📊AI Dashboard
What you are seeing (high‑level)
These two pictures demonstrate the core logic of KNX Permissionless Robotic AI. At a glance:
A user submits a natural‑language task. KNX broadcasts it to multiple AI “miners” (policy providers).
Competing policies are evaluated across a consistent set of metrics. A ranking is produced.
The top policy is deployed to the real robot (or a certified hi‑fidelity sim) for physical execution.
Independent AI‑Verifiers confirm the Proof‑Of‑Physical‑Execution (PoPE/PoPW) and settlement is processed in stablecoins.
How we wire models and verifiers
Shortly, we have integrated multiple VLA models and connected them with an AI‑Verifier layer:
We onboarded 3 VLA starting points:
OpenVLA(no fine-tune),OpenVLA · OFT(parameter‑efficient finetune) andPI‑0.5.A cloud, 3D physical environment is used for safe rollouts and test iterations.
Whenever a user sends a task, it is broadcast to multiple AI‑Miners; each miner returns a candidate trajectory/video.
AI‑Verifiers then score each result (accuracy, safety, speed, optimal track, energy efficiency, stability) and select the best one.
The best model result is used in the real‑world execution step.
Verifiers ensure Proof‑of‑Physical‑Execution, post a ScoreRoot, and payouts/penalties are processed on‑chain in stablecoins.
Under the hood, we use a vision LLM (GPT‑Vision mini–style) to produce strict JSON metrics from frames + instruction. The verifier layer remains pluggable, so different scorers can be swapped or combined (consensus).
Why this matters
Makes robot work markets permissionless: anyone can compete with better policies; the best policy wins—measured, not promised.
Creates a clean separation of concerns: miners focus on control quality; verifiers focus on evidence and metrics; settlement is stablecoin‑native.
Scales from simulation to real robots with the same interfaces (lock → prove → settle).
Related reading
AI Models · Overview / Taxonomy / Supported
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