ConsisVLA-4D: Advancing Spatiotemporal Consistency in Efficient 3D-Perception and 4D-Reasoning for Robotic Manipulation

摘要

Current Vision-Language-Action (VLA) models primarily focus on mapping 2D observations to actions but exhibit notable limitations in spatiotemporal perception and reasoning: 1) spatial representations often rely on additional sensors, introducing substantial computational overhead; 2) visual reasoning is typically limited to future-frame prediction, lacking alignment with the instruction-grounded scene and thus compromising spatiotemporal consistency. To address these challenges, we propose **ConsisVLA-4D**, a unified and efficient framework that enhances spatiotemporal consistency in 3D-Perception and 4D-Reasoning. Specifically, we design: **1) CV-Aligner**, which ensures **C**ross-**V**iew object semantic consistency via filtering instruction-relevant regions and aligning object identities across multiple viewpoints; **2) CO-Fuser**, which guarantees **C**ross-**O**bject spatial geometric consistency by eliminating spatial relation ambiguities between objects across views using compact latent representations. Building upon these, we introduce **3) CS-Thinker** to achieve **C**ross-**S**cene spatiotemporal consistency as actions unfold. It learns implicit knowledge of local dynamics from object-semantic tokens of CV-Aligner and global depth from geometric tokens of CO-Fuser, thereby enhancing efficient visual reasoning under scene variations. Extensive experiments demonstrate that, benefiting from its efficient spatiotemporal consistency design, ConsisVLA-4D achieves **21.6%** and **41.5%** performance improvements, along with **2.3x** and **2.4x** inference speedups compared to OpenVLA on the LIBERO benchmark and real-world platforms, respectively.