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Equation 3 · Why the Physical World Is Harder

What does this equation mean?

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

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This equation gives an approximation: it relates the quantities while allowing an approximation. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.

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PtaskP_{\mathrm{task}}

Symbol P_task

the probability.

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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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≈

≈

Approximately equal to; the equality is not exact.

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subscript

subscript

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

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superscript

superscript

A raised number can be a power. When it is a label or bound, it selects a case or the upper limit of a sum; the formula’s structure distinguishes these uses.

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How to interpret it

Its accuracy depends on the assumptions and range of use described in the article.

What the article says around this equation

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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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 than a finding from any cited study, but it explains an otherwise puzzling pattern: policies that look near-perfect on individual skills collapse on chained ones, and the collapse is not a bug to be found, it is the exponent.

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