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

arXiv: https://arxiv.org/abs/2508.11406 · HTML: https://mlsanity.com/paper/2508.11406/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Foundations of the Socio-physical Model of Activities (SOMA) for Autonomous Robotic Agents](https://mlsanity.com/paper/2011.11972/) (2020)
- [Translating Universal Scene Descriptions into Knowledge Graphs for Robotic Environment](https://mlsanity.com/paper/2310.16737/) (2023)
- [Scaling Laws in Scientific Discovery with AI and Robot Scientists](https://mlsanity.com/paper/2503.22444/) (2025)
- [LiveDocs: Crafting Interactive Development Environments From Research Findings](https://mlsanity.com/paper/2402.09475/) (2024)

## Built on by (most cited first, 1 of 1)

- [InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios](https://mlsanity.com/paper/2509.22502/) (2025)

## Abstract (arXiv)

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.