AimBot: A Simple Auxiliary Visual Cue to Enhance Spatial Awareness of
  Visuomotor Policies
In this paper, we propose AimBot, a lightweight visual augmentation technique
that provides explicit spatial cues to improve visuomotor policy learning in
robotic manipulation. AimBot overlays shooting lines and scope reticles onto
multi-view RGB images, offering auxiliary visual guidance that encodes the
end-effector's state. The overlays are computed from depth images, camera
extrinsics, and the current end-effector pose, explicitly conveying spatial
relationships between the gripper and objects in the scene. AimBot incurs
minimal computational overhead (less than 1 ms) and requires no changes to
model architectures, as it simply replaces original RGB images with augmented
counterparts. Despite its simplicity, our results show that AimBot consistently
improves the performance of various visuomotor policies in both simulation and
real-world settings, highlighting the benefits of spatially grounded visual
feedback.
689d2f69b083e610d741e980
Yinpei Dai
claimed_verified
2025-08-14T13:37:52.850Z
648b889575d21f74acf9cbb1
/avatars/0ffee0809f9e2670821721c005d8dde4.svg
Dai
user
Yinpei
689d2f69b083e610d741e981
Jayjun Lee
689d2f69b083e610d741e982
Yichi Zhang
689d2f69b083e610d741e983
Ziqiao Ma
689d2f69b083e610d741e984
Jed Yang
689d2f69b083e610d741e985
Amir Zadeh
689d2f69b083e610d741e986
Chuan Li
689d2f69b083e610d741e987
Nima Fazeli
689d2f69b083e610d741e988
Joyce Chai
AimBot: A Simple Auxiliary Visual Cue to Enhance Spatial Awareness of
  Visuomotor Policies
In this paper, we propose AimBot, a lightweight visual augmentation technique
that provides explicit spatial cues to improve visuomotor policy learning in
robotic manipulation. AimBot overlays shooting lines and scope reticles onto
multi-view RGB images, offering auxiliary visual guidance that encodes the
end-effector's state. The overlays are computed from depth images, camera
extrinsics, and the current end-effector pose, explicitly conveying spatial
relationships between the gripper and objects in the scene. AimBot incurs
minimal computational overhead (less than 1 ms) and requires no changes to
model architectures, as it simply replaces original RGB images with augmented
counterparts. Despite its simplicity, our results show that AimBot consistently
improves the performance of various visuomotor policies in both simulation and
real-world settings, highlighting the benefits of spatially grounded visual
feedback.
AimBot, a lightweight visual augmentation technique, improves visuomotor policy learning in robotic manipulation by overlaying spatial cues onto RGB images, enhancing performance in both simulation and real-world settings.
visuomotor policy learning robotic manipulation visual augmentation shooting lines scope reticles depth images camera extrinsics end-effector pose spatial relationships gripper augmented images