# AimBot: A Simple Auxiliary Visual Cue to Enhance Spatial Awareness of Visuomotor Policies

arXiv: https://arxiv.org/abs/2508.08113 · HTML: https://mlsanity.com/paper/2508.08113/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2025.

## Built on by (most cited first, 10 of 10)

- [GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization](https://mlsanity.com/paper/2605.12369/) (2026)
- [SutureBot: A Precision Framework & Benchmark For Autonomous End-to-End Suturing](https://mlsanity.com/paper/2510.20965/) (2025)
- [UAOR: Uncertainty-aware Observation Reinjection for Vision-Language-Action Models](https://mlsanity.com/paper/2602.18020/) (2026)
- [DC-WAM: Dynamic-Centric Visual Supervision and Reasoning for World-Action Models](https://mlsanity.com/paper/2607.25918/) (2026)
- [Decoupling Semantics and Geometric Grounding: Spatial Visual Prompts for Language-Conditioned Imitation Learning](https://mlsanity.com/paper/2606.25360/) (2026)
- [TAP-VLA: Tactile Annotation Prompting for Vision Language Action Models](https://mlsanity.com/paper/2606.29089/) (2026)
- [GeoProp: Grounding Robot State in Vision for Generalist Manipulation](https://mlsanity.com/paper/2607.07101/) (2026)
- [X-Imitator: Spatial-Aware Imitation Learning via Bidirectional Action-Pose Interaction](https://mlsanity.com/paper/2605.12162/) (2026)
- [AxisGuide: Grounding Robot Action Coordinate System in RGB Observations for Robust Visuomotor Manipulation](https://mlsanity.com/paper/2606.06761/) (2026)
- [Gaze Prompts: Temporally Dense Human Attention for Vision-Language-Action Fine-Tuning](https://mlsanity.com/paper/2609.34550/) (2026)

## Abstract (arXiv)

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.