Robot Behavior Evaluation Using Observer Data Aggregation
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Solution Overview
Problem
Current technologies face challenges in assessing a robot's humanness and intelligence, particularly in tasks like speech recognition, image recognition, and pattern recognition, which are difficult for traditional processor-based devices to perform effectively.
Innovation Solution
A system that involves a hybrid machine/human computing approach, using automated classification systems to assess a robot's humanness by collecting data from observers and computing a 'bot or not' metric, which includes generating a dynamic representation of the robot, distributing observer interfaces, and aggregating observer data to determine the robot's appropriateness in a context or scenario.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional processor-based devices are used for speech recognition, image recognition, and pattern recognition, then device complexity is reduced, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent introduces observer agents as intermediaries between the robot system and the assessment process. These observer agents (human or automated) collect data about robot behaviors and interactions, then feed this data to a server that computes metrics. This intermediary layer enables sophisticated measurement of robot intelligence and humanness without requiring the robot itself to have complex processing capabilities for these specific assessment functions.
2Productivity
If automated classification systems are used to assess robot humanness, then productivity of evaluation is improved, but device complexity increases
Solution Approach 1:
The evaluation system is segmented into distinct functional components: observer agents that collect data, a server that aggregates and processes data, and metric computation modules that generate assessments. This segmentation allows the system to achieve high productivity in evaluating robot humanness while distributing complexity across multiple specialized components rather than concentrating it in a single complex device.
Solution Approach 2:
The server and metric computation system are designed to handle multiple types of assessments (humanness evaluation, intelligence assessment, behavior analysis) through a unified platform. This multi-functional design improves productivity by using the same infrastructure for various evaluation tasks, while the modular architecture manages complexity by allowing independent development and optimization of each assessment module.
3Measurement precision
If data is collected from multiple observer agents, then measurement precision improves, but loss of time increases
Solution Approach 1:
Observer agents continuously collect and pre-process data about robot behaviors in real-time during normal operation. This preliminary action ensures that when assessment is needed, the data is already available and pre-processed, reducing the time required for actual evaluation while maintaining high measurement precision through multiple observation sources.
Solution Approach 2:
The system maintains continuous data collection from multiple observer agents during robot operation, rather than interrupting the robot to gather data. This continuous action allows parallel data collection from multiple sources without adding sequential time delays, thereby improving measurement precision while minimizing time loss.
Data Source
AI summary
A dynamic representation of a robot in an environment is produced, one or more observer agent collects data, and respective values of one or more metrics for the robot are computed based at least in part on the collected data. Tasks for the robot to perform are generated. Ratings and challenge questions are generated. A server may produce a user interface and a value of a metric based on collected observer data.