The article discusses the TRANSIC framework, which excels in sim-to-real policy transfer by leveraging online human corrections. It highlights TRANSIC's superior performance, achieving an 81% average success rate across four assembly tasks. Notably, it achieves a perfect 100% success rate in the task of stabilization, outperforming baseline methods like BC Fine-Tune by significant margins. TRANSIC demonstrates that utilizing human correction data effectively can substantially enhance performance, indicating its potential for real-world application in robotics and machine learning.
TRANSIC outperforms baseline methods in sim-to-real transfer, achieving an 81% success rate in real robot tasks, demonstrating effective use of human correction data.
The human correction data in TRANSIC was critical for improving average performance, leading to a 124% increase, highlighting its efficacy in sim-to-real transfer tasks.
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