Open, Reproducible and Trustworthy Robot-Based Experiments with Virtual Labs and Digital-Twin-Based Execution Tracing
This work addresses the problem of trust and reproducibility in autonomous scientific experimentation for researchers and practitioners, representing a foundational step rather than an incremental improvement.
The paper tackles the challenge of making autonomous robot experiments open and reproducible by introducing a semantic execution tracing framework and a cloud-based virtual lab platform, enabling transparent and replicable robot-driven science.
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.