Repeatable and Reliable Efforts of Accelerated Risk Assessment in Robot Testing
Linda Capito, Guillermo A. Castillo, Bowen Weng

TL;DR
This paper introduces a new algorithm to improve the repeatability and reliability of accelerated robot risk assessments, ensuring consistent and fair safety evaluations across diverse testing conditions and robot types.
Contribution
The study proposes a novel algorithm that guarantees repeatability and reliability in accelerated risk assessments, addressing limitations of traditional Monte-Carlo and importance sampling methods.
Findings
Demonstrated risk assessment of robot stability under various control algorithms.
Validated the algorithm on inverted pendulum and bipedal robot tests.
Showed improved consistency and fairness in risk evaluation results.
Abstract
Risk assessment of a robot in controlled environments, such as laboratories and proving grounds, is a common means to assess, certify, validate, verify, and characterize the robots' safety performance before, during, and even after their commercialization in the real-world. A standard testing program that acquires the risk estimate is expected to be (i) repeatable, such that it obtains similar risk assessments of the same testing subject among multiple trials or attempts with the similar testing effort by different stakeholders, and (ii) reliable against a variety of testing subjects produced by different vendors and manufacturers. Both repeatability and reliability are fundamental and crucial for a testing algorithm's validity, fairness, and practical feasibility, especially for standardization. However, these properties are rarely satisfied or ensured, especially as the subject robots…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRisk and Safety Analysis
