Retrieval-triggered Action Projection Adjustment for Frozen VLA Policies: An Empirical Study and Failure Analysis
Organizations: Department of Computer Science and Technology, Hainan Bielefeld University of Applied Sciences, Danzhou, China
Abstract
Frozen Vision-Language-Action (VLA) policies cannot incorporate previously encountered failures without updating the underlying model. We present retrieval-triggered projection correction (RTPC), an auditable external-memory wrapper for frozen OpenVLA policies that retrieves stress-collected local-depth records and applies translation-only projection correction while keeping rotation and gripper commands unchanged. RTPC is evaluated on CALVIN single-task manipulation using primary and independent replication cohorts. In the primary evaluation, RTPC reduced a depth-based near-collision or stall proxy event rate without changing task-success count. However, this reduction was not reproduced in the independent replication, and retrieved correction directions were not distinguishable from random same-family directions. Analysis identifies endpoint coupling, batch sensitivity, and the lack of a trigger-matched memory-free baseline as limitations for attributing effects to retrieval-based correction. These findings do not establish physical safety improvement or memory-specific benefits, but provide an empirical evaluation and failure analysis of retrieval-conditioned intervention for frozen VLA policies.
Figures & tables
| Condition | Success | Events | Steps | Mean rate | Pooled rate | Trigger (%) |
|---|---|---|---|---|---|---|
| Base | 25 (22.3%) | 10 | 17,658 | 0.0496 | 0.0566 | 0.00 |
| Slow | 19 (17.0%) | 11 | 18,163 | 0.0546 | 0.0606 | 34.46 |
| Random | 22 (19.6%) | 6 | 17,722 | 0.0298 | 0.0339 | 7.56 |
| RTPC | 25 (22.3%) | 2 | 17,270 | 0.0099 | 0.0116 | 6.38 |
| Outcome | Batch | Difference | Lower | Upper | |
|---|---|---|---|---|---|
| Primary | 112 | –0.0397 | –0.0694 | –0.0099 | |
| Proxy | Replication | 40 | 0.0000 | ||
| Combined | 152 | ||||
| Primary | 112 | ||||
| Success | Replication | 40 | |||
| Combined | 152 |
Vision-Language-Action Models
Vision-language-action models turn visual observations and natural-language instructions into actions for robots and other embodied agents.
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