definitions to generate many varied expressions.
Solution Approach 2:
The patent combines multiple expression elements (each with its own probability distribution) to create composite behavior expressions. Just as composite materials combine different substances to achieve new properties, the system combines multiple expression elements to generate diverse feeling expressions from a limited set of basic definitions.
Data Source
AI summary
An apparatus for selecting a motion signifying artificial feeling is provided. The apparatus includes: an feeling expression setting unit configured to set probabilities of each feeling expression behavior performed for each expression element of a robot for each predetermined feeling; a behavior combination generation unit configured to generate at least one behavior combination combined by randomly extracting the feeling expression behaviors in each expression element one by one; and a behavior combination selection unit configured to calculate an average for the probabilities of the feeling expression behaviors included in each behavior combination for each feeling of a robot and select behavior combinations in which the average of the probabilities of the feeling expression behaviors most approximates the predetermined feeling value of a robot from each behavior combination.