Robot Gesture Generation Using Labanotation Library
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Solution Overview
Problem
Conventional chatting robots lack the ability to provide appropriate and natural gesture responses during interactions, leading to ineffective human-robot communication.
Innovation Solution
A method and apparatus for generating robot gestures by determining concepts from spoken utterances and retrieving corresponding symbolic representations from a gesture library, using labanotation to execute synchronized gestures, enhancing communication between humans and robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional chatting robots are used without gesture generation, then the system complexity is low, but the communication effectiveness and naturalness are poor
Solution Approach 1:
The patent applies preliminary action by pre-building a gesture library with symbolic representations (labanotations) of various gestures before runtime. During interaction, the system retrieves pre-prepared gestures from this library based on speech recognition results, rather than generating gestures in real-time. This pre-computation approach improves communication effectiveness while keeping the runtime system complexity manageable.
Solution Approach 2:
The patent introduces an intermediary gesture library that mediates between speech recognition and robot execution. The library stores symbolic representations (labanotations) that translate spoken concepts into gesture commands. This intermediary layer decouples the complexity of gesture generation from the main communication system, allowing effective communication without directly increasing overall system complexity.
2Adaptability or versatility
If gesture library with comprehensive gestures is created, then the gesture appropriateness and communication naturalness improve, but the data processing and storage requirements increase
Solution Approach 1:
The patent extracts only the essential symbolic representations (labanotations) needed for gesture description from comprehensive gesture data. Instead of storing and processing complete gesture animation files or complex motion capture data, the system extracts condensed symbolic notations that capture the essential gesture characteristics. This reduction extracts only the necessary information, improving gesture appropriateness while reducing data storage requirements.
Solution Approach 2:
The patent changes the parameter representation of gestures from detailed motion data to condensed symbolic parameters (labanotations). By transforming gesture information into a compact symbolic format with defined parameters, the system achieves versatile and appropriate gesture selection while significantly reducing the quantity of data that needs to be stored and processed.
3Ease of operation
If synchronized gesture and speech output is implemented, then the communication naturalness improves, but the coordination complexity and timing precision requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-defining the temporal and spatial parameters of gestures in the gesture library. Each labanotation contains predetermined timing information and execution parameters that guide synchronized speech and gesture output. This pre-computation of coordination parameters reduces runtime complexity while achieving natural synchronization.
4Manufacturing precision
If labanotation symbolic representation is used, then the gesture execution precision improves, but the system complexity and implementation difficulty increase
Solution Approach 1:
The patent uses labanotation as a symbolic copy or representation of physical gestures. Instead of directly implementing complex motion control algorithms, the system creates symbolic copies (labanotations) that represent gesture sequences. These symbolic copies can be stored, retrieved, and executed with high precision while avoiding the implementation complexity of direct physical gesture synthesis.
Data Source
AI summary
A method and apparatus for robot gesture generation is described. Generally speaking, a concept corresponding to a utterance to be spoken by a robot is determined (204). After a concept is determined or selected, a symbolic representation of a gesture that corresponds to the determined concept is retrieved from a predetermined gesture library (206). Subsequently, the symbolic representation is provided to cause the robot to perform the gesture (208). In such way, a more natural, comprehensive and effective communication between human and robots may be achieved.