Recti-Q: Feature-Space Rectification for OOD-Robust Quantized Perception in Edge Robotics
Published in IROS 2026, 2026
Identifies and quantifies the OOD robustness gap introduced by 4-bit PTQ in large vision backbones on resource-constrained robotic platforms, and proposes a frozen-backbone LoRA adapter trained on source data only that preserves PTQ memory savings while closing most of the robustness gap.
Recommended citation: H. Yaghoubi*, P. Pilevar*, M. Lin (*equal contribution). "Recti-Q: Feature-Space Rectification for OOD-Robust Quantized Perception in Edge Robotics." IROS 2026.