Digital Twin (DT)-CycleGAN: Enabling Zero-Shot Sim-to-Real Transfer of Visual Grasping Models

Abstract

Deep learning has revolutionized the field of robotics. To deal with the lack of annotated training samples for learning deep models in robotics, Sim-to-Real transfer has been invented and widely used. However, such deep models trained in simulation environment typically do not transfer very well to the real world due to the challenging problem of "reality gap". In response, this paper presents a conceptually new Digital Twin (DT)-CycleGAN framework by integrating the advantages of both DT methodology and the CycleGAN model so that the reality gap can be effectively bridged. Our core innovation is that real and virtual DT robots are forced to mimic each other in a way that the gaps or differences between simulated and realistic robotic behaviors are minimized. To effectively realize this innovation, visual grasping is employed as an exemplar robotic task, and the reality gap in zero-shot Sim-to-Real transfer of visual grasping models is defined as grasping action consistency losses and intrinsically penalized during the DT-CycleGAN training process in realistic simulation environments. Specifically, first, cycle consistency losses between real visual images and simulation images are defined and minimized to reduce the reality gaps in visual appearances during visual grasping tasks. Second, the grasping agent's action consistency losses are defined and penalized to minimize the inconsistency of the grasping agent's actions between the virtual states generated by the DT-CycleGAN generator and the real visual states. By jointly penalizing both the cycle consistency losses and the grasping agent's action consistency losses in DT-CycleGAN, the reality gaps in both visual appearance and grasping action states in simulated and real environments are minimized, thus significantly contributing to the effective and robust zero-shot Sim-to-Real transfer of the trained visual grasping models. Extensive experiments demonstrated the effectiveness and efficiency of our novel DT-CycleGAN framework for zero-shot Sim-to-Real transfer. Our code and models are released on GitHub to facilitate other researchers' works in this promising direction 4 .

When Brain-inspired AI Meets AGI

“…In fact, following the emergence of large-scale models like GPT-4 and Midjourney V5, many industries, such as text processing and illustration, have witnessed disruptive scenarios where AGI liberates human labor. These models leverage prior knowledge acquired from pretraining across various tasks and context, allowing rapid adaptation to novel tasks without the need for extensive labeled data for finetuning, which is a critical challenge in fields like medical [69] and robotics [70] where labeled data is often limited or even unavailable.…”