Communication Robot Response Learning From Remote Operator Feedback
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Solution Overview
Problem
The burden on remote operators of communication robots is significant due to limited information availability and complex manual selection of emotional responses, which complicates the operation and increases response time.
Innovation Solution
A learning device that includes an acquisition unit for recognizing user intention, a presentation unit for selecting action sets, an operation result detecting unit for remote operator feedback, and a learning unit that determines rewards based on the feedback to learn optimal responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection of emotional routines is implemented, then the communication robot can respond with appropriate emotional expressions, but the operation becomes complicated and response time increases
Solution Approach 1:
The system enables self-service through automated routine selection. The remote operator no longer needs to manually select emotional routines from a pool of available routines. Instead, the system automatically determines appropriate routines based on user recognition results, making the system serve itself rather than requiring continuous human intervention for each response selection.
Solution Approach 2:
The system implements feedback mechanisms where the remote operator's selections (or lack thereof) are used to refine and improve the automated routine selection process. The operator's feedback on the presented action sets helps the system learn and adapt, improving future automatic selections while reducing operational complexity.
2Reliability
If manual selection of emotional routines is implemented, then the communication robot can respond with appropriate emotional expressions, but response time increases
Solution Approach 1:
The system performs preliminary action by pre-processing user recognition results and automatically determining appropriate emotional routines before the remote operator needs to respond. The system prepares action sets in advance based on the recognized user state, so when a response is needed, the work is already partially done, significantly reducing response time while maintaining appropriateness.
Solution Approach 2:
The automated routine selection makes the system self-sufficient in generating appropriate responses without requiring time-consuming manual selection by the remote operator. The system serves itself by automatically matching user states with appropriate emotional routines, eliminating the time delay associated with manual intervention.
3Loss of information
If limited information is used for privacy protection, then user privacy is protected, but the remote operator's ability to make accurate selections is reduced
Solution Approach 1:
The system introduces an intermediary processing layer that transforms raw user information into recognition results. Instead of directly exposing detailed user data to the remote operator (which would compromise privacy), the system acts as an intermediary that processes the data locally and presents only necessary, anonymized recognition results to the operator, maintaining both privacy and selection accuracy.
Solution Approach 2:
The system creates copies of user information in the form of recognition results that capture the essential meaning without revealing sensitive details. These copies (recognition results) are what the remote operator works with, allowing accurate selection based on the copied information while the original detailed information remains protected and does not need to be transmitted or exposed.
Data Source
AI summary
A learning device includes: an acquisition unit configured to acquire a recognition result by recognizing intention indication information of a user who uses a communication robot; a presentation unit configured to select a plurality of action sets corresponding to the recognition result on the basis of the acquired recognition result and to present the selected plurality of action sets to a remote operator who remotely operates the communication robot from a remote location; an operation result detecting unit configured to detect a selection state of the remote operator for the presented plurality of action sets; and a learning unit configured to determine a reward in learning on the basis of the detected selection state of the remote operator and to learn a response to the user's action.