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blog/agibot-world/index.html

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Does AgiBot World boost policy learning at scale?
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</h4>
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<figure class="w-full max-w-wide mt-3 mb-3 laptop:mt-6 laptop:mb-6">
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<figure class="w-full max-w-wide mt-3 laptop:mt-0">
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<div id="fig3-1"></div>
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</figure>
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<figure class="w-full max-w-wide -mt-3 mb-3 laptop:mb-6">
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<figcaption class="text-white text-xs mt-3 leading-loose">
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<figcaption class="text-white text-xs mt-3 laptop:mt-0 leading-loose">
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Figure 3: Policies pre-trained on our dataset outperform those trained on OXE in both seen (0.77
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v.s. 0.47) and out-of-distribution scenarios (0.67 v.s. 0.38).
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</figcaption>
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<figure class="w-full max-w-wide mt-3 mb-3 laptop:mt-0 laptop:mb-6">
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<div id="fig4"></div>
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<figcaption class="text-white text-xs mt-3 laptop:mt-0 leading-loose">
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Figure 4: We evaluate different models pre-trained on the AgiBot World dataset, where GO-1 significantly outperforms previous SOTA policy RDT.
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<div id="fig5"></div>
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<figcaption class="text-white text-xs mt-3 leading-loose">
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Figure 5: We evaluate how model performance scales with data size.
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