Does a Learned Corrector Beat a Simple Retreat? Evidence from a Frozen VLA
Organizations: University College London (UCL), London, United Kingdom
Abstract
Before deploying runtime recovery for a frozen vision-language-action (VLA) policy, one must establish that an intervention improves success beyond ordinary run-to-run variation and that its complexity adds value over a simple action. We evaluate these questions on frozen across four RoboTwin tasks. For each test seed, we pair rollouts with and without correction and include a same-seed base-policy re-run as a placebo. Seed-cluster intervals and prespecified comparison rules assess net gains against stochastic outcome changes. Across 3,888 paired episodes, the full pipeline raises success on beat_allowbreak block_allowbreak hammer by ,pp (95% interval ), with no detectable gain on the other three tasks at the deployed weight. Among failed base episodes on the responsive task, succeed on a plain re-run, compared with after correction; many nominal rescues therefore reflect the base policy's own variability. A fixed-time trigger and scripted return to an earlier joint configuration produce a net gain with no detected difference from the learned pipeline across two rounds, although our prespecified equivalence criterion is not met consistently. Pausing and a constant-action control do not yield comparable gains. On this benchmark, the decision to intervene depends strongly on the task, and a paired placebo plus a simple retreat baseline are needed to establish what learned correction contributes.
Figures & tables
| Task | (%) | Net (pp) [95% CI] | (%) | (%) | Placebo-corr. rescue / breakage | ||
| bbh (beat_block_hammer) | 972 | 54.0 | 13.5 [9.4, 17.7] | 63.5 / 43.2 | 29.1 / 36.8 | 20.4 [14.1, 26.2] / 7.6 [ 12.9, 2.5] | |
| qr (rotate_qrcode) | 972 | 60.8 | 0.6 [ 2.9, 4.2] | 0.77 | 40.7 / 43.3 | 25.2 / 27.9 | 2.6 [ 8.8, 3.4] / 2.7 [ 7.2, 1.7] |
| a2b (place_a2b_left) | 972 | 38.3 | 2.3 [ 1.0, 5.6] | 0.18 | 22.5 / 20.8 | 30.4 / 33.6 | 1.7 [ 2.1, 5.6] / 3.2 [ 8.9, 2.2] |
| a2br (place_a2b_right) | 972 | 34.0 | 0.8 [ 4.3, 2.5] | 0.68 | 21.5 / 21.5 | 44.2 / 41.8 | 0.0 [ 4.2, 3.9] / 2.4 [ 3.7, 8.3] |
| pooled | 3,888 | 46.8 | 3.9 [2.1, 5.7] | 34.4 / 30.0 | 30.9 / 34.2 | — | |
| pooled without bbh | 2,916 | 44.3 | 0.7 [ 1.4, 2.7] | 0.51 | 26.4 / 26.4 | 31.6 / 33.1 | — |
| Ablation | bbh | qr | a2b | pooled | Verdict |
|---|---|---|---|---|---|
| Admission: all 175 vs admitted 87 | 1.1 [ 6.0, 3.9] | 0.5 [ 3.9, 4.8] | 2.5 [ 6.6, 1.5] | 1.0 [ 3.6, 1.5] | equivalent |
| Admission: random 87 (draw a) vs admitted 87 | 4.2 [ 9.3, 0.9] | 0.6 [ 4.9, 3.7] | 2.8 [ 6.8, 1.2] | 2.5 [ 5.1, 0.1] | equivalent |
| Admission: random 87 (draw b) vs admitted 87 | 0.3 [ 4.3, 4.9] | 0.6 [ 5.1, 3.9] | 5.1 [ 9.0, 1.2] | 1.8 [ 4.3, 0.7] | equivalent |
| Trigger: timer vs monitor | 2.0 [ 6.8, 2.9] | 0.3 [ 3.9, 4.5] | 4.8 [ 8.5, 1.2] | 2.2 [ 4.6, 0.3] | equivalent |
| Teacher: rung 1 only (60 rescues) | 4.3 [ 9.6, 0.9] | 2.8 [ 7.1, 1.5] | 0.0 [ 4.0, 4.0] | 2.4 [ 5.0, 0.3] | equivalent |
| Hand-back depth: 20 steps vs 40 | 5.4 [ 10.6, 0.2] | 4.0 [ 0.2, 8.2] | 3.1 [ 7.1, 0.8] | 1.5 [ 4.1, 1.1] | equivalent |
Robot Failure Recovery
Robot failure recovery studies how robots detect, diagnose, and respond to errors, damage, and unexpected physical interactions.
See all 73 papers