Open, Reproducible and Trustworthy Robot-Based Experiments with Virtual Labs and Digital-Twin-Based Execution Tracing

TL;DR

This paper introduces a semantic execution tracing framework and a cloud-based platform to enable open, trustworthy, and reproducible robot experiments, facilitating transparent scientific discovery with autonomous systems.

Contribution

It presents a novel semantic execution tracing framework and the AICOR Virtual Research Building platform for scalable, transparent, and reproducible robot experiments.

Findings

Semantic execution tracing ensures transparency and reproducibility.

The VRB platform enables sharing and validation of robot experiments.

Framework supports autonomous scientific discovery.

Abstract

We envision a future in which autonomous robots conduct scientific experiments in ways that are not only precise and repeatable, but also open, trustworthy, and transparent. To realize this vision, we present two key contributions: a semantic execution tracing framework that logs sensor data together with semantically annotated robot belief states, ensuring that automated experimentation is transparent and replicable; and the AICOR Virtual Research Building (VRB), a cloud-based platform for sharing, replicating, and validating robot task executions at scale. Together, these tools enable reproducible, robot-driven science by integrating deterministic execution, semantic memory, and open knowledge representation, laying the foundation for autonomous systems to participate in scientific discovery.