Robot Control with Completion Rate and Certainty Feedback
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Solution Overview
Problem
Conventional AI systems used for robot control operate as 'black boxes,' making it difficult for users to understand and trust autonomous robot movements based on learned models, as the basis for estimation is not explained.
Innovation Solution
A robot control device that includes a learned model, a control data acquisition section, a completion rate acquisition section, and a certainty factor acquisition section, which acquires and provides control data, completion rates, and certainty factors to explain the robot's actions, reducing the 'black box' nature and enhancing user trust.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a learned model is used to control robot movements, then automation and productivity are improved, but the system becomes a black box making it difficult for users to understand and trust the robot's actions
Solution Approach 1:
The patent introduces completion rate and certainty factor as intermediary metrics that bridge the gap between the learned model's internal decision-making process and user understanding. These metrics act as mediators that translate the black box model's confidence and progress information into comprehensible forms for users, allowing them to trust and monitor autonomous robot movements without needing to understand the complex neural network internals.
Solution Approach 2:
The system implements feedback mechanisms by continuously providing completion rates and certainty factors to users during robot operation. This feedback loop allows users to monitor the learned model's confidence levels and progress, enabling them to understand when the robot is making reliable decisions versus when human intervention might be needed, thus building trust in the automated system.
2Device complexity
If a learned model operates as a black box, then device complexity is reduced, but user trust and understanding of robot actions deteriorate
Solution Approach 1:
The patent segments the learned model's output information into distinct, interpretable components: completion rate and certainty factor. Instead of presenting the complex internal state of the neural network as a single undifferentiated black box, the system divides the information into separate metrics that users can understand and evaluate independently, maintaining model simplicity while enhancing user trust through transparent information provision.
3Reliability
If completion rate and certainty factor are provided, then user understanding and trust are improved, but device complexity and information processing requirements increase
Solution Approach 1:
The patent creates simplified copies or representations of the learned model's internal confidence and progress states in the form of completion rate and certainty factor metrics. Rather than requiring users to directly interpret complex neural network activations, the system generates simplified proxy measurements that capture the essential information needed for user understanding, adding minimal complexity while significantly improving trust.
Data Source
AI summary
A robot control device includes: a learned model created through learning work data composed of input and output data, the input data including states of a robot and the surroundings where humans operate the robot to perform a series of works, the output data including human operation corresponding to the case or movement of the robot caused thereby; a control data acquisition section that acquires control data by obtaining output data related to human operation or movement from the model, being presumed in response to and in accordance with the input data; a completion rate acquisition section acquiring a completion rate indicating to which progress level in the series of works the output data corresponds; and a certainty factor acquisition section that acquires a certainty factor indicating a probability of the presumption in a case where the model outputs the output data in response to the input data.