Add htn to v1.0/v1.1 baselines, fix stale fp401-t3a note, add box_threshold and tier4 investigation addenda

README.md

@@ -74,7 +74,9 @@ a GPU/Unity this Space's free tier doesn't have — is live at
 Every task pairs a natural-language
 instruction with a structured goal/object/target spec and a
 machine-checkable success predicate, across 5 real AI2-THOR scenes
-spanning all 4 iTHOR room types.
+spanning all 4 iTHOR room types. `v1.0` is frozen (see "Versioning"
+below) — for a larger, 9-scene extension with a fourth difficulty
+tier, see [`v1.1`](#milo-benchmark-v11) (below, same page).
 
 **This is a synthetic, AI2-THOR-derived dataset, not a human-collected
 one.** Every instruction was authored by a human against a live scan
@@ -146,13 +148,14 @@ Reference implementation: `backend/planning_evaluation/live_state.py`'s
 `check_goal_live()` in the [MILO
 repository](https://github.com/NaishaShetty/MILO) (this dataset's origin repo).
 
-### Baselines (v1.0, real runs, all three planners)
+### Baselines (v1.0, real runs, all four planners)
 
 | Planner | Goal success | tier1_locate | tier2_pickup | tier3_store | Notes |
 |---|---|---|---|---|---|
 | `rule_based` | 24/25 (96%) | 10/10 | 10/10 | 4/5 | Deterministic, no LLM. One failure is a real AI2-THOR placement/geometry limit, not a planner defect. |
 | `behavior_tree` | 24/25 (96%) | 10/10 | 10/10 | 4/5 | Same task/plan-step outcomes as `rule_based` (shares its goal-handler templates); same single failure. |
-| `react` (`qwen2.5:7b`, Q4_K_M, via Ollama, local) | 20/25 (80%) | 10/10 | 10/10 | 0/5 | `goal_success`/`execution_success`/`plan_success` agree on every episode — no predicate artifact. All 5 failures are genuine multi-step reasoning failures (the model proposes an action before its precondition chain is satisfied, e.g. `pickup` before navigating close enough), not infrastructure. Run on an RTX 4050 Laptop GPU (6GB VRAM, 82%/18% GPU/CPU split), zero rate-limit retries needed (fully local, no quota). |
+| `htn` | 24/25 (96%) | 10/10 | 10/10 | 4/5 | A real Hierarchical Task Network engine (compound tasks, a state-conditional method library, recursive decomposition) — not a second implementation of `rule_based`'s control flow. Matches `rule_based`/`behavior_tree` exactly, including the identical single failure, at comparable latency (~647ms/episode avg vs. ~620–633ms). Slice 1 only: covers `tier1_locate`/`tier2_pickup`/`tier3_store`, not this project's `tier4_multi_step` tier (see `v1.1`'s card below). |
+| `react` (`qwen2.5:7b`, Q4_K_M, via Ollama, local) | 20/25 (80%) | 10/10 | 10/10 | 0/5 | `goal_success`/`execution_success`/`plan_success` agree on every episode — no predicate artifact. All 5 failures are genuine multi-step reasoning failures (the model proposes an action before its precondition chain is satisfied, e.g. `pickup` before navigating close enough), not infrastructure. Run on an RTX 4050 Laptop GPU (6GB VRAM, 82%/18% GPU/CPU split), zero rate-limit retries needed (fully local, no quota). Reconfirmed unchanged (identical 20/25, identical per-task failures) after later detection-threshold/prompt fixes described below — those fixes don't touch this planner's LLM-proposal path. |
 
 `react` was also attempted against Gemini's free tier
 (`gemini-flash-latest`) first; that attempt is **not** a valid
@@ -193,21 +196,27 @@ keeps as ground truth.
 
 ### Known limitations — kept deliberately, not hidden
 
-Two `tier3_store` tasks are **known, currently reproducible failures**
-against the reference planner, kept in v1.0 on purpose as honest
-negative examples rather than removed to inflate a headline number:
+When this section was first written, two `tier3_store` tasks were
+**known, currently reproducible failures** against the reference
+planner, kept in v1.0 on purpose as honest negative examples rather
+than removed to inflate a headline number:
 
