haohw/realrobot-openpi-new-knife-v3

Foundation + Edited Checkpoints — Real-Robot pk (new_knife)

Foundations available in this repo

Folder	Source	Inference config	Notes
mixed_new_knife_v3/best_val/600/	Coworker's training, 600 steps best-val	pi05_real_pk_mixed_new_v3	Real-robot SR ≈ 40 % (per coworker rollout 2026-05-14)
mixed_v2_step1000/	OUR training, 1000 steps	pi05_real_pk_mixed_v2	Untested on real robot. Strict val (own held-out): val_all=0.00998. Use this if mixed_new_knife_v3 underperforms.

⚠ The two foundations use DIFFERENT norm_stats — wires are NOT interchangeable. Pick the inference config that matches the foundation folder name in the table above.

Edited ckpts (from mixed_new_knife_v3/best_val/600/)

Foundation: mixed_new_knife_v3/best_val/600/ (this repo) Edit recipe: cg_distill_vla.py from behavior-uncloning/experiments/maniskill3_mode_editing/ Eval set: real_pass_knife_mixed_v2_eval (10 ep, 1188 frames)

Priority	Folder	Direction	Status	Notes
★ 1 (best)	edits/action_v4_keep_left_step100/	keep_LEFT	directionally correct	only ckpt with measurable L-ward predicted-action shift on R-mode val frames (Δ=−0.053 on R pre-commit, ~9% of GT L-R action spacing). Real signal.
2	edits/action_v4_keep_right_step200/	keep_RIGHT	weak / mixed	best of the keep_right action_v4 candidates by smallest wrong-direction shift. Worth real-robot test as keep_right has no clearly-converged ckpt.
3	edits/action_v4_keep_right_step300/	keep_RIGHT	alternative	other keep_right candidate; in-train val_pref winner but path-3 shift is L-ward (wrong direction). Test for completeness.
4	edits/hidden_v8_keep_left_step550/	keep_LEFT	near-zero shift	hidden_v8 path; predictions barely moved (≤7% of GT spacing), classifier-side metric collapsed (Goodhart). May or may not show on robot.
5	edits/hidden_v8_keep_right_step50/	keep_RIGHT	near-zero shift	same caveat as #4, opposite direction.

All edit ckpts use pi05_real_pk_mixed_new_v3 for inference (same arch + norm_stats as the foundation they were edited from).

Recipes (all on pi05_real_pk_mixed_new_v3 config)

action_v4 (priority #1–3)

--steering-mode action_v4 --use-hidden-classifier --num-modes 2
--gamma 0.1 --beta 1.0 --lr 5e-5 --action-commit-threshold 0.15
--classifier-action-dim 8 --batch-size 8 --num-steps 300 --save-interval 50
--freeze-vit-only

Classifier: v3-style 1vr (hidden + action + progress → P(target_mode)), trained on foundation hidden states + GT actions. Aux losses L_grad_min + L_align shape ∂P/∂a.

hidden_v8_mc_allpairs (priority #4–5)

--steering-mode hidden_v8_mc_allpairs_precommit_gated --num-modes 2
--gamma 0.1 --beta 1.0 --lr 1e-5 --batch-size 32 --num-steps 600 --save-interval 50
--unpref-gate-mode classifier_conf  (P_true<0.5)
--freeze-vit-only

Classifier: v5h-mc (hidden-only → 2-class softmax). NOTE: edit & eval used the SAME classifier → 4-bucket P(target) eval saturated to 0.998 (Goodhart). Cannot judge edit success from val metric; only real-robot rollout will tell.

Methodology caveats

• In-train val_pref ranking is unreliable for picking the SR-best ckpt — see 1pillar Phase 3 / Phase 3b discussions for the same observation.
• hidden_v8 path failed the path-3 sanity check: predicted action chunks barely change vs foundation (Δ ≤ 0.04 on a GT L-R spacing of 0.61). Likely cause: LLM hidden gets pushed to maximize hidden-only classifier, but action expert decodes back to original distribution. Sim 1pillar Phase 3b worked because there the eval was rollout SR (not classifier P), and sim's hidden-action coupling is tighter.
• action_v4 keep_left step 100 is the only ckpt with measurable directional shift. Later ckpts (step 200/300) over-edit and collapse to wrong direction, opposite of 1pillar sim where step 200 was peak. Real-robot may want γ smaller or fewer steps.

Splits (canonical to OUR side)

• Foundation training data (this ckpt): real_pass_knife_mixed_v2_train 70 ep
• Eval data: real_pass_knife_mixed_v2_eval 10 ep, mixed_ep IDs [1, 7, 10, 29, 31, 36, 45, 52, 54, 66]
• Mode encoding: 0 = left, 1 = right
• Asset id (norm_stats): real_pass_knife_new_mixed_train (matches the foundation's training norm stats, NOT mixed_v2_train)