Detection of Subtle Context-Dependent Model Inaccuracies in High-Dimensional Robot Domains

Area of Science:

• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• Autonomous robots require accurate models for decision-making.
• Real-world environments present complexities that challenge model accuracy.
• High-dimensional data from robot execution can reveal model inaccuracies.

Purpose of the Study:

• To address the challenge of detecting context-dependent model inaccuracies in high-dimensional spaces.
• To develop a tractable method for identifying situations where robot models fail.

Main Methods:

• The study proposes an informed search through low-dimensional projections of robot execution data.
• This approach aims to identify parametric Regions of Inaccurate Modeling (RIMs).

Main Results:

• The proposed method significantly enhances the detection power of existing RIM-detection algorithms.
• Empirical evidence from two robot domains supports the effectiveness of the approach.

Conclusions:

• The developed method provides a viable solution for detecting model inaccuracies in high-dimensional robotic contexts.
• This research contributes to more robust and reliable autonomous robot operation.