The article presents TRANSIC, an innovative approach to sim-to-real policy transfer for robots. By utilizing a human-in-the-loop framework, TRANSIC allows human intervention to inform and correct robot policies during real-world execution, mitigating common sim-to-real discrepancies. Through the incorporation of online corrections and residual policy learning, robots adapt more seamlessly to real environments, enhancing their operational effectiveness. The framework is empirically validated through detailed experiments across multiple assembly tasks, demonstrating not only improved performance but also the scalability of human assistance in diverse robotic learning scenarios.
The TRANSIC approach illustrates how leveraging human oversight can significantly close the sim-to-real gaps when training robots, ensuring better policy transfer.
By integrating human-in-the-loop methods, TRANSIC enhances the learning process, enabling robots to adapt their actions more effectively in real-world scenarios.
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