I am indeed impressed by @RewardAI_ , huge congrats to @zipengfu @chenwang_j @yifengzhu_ut ! Everyone knows the demo is super impressive that multiple embodiment are working autonomously with excellent speed. But fundamentally it's the omni-embodiment model with model-level abstraction that supports manipulation-level precision. Cross-embodiment is not new. But previously we use retargeting. We first translate human actions into embodiment-specific robot actions before training the policy. But now Reward AI is doing this at the policy level. The representation is not published yet but I expect it's the same policy learns an embodiment-agnostic action representation from human data, and that representation can then be executed across different robot embodiments through their respective control layers. Purely-human data is not new. For example, @GeneralistAI GEN-1 pretrain stage takes 0 robot data. @sundayrobotics takes the similar approach for it's ACT-1 (but added robot data in ACT-2). But it's new that Reward AI achieves cross-embodiment without embodiment-specific retargeting, and with that speed? I am indeed curious. So I think here are the noteworthy in its upcoming blog: Q1: how is the action representation in this "single policy"? How is that cross-embodiment representation different from the traditional one? Q2: how does human data enter the pipeline? How is the synchronization between multimodal data with different Hz? Q3: Please notice that although it aims to achieve the abstraction on data- and policy-level, it didn't kill the embodiment-specific adaptation, but push it to the action layer. Then how heavy is the work to integrate each embodiment? Although it depends on my Q1, everyone who has done that knows it's not a light work.