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tactino merged 1 commit into from
Sep 18, 2026
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docs: put the robot above the numbers #28

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8 changes: 8 additions & 0 deletions README.md
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Expand Up @@ -7,6 +7,14 @@ The training side of PlugRL: it holds the policy and the learning algorithm,
batches inference across every connected env client over WebSocket, and learns
from the feedback those clients send back.

<img src="https://plugrl.github.io/media/libero-demo.gif" width="360"
alt="A Franka arm in LIBERO reaching for and picking up an object, seen from the policy's own camera">

*The **unmodified** pi0.5, driven through this server on `libero_spatial` task
0 - three episodes, all three successful. These are the frames the env client
sends as observations, at the policy's native 224x224, not an outside camera.
The fine-tuned policy is the one that scores zero, below.*

**A full-size pi0.5 has run end to end through it on LIBERO** - inference,
feedback and FPO training - with the server's record of episodes and steps
reconciling exactly with the clients'. As a control, the unmodified checkpoint
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