AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning

• URL: http://arxiv.org/abs/2607.03182v1
• Date: Fri, 03 Jul 2026 10:38:30 GMT
• Title: AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning
• Abstract summary: AnchorVLA is a hierarchical decision-anchored VLA planning framework.<n>It uses trajectory-pattern anchors as an explicit interface between high-level VLA reasoning and continuous trajectory execution.<n>It achieves a state-of-the-art Success Rate of 77.28 and a competitive Driving Score of 89.92.
• Score: 13.949868529278504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories. Vision-Language-Action models have been introduced into driving planning to improve long-tail generalization, commonsense reasoning, high-level semantic understanding, and explainability. However, existing VLA planners mainly follow planning-head-based trajectory prediction or full-trajectory autoregressive generation. The former only weakly constrains continuous trajectory generation with VLA reasoning, while the latter relies on long sequences of low-information-density coordinate tokens, making semantic-action alignment difficult and leading to discretization errors and inefficient inference. To address these limitations, we propose AnchorVLA, a hierarchical decision-anchored VLA planning framework that uses trajectory-pattern anchors as an explicit interface between high-level VLA reasoning and continuous trajectory execution. Specifically, Decision-as-Anchor Representation represents behavior-level driving decisions with anchor tokens, each encoding an entire local motion pattern rather than a single coordinate point. Decision-Anchored Residual Flow then generates fine-grained continuous trajectories in the selected anchor-defined residual space, capturing multi-modal execution refinements after high-level decision making. By reasoning over compact and semantically meaningful anchors instead of autoregressively generating waypoint sequences, AnchorVLA preserves LLM-based decision making while improving inference efficiency, semantic-action alignment, and continuous generation flexibility. Experiments on the Bench2Drive closed-loop benchmark show that AnchorVLA achieves a state-of-the-art Success Rate of 77.28 and a competitive Driving Score of 89.92.

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