Pre-registration: grasp-demos-v2 regen

2026-08-17 06:16Z, posted in-channel (msg 1538793633703268372) before launch; launched 06:16:38Z as unit demo_gen_v2 on the A100 box.

Plain words: we are re-generating the 5,000-episode scripted-expert demo dataset with three improvements queued since v1 shipped — a smoother, more accurate expert; the robot’s camera bracket drawn the way the real one looks; and the wrist camera moved to the pose we just fitted against real rig footage. Everything else — spawn protocol, tints, seeds, scale — is deliberately identical to v1, so any change in the keep-rate or downstream training is attributable to those three knobs.

Command (dry-run verified)

sim.collect_demos_sharded \
  --out ~/datasets/fontaine/grasp_demos_v2 \
  --repo-id fontaine/grasp_demos_v2 \
  --shards 96 --target-kept 5000 \
  --seed-start 10000 --seeds-per-shard 2000 \
  --spawn-version v2.1 --tint-band mix70 \
  --bracket-appearance real --wrist-pose refit

96 shards round-robined over the box’s 8×A100; SAME seed universe as v1 (10000+, stride 2000) so spawn draws match and deltas attribute cleanly (seed policy: same seeds for comparability — this is a regen, not a resume).

Changes vs v1 (all else identical)

• expert v1.3 (9ba7d30): place-center bar 3→1.5 cm, retreat glide 5°/tick, tail budget 450 — bench kept 52.5%, parked 98.6% (n=120).
• bracket_appearance='real' (4a9bf5c): leader bracket hidden, follower ring filled; render-only, physics oracle-pinned.
• wrist_pose='refit' (4b14b1f): fitted wrist-cam pose (pitch −23°, yaw +14°, roll −9.5°, camera-frame offset +3.3/+1.3/−3.0 cm); held-out G2+G3 PASS, G1 −44.5% vs the −50% bar (disclosed) — riding per the 05:46Z ship-and-ride recommendation unless vetoed.

Receipt: launch HEAD 7078cf0 (banked as expert_head in provenance; the driver manifest now carries bracket_appearance and wrist_pose — pass-through plumbing landed this session, check.py 980 green).

Anchors and gates

• Kept-rate anchor 45.9% — v1’s realized rate (5,000/10,883 attempted, same seeds). Expert v1.3’s bench says ≥ that. Halt-and-diagnose bar: sustained aggregate < 40% once ≥ 500 attempted — that is a render/protocol regression, not luck.
• Complete: 5,000/5,000 kept, 0 failed shards; provenance carries the three new knobs + expert head.
• GPU-hours gate: 40 (v1 ran 2h07m wall ≈ 16.9 GPU-h).
• Boundary: merge (sim.merge_demo_shards, bit-identical-oracle path) → upload public fontaine-grasp-demos-v2 → dataset card + results post with the kept-rate verdict. ETA ~2.5–3.5 h from v1’s wall clock.