ScanBot: Towards Intelligent Surface Scanning in Embodied Robotic
  Systems
Authors: name: Zhiling Chen, name: Yang Zhang, name: Fardin Jalil Piran, name: Qianyu Zhou, name: Jiong Tang, name: Farhad Imani

We introduce ScanBot, a novel dataset designed for instruction-conditioned,
high-precision surface scanning in robotic systems. In contrast to existing
robot learning datasets that focus on coarse tasks such as grasping,
navigation, or dialogue, ScanBot targets the high-precision demands of
industrial laser scanning, where sub-millimeter path continuity and parameter
stability are critical. The dataset covers laser scanning trajectories executed
by a robot across 12 diverse objects and 6 task types, including full-surface
scans, geometry-focused regions, spatially referenced parts, functionally
relevant structures, defect inspection, and comparative analysis. Each scan is
guided by natural language instructions and paired with synchronized RGB,
depth, and laser profiles, as well as robot pose and joint states. Despite
recent progress, existing vision-language action (VLA) models still fail to
generate stable scanning trajectories under fine-grained instructions and
real-world precision demands. To investigate this limitation, we benchmark a
range of multimodal large language models (MLLMs) across the full
perception-planning-execution loop, revealing persistent challenges in
instruction-following under realistic constraints.