Planner-family transfer
Reproduces tab:planner-transfer: the same checkpoints scored under six planners, to test whether
VIScore depends on the search procedure it was developed with.
python reproduce/planner/planner_tables.py # the six blocks, as printed in the paper
python reproduce/planner/planner_tables.py --latex # LaTeX rows
Verified against the published table: 6 blocks x 7 rows, 0 mismatches.
Why this lives in its own folder
Every arm must be evaluated on the same checkpoints for the comparison to be paired, so this
experiment uses a fixed 36-checkpoint set rather than the pools of reproduce/tables.py. All 25 of
its training runs also appear in those pools, and 20 of the 36 checkpoints are themselves cells of
the development or held-out pool; the remaining 16 are further epochs of the same runs, which widens
the quality range any single pool covers. It is a subset of the paper's runs, not of its
reported checkpoints, so these numbers are not comparable to reproduce/tables.py's — the
pools differ, and so do the success-rate levels they average over.
A refresh of this experiment on the full 103-checkpoint held-out pool is in progress. It is not what the paper reports, and it is not published here.
Files
| File | Contents |
|---|---|
planner_cells.csv |
36 checkpoints: planner-independent metrics, one sobriety per probe family, one success rate per planner |
planner_tables.py |
recomputes the table; no GPU, no checkpoints, no dataset |
The probe assignment is a rule, not a choice
Sobriety is the one factor that contains a search, so each planner block needs a probe. The rule was declared before any outcome was seen — same search family, matched budget fraction:
| planner | probe |
|---|---|
| CEM, MPPI, iCEM, predictive sampling | mini-CEM (sobriety_mcem) |
| gradient, 100 starts x 30 steps | mini-AdamW (sobriety_madam) |
| single-start gradient, 1 start x 100 steps | single-start mini-AdamW (sobriety_ss) |
Selecting per planner whichever probe correlated best would manufacture the result: predictive sampling's pooled correlation is +0.51 under the declared probe and +0.80 under the one that would have flattered it. The frozen table carries all three sobriety columns so this is checkable rather than described.
Scope
All six planners optimise an action sequence through the learned predictor and replan on a receding
horizon — that is, they are model-predictive controllers. Amortized policies (an inverse dynamics
model, goal-conditioned behaviour cloning, a learned actor) are a different class and are outside
what this table tests: they neither roll the predictor out nor search at deployment, so veracity
loses its causal path and sobriety has no estimand at all rather than merely a missing estimator.
visscore's exclude option exists for that case.