VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon
- Published
- Source
- arXiv
- Paper number
- 561
- Field
- Robotics
- arXiv ID
- 2607.01804
Key points
- The core problem is that the longer H is, the lower the policy call cost, but the more open-loop blind spots accumulate and cause errors.
- A 40M LVM detects visual dynamics deviations in latent space in real time, so no backbone retraining is needed and the method is plug-and-play.
- The adaptive action horizon keeps long chunks in confident regions and cuts them immediately, then reruns OGG when deviation is detected.
- With a horizon of 50 on pi0.5, success rate rises from 48.7 percent to 58.7 percent and average calls fall from 5.15 to 4.98, which improves success per call by 24.6 percent.
- In real-world disturbance recovery, performance improves from 40.0 percent to 68.3 percent, and precision alignment tasks improve by 16.6 percent.
- Cuts happen mostly in critical phases, 83.7 percent of them during grasping and alignment, which shows that the monitor correctly detects the meaningful moments.
Paper links
External research summaries. These are not HDATF publications or measured product results.