 - `milo-v1-fp301-t3a` ("Put the book away in the drawer.") fails at
   execution due to a real AI2-THOR physics/geometry limit — the
   drawer opens correctly, but AI2-THOR cannot find room for this
   particular book inside this particular drawer's real interior
-  volume. Not a planner defect.
-- `milo-v1-fp401-t3a` ("Put the spray bottle away on the shelf.")
-  fails because the reference rule-based planner tries to `open` the
+  volume. Not a planner defect. **Still failing** — this is the one
+  remaining failure in the Baselines table above, reproduced
+  identically by `rule_based`, `behavior_tree`, and `htn`.
+- `milo-v1-fp401-t3a` ("Put the spray bottle away on the shelf.") used
+  to fail because the reference rule-based planner tried to `open` the
   shelf before placing — a shelf is a valid receptacle but is not
-  openable, and the planner's `_deposit()` logic doesn't yet check
-  `openable` before deciding to open. A genuine, reproducible planner
-  bug, tracked in the origin repo's roadmap.
+  openable, and `_deposit()` didn't check `openable` before deciding
+  to open. **Fixed since this section was first written**: `_deposit()`
+  now checks the target's `is_openable` before inserting an `open`
+  step (see `backend/planner/rule_based.py`). This task now passes for
+  every planner in the Baselines table above — it is not the source of
+  any of their current failures.
 
 A different, honest limitation of the success predicate itself:
 `tier1_locate`'s live check (existence of an object of the named
@@ -286,6 +295,30 @@ reliability on AI2-THOR's synthetic renders — a different, still-open
 question this dataset now measures separately instead of conflating
 with the first.
 
+#### Second addendum — detection threshold properly validated and changed (0.35 → 0.25)
+
+The root-cause data point above (0.15 recovers detections but was
+never validated for false positives) has since been followed up
+properly, not left open: a dedicated validation set (8 real AI2-THOR
+scenes, 9 true positives, 14 confirmed-absent true negatives) swept
+real precision/recall at `box_threshold` 0.15/0.20/0.25/0.30/0.35
+(two independent runs, consistent). **0.25 was adopted as the new
+default** (`GroundingDINODetector`'s default `box_threshold`, changed
+from 0.35): same recall as 0.15 (77.8%) with meaningfully better
+precision (63.6% vs. 53.8%), and better recall than the old 0.35
+(77.8% vs. 55.6%) with equal-or-better precision. This is a real
+config-default change to the detector this dataset's `perceived_by_agent`
+signal depends on, not a re-measurement of the numbers above — the
+`perceived_by_agent` counts reported in the first addendum
+(`rule_based`/`behavior_tree` 6/10, `react` 5/10) were measured at the
+old 0.35 threshold and have not been re-run at 0.25; treat them as
+historical, not current, if reproducing this check. `goal_success` is
+unaffected either way (it has never depended on vision detection for
+any tier). See the origin repository's `docs/roadmap.md` for the full
+validation methodology and a methodology bug this pass also caught and
+fixed (`GroundingDINODetector` sometimes merges adjacent prompt phrases
+into one compound label, which naive exact-string matching missed).
+
 ### Collection methodology
 
 1. For each of the 5 candidate scenes, a live AI2-THOR
@@ -511,19 +544,59 @@ addendum) on both of a `tier4_multi_step` task's independent sub-goals
 in several cases (`FloorPlan202`, `FloorPlan402`'s second sub-goal,
 `FloorPlan401`), not only single-object `tier3_store` tasks.
 
-### Baselines (v1.1, real runs, all three planners)
+### Baselines (v1.1, real runs, all four planners)
 
