Rethinking Robustness in Imitation Learning: What is crucial for Sim-to-Real?

Imitation Learning (IL) has emerged as a promising paradigm for training robotic policies from expert demonstrations. A significant challenge in real-world robotics, however, is the robustness gap between simulation and real-world deployment, often referred to as the "Sim-to-Real" gap. This paper critically examines the concept of robustness in imitation learning for Sim-to-Real transfer. We investigate various factors that contribute to the success or failure of IL policies when deployed in the physical world, including data distribution shifts, environmental variations, sensor noise, and actuator imperfections. Through empirical analysis and theoretical discussions, we highlight the crucial aspects of robustness that need to be addressed to achieve effective Sim-to-Real transfer. We propose a comprehensive framework for evaluating and improving the robustness of IL agents, considering both policy generalization and safety. Our findings suggest that explicit consideration of diverse perturbations and structured training methodologies are vital for bridging the Sim-to-Real gap, leading to more reliable and deployable robotic systems.