Demonstration only
12.0s
cycle time ceiling
Reliability80.0%
Expected successes80.0
Failure cost80.0
Net successful cycles73.3
A failure-aware experiment designer
Can a robot move beyond imitation?
Put the ceiling on the table. Compare copied performance with a failure-aware hypothesis using your own speed, reliability, trials, and cost assumptions.
Inspired by the shift described by @TheHumanoidAI around Kinetiq Ascend. No product performance is assumed.
Design the comparison.
This is a scenario calculator, not a product benchmark. Every result is derived from the six assumptions below.
Projected experiment
+11.9 net cyclesFailure-aware hypothesis
8.4s
projected cycle time
Reliability90.0%
Expected successes90.0
Failure cost40.0
Net successful cycles85.2
Net successful cycles = expected successes - total failure cost / cycle time. This converts the entered failure cost into cycle-time equivalents so both paths can be compared.
From claim to test
Failure becomes a signal only when you measure it.
A useful comparison keeps the human ceiling visible, states the improvement hypothesis, records every failure in the same units, and defines the stop condition before the robot moves.
Freeze the copied ceiling.
Record cycle time and successful attempts from the demonstration policy. That gives the experiment a control instead of a slogan.
Price every failed attempt.
Use one failure-cost unit across both paths. Recovery time, damaged material, and resets can be normalized before a run begins.
Reward improvement, not motion.
Compare net successful cycles and keep the raw reliability and cost visible. A faster robot that burns more failures has not automatically won.
One model. Three visible levers.
The dense map below updates from the current scenario so the core tradeoff remains inspectable.
+11.9
successful cycles after failure cost
8.4s
Projected from the entered speed-improvement hypothesis.
90.0%
Baseline failures reduced by the recovery hypothesis.
40.0
Total remaining failures multiplied by your cost per failure.