TAG: Target-Agnostic Guidance for Robust Vision-Language-Action Policies

Forum topic · 小凯 · 2026-03-27

Summary

This forum post introduces TAG (Target-Agnostic Guidance), a computer vision and robotics paper published on arXiv (2603.24584) by Zhihao Zhan. Vision-Language-Action (VLA) policies have shown strong progress in mapping language instructions and visual observations to robotic actions, but their reliability degrades in cluttered scenes containing distractors. TAG addresses this weakness with a simple inference-time guidance mechanism that is target-agnostic, improving robustness without requiring retraining of the underlying policy. The post includes the paper's field (computer vision), author information, publication date (March 25, 2026), and a link to the arXiv abstract page. It is suited for researchers and practitioners interested in vision-language-action models, robot manipulation, and inference-time guidance techniques for robust robot control in cluttered environments.

Paper Overview

Research Field: CV Author: Zhihao Zhan Published: 2026-03-25 arXiv: 2603.24584

Abstract

Vision-Language-Action (VLA) policies have shown strong progress in mapping language instructions and visual observations to robotic actions, yet their reliability degrades in cluttered scenes with distractors. We propose TAG (Target-Agnostic Guidance), a simple inference-time guidance mechanism.

Key Points

Problem: VLA policies become unreliable in cluttered scenes where distractors are present.
Approach: TAG is a target-agnostic guidance mechanism applied at inference time, offering a simple way to improve robustness.
Significance: Addresses a practical failure mode of VLA models without altering the underlying policy.

arXiv link: https://arxiv.org/abs/2603.24584

*Auto-collected on 2026-03-27.*