Adaptive Robot Gesture Control Through Motion Pattern Learning
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Solution Overview
Problem
Existing robot control systems lack the ability to dynamically adjust and learn from user interactions to create unique and lifelike gestures, failing to adapt and personalize motions based on user preferences and contact history.
Innovation Solution
A robot control device that includes processors to generate and update gesture patterns through unsupervised learning, combining motion elements based on evaluation values derived from user interactions, allowing the robot to evolve its gestures and develop a personalized personality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If robot motion patterns are fixed and predetermined, then the robot can execute standardized motions reliably, but the robot cannot adapt to user preferences or develop unique personality characteristics
Solution Approach 1:
The motion pattern generation system transitions from static predetermined patterns to dynamic adaptive patterns. The robot evaluates user reactions to each motion pattern and dynamically adjusts future motion selections based on accumulated evaluation values, enabling the system to adapt to user preferences while maintaining a manageable structure through iterative learning rather than complex rule-based control.
Solution Approach 2:
The robot autonomously generates new motion patterns by combining elements from existing patterns without requiring external programming. The system automatically evaluates user reactions, derives evaluation values, and uses these to probabilistically select and combine motion elements for new patterns, enabling self-improvement and personality development through unsupervised learning.
2Adaptability or versatility
If the robot uses simple predetermined motion patterns, then the control system remains simple, but the robot cannot express unique personality or learn from user interactions
Solution Approach 1:
The system implements a feedback loop where user reactions to robot motions are detected and converted into evaluation values. These evaluation values feed back into the motion pattern generation process, influencing which motion elements are selected and combined. This feedback mechanism enables the robot to learn from user interactions and develop personalized motion patterns that express unique personality characteristics.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based motion control systems with an information-processing-based learning system. Instead of using complex mechanical mechanisms to generate varied motions, the system uses probabilistic algorithms and evaluation value-based selection to automatically generate and adapt motion patterns, substituting physical complexity with computational intelligence.
3Reliability
If the robot accumulates and learns from extensive user interaction data, then the robot develops more personalized and lifelike gestures, but the data processing and pattern generation complexity increases
Solution Approach 1:
The motion patterns are segmented into discrete motion elements that can be independently evaluated and recombined. Instead of processing entire motion sequences as monolithic units, the system breaks down motions into elemental components, evaluates them based on user reactions, and probabilistically recombines them based on accumulated evaluation values. This segmentation reduces data processing complexity while maintaining the ability to learn personalized preferences.
Solution Approach 2:
The system changes the parameter representation of motion patterns from fixed, holistic descriptions to probabilistic distributions over motion elements. By representing motions as combinations of elements with associated probabilities derived from evaluation values, the system can efficiently process and adapt to user preferences without requiring complex processing of entire motion sequences, thus improving reliability while managing complexity.
Data Source
AI summary
A robot control device includes one or more processors. The one or more processors are configured to: cause a robot to make motions in accordance with respective motion patterns each of which is made up of a combination of motion elements, in response to detecting an action from outside with a sensor; derive respective evaluation values of the motion patterns; and generate a new motion pattern by combining, based on the evaluation values, the motion elements of the motion patterns.