ScanBot: Towards Intelligent Surface Scanning in Embodied Robotic
  Systems
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.
6833ed0f924393051ae89d09
Zhiling Chen
claimed_verified
2025-05-26T08:09:23.482Z
661b42ee2b14565c7af4ff73
/avatars/a58ced9fd5f3e86ef5ebb43ce1a7af29.svg
zhiling chen
user
ed1son
6833ed0f924393051ae89d0a
Yang Zhang
6833ed0f924393051ae89d0b
Fardin Jalil Piran
6833ed0f924393051ae89d0c
Qianyu Zhou
6833ed0f924393051ae89d0d
Jiong Tang
6833ed0f924393051ae89d0e
Farhad Imani
extracted_pending
2025-05-26T17:59:31.038Z
6834ab8de2a34a2f37676c4b
/avatars/aa95bbf53a9c55e9148fdfb8d62e57e8.svg
Farhad Imani
user
FarhadImani1
ScanBot: Towards Intelligent Surface Scanning in Embodied Robotic
  Systems
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.
The ScanBot dataset, focusing on instruction-conditioned high-precision robotic surface scanning, showcases challenges for vision-language action models in achieving precise scanning trajectories under real-world constraints.
ScanBot instruction-conditioned high-precision surface scanning robotic systems laser scanning sub-millimeter path continuity parameter stability multimodal large language models vision-language action models perception-planning-execution loop