A dual-arm SO-101 table-setting system for the Intel Bimanual VLA Manipulation track, where nearly every capability exists because a failure was measured and diagnosed before being fixed. The gripper initially couldn't pick anything up. Not a tuning problem, but a MuJoCo convex-hull artifact: the finger's collision hull is a 21.7° ramp across the grasp region, and no grasp height could fix a ramp that moves with the tool frame. The fix was explicit jaw pads fitted to the finger's real flat faces. An in-air handover between the arms turned out to be geometrically impossible on this platform: the gripper hull is wider than the longest grasp feature in the scene, even at the best pose found by exhaustive search. So the handover is table mediated, and that's documented, not assumed. Scripted pick success went from 57.5% to 92.5% through three measured fixes. A trained ACT policy (44k steps, 134 demonstrations) initially placed only 3 of 40 props and fixated on one object. Diagnosis: no language conditioning in stock ACT, a collapsed VAE latent, and class imbalance. Conditioning was added using ACT's existing spare observation slot. A second dataset with randomized starting states isolated one cause: conditioning now measurably redirects attention toward the named object, while the two
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