AnchorVLA4D: an Anchor-Based Spatial-Temporal Vision-Language-Action Model for Robotic Manipulation

TL;DR

AnchorVLA4D enhances robotic manipulation by integrating visual anchors and a spatial encoder to improve spatial-temporal reasoning, leading to better object handling and higher success rates without extra sensors.

Contribution

The paper introduces AnchorVLA4D, a novel spatial-temporal VLA model that uses visual anchors and a lightweight encoder to improve spatial awareness in robotic manipulation tasks.

Findings

Achieved 13.6% improvement on the Simpler WidowX benchmark.

Attained an average success rate of 80% on real-world tasks.

Requires no additional sensing modalities, maintaining low inference overhead.

Abstract

Since current Vision-Language-Action (VLA) systems suffer from limited spatial perception and the absence of memory throughout manipulation, we investigate visual anchors as a means to enhance spatial and temporal reasoning within VLA policies for robotic manipulation. Conventional VLAs generate actions by conditioning on a single current frame together with a language instruction. However, since the frame is encoded as a 2D image, it does not contain detailed spatial information, and the VLA similarly lacks any means to incorporate past context. As a result, it frequently forgets objects under occlusion and becomes spatially disoriented during the manipulation process. Thus, we propose AnchorVLA4D, a simple spatial-temporal VLA that augments the visual input with an anchor image to preserve the initial scene context throughout execution, and adds a lightweight spatial encoder that jointly processes the anchor and current frames to expose geometric relationships within an episode. Built on a Qwen2.5-VL backbone with a diffusion-based action head, AnchorVLA4D requires no additional sensing modalities (e.g., depth or point clouds) and introduces negligible inference overhead. Combining anchoring with a frozen pretrained spatial encoder yields further gains, realizing a 13.6% improvement on the Simpler WidowX benchmark and confirming the approach on real-world tasks, where it achieved an average success rate of 80%.