Bridging the Sim-to-Real Gap for Efficient and Robust Robotic Skill Acquisition
Keywords: Robotics, Sim-to-Real, RL, IL
Abstract: In recent years, the integration of learning-based methods, particularly Imitation Learning (IL) and Reinforcement Learning (RL), has brought significant advancements to robotics applications. These methods enable robots to learn complex behaviors, with IL facilitating rapid skill acquisition via expert demonstrations and RL permitting self-discovery of optimal strategies. However, the reliance on real-world data presents substantial challenges, as data collection can be resource-intensive, time-consuming, and risky for robotic systems. While simulation environments have emerged as a practical solution, providing abundant training data, they also introduce the sim-to-real gap—a critical challenge that hampers the effective transfer of learned behaviors from simulations to real-world scenarios due to discrepancies in sensor performance, environmental conditions, and material properties.
To address these challenges, we propose a novel framework that combines the strengths of both IL and RL while minimizing transfer difficulties associated with simulation-trained models. Our approach leverages the cost-effectiveness of simulated data to enhance robot learning outcomes, utilizing advanced techniques to improve transferability and reduce the sim-to-real gap. By harmonizing the efficiency of IL with the autonomy of RL, we aim to create a more effective learning paradigm that not only accelerates skill acquisition but also enhances real-world applicability. Our results demonstrate that this integrated approach can significantly improve performance in robotic tasks, paving the way for more autonomous and capable robotic systems.
Submission Number: 45
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