The 1st International Workshop on Digital Twin Frameworks for Real-World Robotic Learning (RoboTwin 2026)

Call for Papers

Digital twins are emerging as a critical enabling infrastructure for deploying intelligent robotic systems in real-world smart environments. This workshop focuses on how digital twin frameworks can accelerate the development, validation, and safe deployment of learning-based robotics applications, including industrial inspection, autonomous manipulation, and robotics for smart infrastructure.
We address both the foundations of twin formation – multi-fidelity modelling, hybrid physics-data representations, dynamic synchronization, and online system identification – and their integration with learning-based robot control and perception. Emphasis is placed on closed-loop digital twin pipelines that support sim-to-real transfer, adaptive control, performance monitoring, and lifecycle optimization under real-world uncertainty.
Key themes include – but not limited to – formal metrics for twin fidelity, benchmarking protocols for reproducibility, uncertainty modelling and safety guarantees, scalable edge–cloud collaborative architectures for distributed robotic systems, and digital twin-enabled robotic applications in the real world. We also encourage discussion of interoperability and emerging standardization challenges for robotic digital twin ecosystems.