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

2025-08-11

AI Summary

To address the limited spatial awareness of vision–motor policies in robotic manipulation, this paper proposes AimBot—a lightweight, model-agnostic visual enhancement method requiring no architectural modifications or additional training overhead. Its core innovation lies in real-time generation of ray-casted targeting lines and reticle-style spatial anchors on multi-view RGB images, derived from depth maps, camera intrinsics/extrinsics, and end-effector pose; these explicitly encode the 3D spatial relationship between the end-effector and target objects. The enhancement incurs negligible latency (<1 ms) while substantially improving agents’ understanding of spatial structure. Evaluated across simulation and real-world robotic platforms, AimBot consistently enhances accuracy and generalization of diverse vision–motor policies—including grasping, peg-in-hole insertion, and pushing/pulling—demonstrating the effectiveness and broad applicability of spatially anchored visual feedback.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Multi-modal Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Enhances spatial awareness in visuomotor policy learning
• Provides auxiliary visual cues for robotic manipulation tasks
• Improves performance without altering model architectures

Innovation

• Lightweight visual augmentation technique
• Overlays spatial cues on RGB images
• Minimal computational overhead, no architecture changes