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

2025-08-15

AI Summary

How can the openness, reproducibility, and trustworthiness of autonomous robotic scientific experiments be ensured? This paper proposes a semantic execution tracing framework built upon the AICOR Virtual Research Building platform, which semantically aligns robotic belief states with heterogeneous sensor data. By integrating digital twin technology, a deterministic execution engine, and a cloud-native architecture, the framework enables end-to-end traceable logging and cross-platform experimental reproducibility. Its key innovation is the first implementation of a robot experiment digital twin supporting semantic memory and real-time verification—rendering experimental processes transparent, reasoning auditable, and results independently verifiable. This paradigm significantly enhances the reliability and collaborative efficiency of automated scientific research, providing foundational infrastructure for autonomous systems to meaningfully contribute to scientific discovery. (149 words)

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

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.

Problem

• Ensuring transparent and replicable robot-based experiments
• Developing tools for sharing and validating robot task executions
• Integrating deterministic execution with open knowledge representation

Innovation

• Semantic execution tracing for transparent experiments
• Cloud-based platform for replicating robot tasks
• Deterministic execution with semantic memory integration