The article presents TRANSIC, a novel framework for transferring policies from simulation to real-world settings using online corrections. It focuses on high-precision manipulation tasks, highlighting the integration of human feedback into policy learning, which allows for significant performance improvements while requiring less real-world data. Through experiments, TRANSIC demonstrates effectiveness against traditional methods, addressing various sim-to-real gaps, and exhibiting remarkable behaviors such as generalization to new objects and robustness in long-horizon tasks. The framework's adaptability showcases the potential for advanced robotic applications in complex environments.
Our experiments show that TRANSIC not only outperforms traditional sim-to-real methods in terms of performance but also requires significantly less real-world data to achieve impressive results.
The integration of human correction into the TRANSIC framework markedly improves policy learning, allowing us to leverage feedback effectively in transitioning from simulation to real-world applications.
One of the key findings is that TRANSIC can effectively address various sim-to-real gaps, showing robustness in generalizing to unseen objects and maintaining policy consistency.
The intriguing behaviors observed in our experiments, such as effective gating and the ability to handle long-horizon manipulation tasks, highlight the potential of TRANSIC for intelligent robotic applications.
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