 | Planner | Goal success | tier1_locate | tier2_pickup | tier3_store | tier4_multi_step | Notes |
 |---|---|---|---|---|---|---|
-| `rule_based` | 50/54 (92.6%) | 18/18 | 18/18 | 7/9 | 7/9 | Both `tier3_store` failures are the same real AI2-THOR placement-geometry limit `v1.0` already documents (`FloorPlan301`, now also `FloorPlan203` -- same task shape, independently reproducing). Both `tier4_multi_step` failures are a real, newly-surfaced harness gap (not a planner defect): a failed `place` in sub-goal 1 leaves the object physically held, and `WorldState` re-seeding between sub-goals has no signal for that, so sub-goal 2's plan assumes an empty hand and AI2-THOR rejects it. |
+| `rule_based` | 50/54 (92.6%) | 18/18 | 18/18 | 7/9 | 7/9 | Both `tier3_store` failures are the same real AI2-THOR placement-geometry limit `v1.0` already documents (`FloorPlan301`, now also `FloorPlan203` -- same task shape, independently reproducing). Both `tier4_multi_step` failures are a real, newly-surfaced harness gap (not a planner defect): a failed `place` in sub-goal 1 leaves the object physically held, and `WorldState` re-seeding between sub-goals has no signal for that, so sub-goal 2's plan assumes an empty hand and AI2-THOR rejects it. See "`tier4_multi_step` investigation update" below for the current, precise per-episode status. |
 | `behavior_tree` | 50/54 (92.6%) | 18/18 | 18/18 | 7/9 | 7/9 | Same task/plan-step outcomes as `rule_based` (shares its goal-handler templates); same failures for the same reasons. |
+| `htn` | 43/45 (95.6%)¹ | 18/18 | 18/18 | 7/9 | not attempted¹ | A real Hierarchical Task Network engine (compound tasks, a state-conditional method library, recursive decomposition) -- not a second implementation of `rule_based`'s control flow. ¹Slice 1 only: does not yet support `tier4_multi_step`'s multi-subtask decomposition, so those 9 tasks were deliberately not attempted, not scored as failures -- goal success is out of 45, not 54. Both `tier3_store` failures (`milo-v1-fp301-t3a`, `milo-v1.1-fp203-t3a`) are the identical placement-geometry limit `rule_based`/`behavior_tree` hit on the same pair -- no new failure mode across the 4 additional scenes, i.e. `v1.0`'s 5-scene result generalizes. |
 | `react` (`qwen2.5:7b`, Q4_K_M, via Ollama, local) | 36/54 (66.7%) | 18/18 | 18/18 | 0/9 | 0/9 | `tier4_multi_step`'s 0/9 is the arithmetically expected composition of `tier3_store`'s already-0% rate (a tier requiring two consecutive successful `store` sequences cannot score above a planner's single-`store` success rate) -- confirmed by inspecting each failure, not assumed: every one shows the same precondition-mis-sequencing pattern `v1.0`'s Addendum 3 already documents. `goal_success`/`execution_success`/`plan_success` agree on every episode; 0/54 episodes needed a retry. |
 
 See the origin repository's `experiments/reports/
 phase_e_milo_benchmark_report.md`'s Addendum 7 for full methodology,
-per-failure root-cause detail (including the `tier4_multi_step`
-WorldState-reseeding gap above, tracked open in `docs/roadmap.md`, not
-fixed in this pass), cost/latency, and exact reproduction commands.
+per-failure root-cause detail, cost/latency, and exact reproduction
+commands.
+
+#### `tier4_multi_step` investigation update
+
+The `WorldState`-reseeding gap noted in the `rule_based`/`behavior_tree`
+row above has since been investigated in depth (not fixed and
+re-benchmarked -- the table above is still the original publish run).
+Two distinct causes were found behind the two known-failing episodes:
+
+- **`milo-v1.1-fp302-t4a`**: the engine-crash *symptom* (the planner
+  blindly issuing a doomed action once a hand is already occupied) is
+  fixed and verified -- re-seeding `robot_holding` with one detection
+  call per object name (rather than one joint multi-phrase prompt) at
+  a validated `box_threshold=0.25` correctly detects the held object
+  and lets the planner correctly *decline* to plan the second sub-goal
+  instead of crashing AI2-THOR. This fix is demonstrated via a targeted
+  investigation script, not yet merged into the production benchmark
+  harness's `_seed_initial_state_from_live_metadata()` path -- the
+  table above does not yet reflect it. The task's `goal_success` is
+  still `False` either way, now solely because sub-goal 1 hits the same
+  real placement-geometry limit `tier3_store` already has, unrelated to
+  this bug.
+- **`milo-v1.1-fp201-t4a`**: still open. Per-name detection queries do
+  find the held object, but at a measured depth (0.853m) outside the
+  held-object heuristic's `HELD_OBJECT_MAX_DEPTH_M=0.5m` cutoff --
+  calibrated against smaller held objects (0.347m-0.459m) than this
+  one. A separate, nearer, *not*-held object was also wrongly preferred
+  by the heuristic's "closest wins" tie-break. The concrete next step
+  identified (seeding `robot_holding` from AI2-THOR's own live
+  `isPickedUp`/`inventoryObjects` metadata, sidestepping both the
+  detection-prompt and depth-calibration dependencies) has not been
+  implemented.
+
+A related, broader finding surfaced during this investigation: Grounding
+DINO's confidence measurably drops under multi-phrase joint prompts
+(confirmed on 2 independent objects/frames) -- this does not affect
+`goal_success` for any tier in this dataset or `perceived_by_agent`'s
+`tier1_locate` check (both already query one object name at a time),
+but does affect some manual demo scripts in the origin repository. Full
+chain, exact numbers, and regression tests: the origin repository's
+`docs/roadmap.md`.
 
 ### Second local model for `react`: `qwen2.5:3b` comparison