Same policy. Different robot. Different result.
RC measures how physical-AI policies perform on real robots, per body and per task, with the uncertainty shown.
Three ways in.
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The argument ILLUSTRATIVE
Change only the robot, and success moves. Attempts don't.
One policy, one task family, six bodies. Whether the policy tries barely changes. Whether it succeeds spreads about five times as much.
2.5 ptAttempt rate, spread across bodies (σ)
13.1 ptSuccess rate, spread across bodies (σ)
See every body and policy in the Cross-Embodiment Table (XE-Table)
How it works.
Four steps from a robot run to a number you can cite.
- RecordEvery run on the cell is recorded, time-synced and sealed.
- JudgeA versioned VLM judge scores it. An operator checks it blind.
- LedgerThe result lands in an append-only table, body by body.
- ReportYou get per-body numbers with intervals, and what we refused to conclude.
Real runs 0 Eval revenue $0 Everything shown is simulated data i