docs: put the robot above the numbers - #28
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A clip of the unmodified pi0.5 on libero_spatial task 0, three episodes, all successful, captured through this server with the env client's recorder on. The frames are the observation stream, not an outside camera. It is served from the documentation site rather than committed here: the clip is 934 KB at the policy's native 224x224 and this repository caps added files at 500 KB, so carrying it would mean a choppier or smaller loop for no reason. That makes this depend on plugrl.github.io#9 - merged before that deploys, the image is broken. The caption names it as the baseline; the fine-tuned policy is the one that scores zero further down the page.
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The front page says a full-size pi0.5 ran through this server. This adds the clip that shows it, above the numbers.
Merge order
This depends on plugrl.github.io#9. The image is served from the documentation site -
https://plugrl.github.io/media/libero-demo.gif- which exists only once that pull request is merged and Pages redeploys. Merged before then, the README shows a broken image.Why the image is not in this repository
check-added-large-filescaps additions at 500 KB. The clip is 934 KB at the policy's native 224x224, and cutting it to fit would mean either a visibly choppy loop or a downscale below the resolution the policy actually sees - for no gain, since the site repository is a static site that already carries assets and is already linked from this README several times.What the clip is, and what the caption has to say
An evaluation run on the cluster -
evalmoves no weights - one env client onlibero_spatialtask 0, three episodes, all three successful. The frames are the observation stream the client sends over the boundary, so this is what the policy sees rather than a camera pointed at a simulator.The caption says it is the unmodified checkpoint, and that the fine-tuned policy is the one scoring zero further down. Without that, a reader meeting a successful robot two paragraphs above "the reinforcement learning result is negative" will read the clip as the trained policy. It is the baseline.
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