Failure Prediction from Limited Hardware Demonstrations

Parashar, Anjali; Garg, Kunal; Zhang, Joseph; Fan, Chuchu

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https://hdl.handle.net/2142/130312 Copy

Description

Title

Failure Prediction from Limited Hardware Demonstrations

Author(s)

• Parashar, Anjali
• Garg, Kunal
• Zhang, Joseph
• Fan, Chuchu

Issue Date

2025-09-17

Keyword(s)

• Failure discovery
• Testing
• Bayesian inference

Abstract

Prediction of failures in real-world autonomous systems either requires accurate model information or extensive testing. Partial knowledge of the system model makes simulation-based failure prediction unreliable. Moreover, obtaining such demonstrations is expensive, and could potentially be risky for the autonomous system to repeatedly fail during data collection. This work presents a novel three-step methodology for discovering failures that occur in the true system by using a combination of a limited number of demonstrations from the true system and the failure information processed through sampling-based testing of a model dynamical system. Given a limited budget of demonstrations from true system and a model dynamics (with potentially large modeling errors), the proposed methodology comprises of a) exhaustive simulations for discovering algorithmic failures using the model dynamics; b) design of initial demonstrations of the true system using Bayesian inference to learn a Gaussian process regression (GPR)-based failure predictor; and c) iterative demonstrations of the true system for updating the failure predictor. To illustrate the efficacy of the proposed methodology, we consider: a) the failure discovery for the task of pushing a T block to a fixed target region with UR3E collaborative robot arm using a diffusion policy; and b) the failure discovery for an F1-Tenth racing car tracking a given raceline under an LQR control policy.

Publisher

Allerton Conference on Communication, Control, and Computing

Series/Report Name or Number

2025 61st Allerton Conference on Communication, Control, and Computing Proceedings

ISSN

2836-4503

Type of Resource

Text

Genre of Resource

Conference Paper/Presentation

Language

eng

Handle URL

https://hdl.handle.net/2142/130312&&

Copyright and License Information

Copyright 2025 owned by the authors.

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