The article details TRANSIC, a method designed for sim-to-real policy transfer in robotic learning by harnessing online corrections. It highlights how TRANSIC surpasses traditional methods, like IWR, in scaling with human effort, notably improving success rates as human correction data increases. The method also effectively avoids issues like catastrophic forgetting by focusing on gated residual policies. Moreover, TRANSIC displays intriguing emergent behaviors, exemplified by its ability to generalize to new objects without prior exposure, showcasing its significant advances in robotic learning capabilities.
TRANSIC demonstrates superior human data scalability, achieving a 42% average success rate improvement with increased corrections, significantly outpacing the 23% improvement of IWR.
The integrated framework of TRANSIC effectively implements online correction by allowing the system to learn gated residual policies, combating issues like catastrophic forgetting.
Interestingly, robots trained through TRANSIC displayed unexpected emergent behaviors, including a capacity for zero-shot generalization to new objects, showcasing its advanced learning potential.
In contrast to IWR, which struggles with increased human data due to performance plateaus and declines, TRANSIC leverages human corrections to enhance learning efficiency.
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