Across Parts 23-34 the verdict on the success-rate scoreboard is clear and modest: the value-aware abstraction-in-loop ties PPO where the prior fits, hits a physical ceiling on PickCube, and trails PPO on harder variation. This capstone asks the better question — what is the abstraction actually good for? — along the three axes a user flagged: interpretable, reusable, stable.

Why success rate is the wrong scoreboard

task / regime abstraction PPO note
PandaPickCube (prior fits) ~0.79 ~0.82 tie; abstraction more sample-efficient (Part 27)
PickCube ceiling ~0.83 ~0.83 physical (far-reach upright IK 99.9% infeasible, Part 30)
PickCubeOrientation, fixed prior 0.33 0.82 abstraction loses (wrong prior, Part 31)
PickCubeOrientation, parametrized ~0.67 0.82 recovers ~70% (Part 33)
PickCubeOrientation, fuller param ~0.68 0.82 class ceiling (Part 34)

On asymptotic success the abstraction never beats PPO. If that were the whole story, the answer would be “don’t bother.” But three other axes tell a different, more defensible story.

1. Reusability — real, but conditional on a parametrized prior

The abstraction transfers across tasks only when its prior is parametrized to the task’s degrees of freedom:

  • Fixed top-down/fixed-yaw prior on a random-yaw target → 0.33 (worse than learning-from-scratch, Part 31).
  • Parametrize the prior by the target yaw → 0.67, recovering ~70% of the gap (Part 33), with no further gain from richer parametrization (Part 34).

So reusability is not free and not automatic: a fixed abstraction is a liability off its design regime; a parametrized abstraction is a genuine, transferable asset (and far more sample-efficient early — it carries a head-start the from-scratch learner lacks). The design lesson: parametrize priors to the task’s variation, do not hard-code them.

2. Stability — a matched prior makes the correction small and stable

Stability tracks how well the prior fits:

  • Fixed PickCube residual: fragile optimum — peaks ~0.79 then collapses under continued training (Part 28). A large correction on a so-so prior is brittle.
  • Orientation-aware residual: peak ≈ final (0.68, ~0.03 drop, Part 33/34) — a small correction on a matched prior is stable.
  • Over-complicated prior (v2 combo): larger peak→final drop (0.63→0.55, Part 34) — stacking levers re-introduces instability.
  • For external calibration: PPO is stable at its ceiling; and the InFOM reproduction shows the other face of instability — 1 of 3 seeds collapsed to 0.16 while two hit ~0.97 (seed-variance), a reminder that “stable” must be measured across seeds, not asserted.

The pattern: the right (matched, parametrized) prior buys stability — the learned part stays a small, robust correction instead of a large, fragile one.

3. Interpretability — structural, and the campaign relied on it

This axis is structural rather than a single metric, but it did real work throughout:

  • The controller is a human-readable phase machine (reach → descend → grasp → lift → place), not a black-box MLP. You can read what the policy is trying to do.
  • Failures are phase-attributable: the ~17% PickCube tail was localized to the grasp phase orientation (Parts 28-29), then proven a physical far-reach-upright limit (Part 30) — a diagnosis you can only make when the policy’s structure is legible.
  • The residual-magnitude diagnostic told us where the learned part was doing work: “the residual was already compensating for the controller’s orientation limit” (Part 29) — which is why hard-coding the tilt was redundant. That is an interpretability win with direct experimental consequences.
  • PPO offers none of this: a 0.82 success rate and an opaque network.

The honest takeaway

An abstraction does not beat strong model-free RL on asymptotic success — on PandaPickCube it ties and hits a physical ceiling; on harder variation it trails even when parametrized. Its value is elsewhere, and it is real: sample-efficiency (Part 27), stability when the prior is matched, reusability when the prior is parametrized to the task’s DOF, and interpretability (legible structure, phase-attributable failures, a residual diagnostic). That is a narrower but far more defensible claim than “abstraction wins” — and it is what the data actually supports. The route forward for capability is learning the skill (end-to-end RL); the route forward for trustworthy, transferable, debuggable control is a parametrized abstraction with a small learned residual on top.

Caveats

A synthesis of Parts 23-34 (all numbers cited there, read from JSON). The interpretability axis is structural, not a new quantitative metric — claims are tied to specific diagnoses the structure enabled (Parts 28-30), not to a fresh number. Reusability/stability are quantified from the orientation-task arc (Parts 31/33/34) and the PickCube fragile-optimum (Part 28); the InFOM seed-variance (0.16 vs ~0.97) is from the OGBench cube reproduction. Data across exp/tdmpc_glass/{generalization_PandaPickCubeOrientation*, hl_*} and the InFOM repro_results/. Prior: Parts 27 (tie + sample-efficiency), 28 (fragile optimum), 30 (physical ceiling), 31/33/34 (reusability arc).