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

Created by
  • Haebom

Author

Benjamin Alt, Mareike Picklum, Sorin Arion, Franklin Kenghagho Kenfack, Michael Beetz

Outline

This paper envisions a future where autonomous robots perform scientific experiments in an open, trustworthy, and transparent manner, while also being accurate and repeatable. To achieve this, we present a semantic execution tracking framework and a cloud-based platform, the AICOR Virtual Research Building (VRB), which ensures transparency and reproducibility of automated experiments by co-recording sensor data with semantically annotated robot belief states. VRB is used to share, replicate, and validate robot task execution at scale. These tools integrate deterministic execution, semantic memory, and open knowledge representation to enable reproducible robot-driven science, laying the foundation for autonomous systems to participate in scientific discovery.

Takeaways, Limitations

Takeaways:
Improving transparency and reproducibility in scientific experiments using autonomous robots.
Accelerating Scientific Discovery with a Semantic Execution Tracing Framework and the AICOR VRB Platform
Introducing new possibilities for robot-driven science through the integration of deterministic execution, semantic memory, and open knowledge representation.
Providing an efficient platform for sharing, replicating, and validating large-scale robotic task execution.
Limitations:
Lack of specific details on the practical scientific experimental application and performance evaluation of the proposed framework and platform.
Further research is needed on the applicability and generalizability of this approach to various types of robotics and scientific experiments.
The need for verification of the scalability and stability of the AICOR VRB platform.
Lack of detailed discussion on robot reliability and error handling.
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