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tinyvla2/RESULTS.md

@@ -19,3 +19,49 @@ Test split jaco[956:976] (n=20), GT trajectories ~210mm, zero-prediction floor ~
    N=100. Protocol caveat: warm-start the new ID, more steps, tune LR.
 
 Honest takeaway: **the win is action-space unification, not morphology conditioning.**
+
+## TidyBot (wheeled mobile manipulator) — B vs C morphology test (endpoint mm)
+Held-out mobile morphology (is_mobile=1, holonomic), test eps [95:110], n=15.
+
+| N eps | B (canon+ID) | C (canon+morph) |
+|---|---|---|
+| 0 (zero-shot) | 352 | 419 |
+| 30 | 332 | 383 |
+| 80 | 335 | **329** |
+
+C starts WORSE (OOD descriptor extrapolation) but adapts far steeper (419→329, −90mm)
+vs B's plateau (352→335, −17mm), OVERTAKING B by N=80 on both endpoint (329<335) and
+rotation (24°<28°). First support for morphology conditioning: it helps on a genuinely
+NEW morphology (mobile), unlike held-out arms (jaco: C≈B, both flat). Margin small
+(~2%, n=15) — crossover needs confirmation (more N, seeds).
+
+## Morphology-diversity continue-train (user idea 3) — held-out jaco zero-shot
+| model | training morphologies | morph→fast | morph→slow | jaco endpoint |
+|---|---|---|---|---|
+| C           | 3 (arms only)        | ✓ | ✗ | 178 mm |
+| C-diverse   | 8 (arms + mobile)    | ✓ | ✗ | **150 mm** (−16%) |
+| C-diverse-slow | 8                 | ✓ | ✓ | (training) |
+
+Morphology diversity in continue-training improves held-out-arm transfer 178→150mm:
+the descriptor→conditioning MLP only generalizes once it has seen diverse morphologies
+(arm-only C's encoder was useless/harmful on new morphologies). Caveat: TidyBot
+(331mm) is now IN C-diverse training, so it's not a clean OOD-mobile test; a strict
+OOD-mobile claim needs a held-out mobile robot (leave-one-out or a new dataset).
+
+## Slow-path morphology injection (idea: feed morphology to Qwen prefix too)
+| model | morph→fast | morph→slow | jaco endpoint |
+|---|---|---|---|
+| C-diverse      | ✓ | ✗ | 150 mm |
+| C-diverse-slow | ✓ | ✓ | 162 mm |
+No benefit (slightly worse, within n=20 noise). Fresh morph_to_lm projector (17K
+params) likely underfit in 25k steps; injecting into the frozen LM prefix perturbs
+the semantic latent without clear gain on a held-out arm. Idea sound, didn't pay off here.
+
+## TinyVLA-2 overall verdict
+1. Canonical action space >> native for cross-embodiment transfer (10-45x). DECISIVE.
+2. Morphology conditioning ~= learned ID on held-out ARMS — UNLESS the descriptor
+   encoder is trained on DIVERSE morphologies, which improves held-out transfer
+   (178->150mm). On a mobile morphology (TidyBot) C adapts steeper than B (overtakes by N=80).
+3. Slow-path morphology injection: no clear benefit here.
+Takeaway: unify actions (big win); morphology conditioning helps only with morphological
+diversity in training.