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Ptask≈pHP_{\mathrm{task}} \approx p^{H}

Why this formula appears here

This compounds badly over long horizons. If a task requires H decisions and each succeeds independently with probability p , the task succeeds with probability Ptask≈pHP_{\mathrm{task}} \approx p^{H}. The independence assumption is generous — real failures correlate, because one slip puts the system into a region where subsequent steps are also more likely to fail — but even the generous version is brutal. For a 300-step manipulation sequence to complete nine times in ten, per-step reliability must be about 0.99965. A per-step reliability of 0.99, which would be an excellent number for almost any perception module, yields a task success rate of roughly five percent. This is straightforward arithmetic rather…

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pHp^{H}

Symbol p^H

pHp^H is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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Ptask≈pH.P_{\mathrm{task}} \approx p^{H}.

Equation 3 · Robotics & Embodied AI

Why the Physical World Is Harder

This equation gives an approximation: it relates the quantities while allowing an approximation.

This compounds badly over long horizons. If a task requires H decisions and each succeeds independently with probability p , the task succeeds with probability Ptask≈pHP_{\mathrm{task}} \approx p^{H}. The independence assumption is generous — real failures correlate, because one slip puts the system into a region where subsequent steps are also more likely to fail — but even the generous version is brutal. For a 300-step manipulation sequence to complete nine times in ten, per-step reliability must be about 0.99965. A per-step reliability of 0.99, which would be an excellent number for almost any perception module, yields a task success rate of roughly five percent. This is straightforward arithmetic rather…

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