Baoyu Li ✈️ CoRL 2026
@BaoyuLi6
CS PhD @GeorgiaTech @ICatGT | Prev. MSCS @UofIllinois @siebelschool | Robot Learning 🤖 | Opinions are my own.
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Egocentric human data is abundant, but human motion is not always positive supervision for robot policy due to embodiment gaps. Naive BC co-training can HURT performance ☹️.
🌟Our key finding in **EgoWAM**: the state-prediction branch of a World Action Model effectively bridges this embodiment gap, enabling robot performance to scale with diverse **in-the-wild** human data.
💡The key question then becomes: what world representation transfers best across embodiments?
👇🏻Let’s take a deep dive into it:
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