Mind the Gap: Towards Building a More Seamless Sim-to-real Platform for Robotic Grasping and Manipulation

This paper presents a vision of creating a more seamless sim-to-real robotic grasping and manipulation platform, bridging the gap between simulated environments and real-world applications. In recent years, the emergence of digital twin technology has revolutionized how we develop and test such applications. A digital twin can simulate the behavior of a physical system in a virtual replica of the real-world environment in real-time, enabling engineers and researchers to conduct detailed analysis and evaluation before deploying robotic systems in the real world. In this paper, we study the potential of creating a digital twin that allows researchers and engineers to seamlessly deploy a grasping system trained on synthetic data and tested in a simulation environment without writing any additional code or doing additional calibration. Using our proposed platform, we present a case study done using a robotic grasping algorithm and analyze the advantages and limitations of our current platform. In the end, we suggest some possible improvements and future directions to enhance the platform's effectiveness and applicability.