Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models

• URL: http://arxiv.org/abs/2608.04765v1
• Date: Wed, 05 Aug 2026 12:32:15 GMT
• Title: Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models
• Abstract summary: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.<n>We propose a hierarchical long-horizon VLA architecture with an explicit language-memory module.<n>We evaluate the proposed method in multiple simulation environments and conduct sim-to-real experiments on a real robotic platform.
• Score: 4.644589778994777
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control. However, existing VLA models still face major challenges in long-horizon tasks: sparse expert demonstrations constrain cross-task compositional generalization; the non-Markovian nature of long-horizon tasks makes it difficult for policies conditioned only on current observations to maintain temporal consistency; limited closed-loop error correction allows execution errors to accumulate; and end-to-end action fine-tuning may weaken the high-level semantic representations of vision-language model (VLM) backbones. To address these issues, we propose a hierarchical long-horizon VLA architecture with an explicit language-memory module. The central idea is to convert discrete temporal observations into a coherent textual memory sequence with temporal logic. The system is decoupled into a high-level VLM and a low-level VLA: the high-level VLM performs semantic reasoning through a visual question answering training paradigm, while the low-level VLA executes precise continuous control conditioned on subtask instructions and visual observations. The high-level VLM recursively updates both language memory and subtask instructions using the previous memory as a contextual anchor, enabling persistent temporal tracking and dynamic correction during long-horizon execution. We evaluate the proposed method in multiple simulation environments and conduct sim-to-real experiments on a real robotic platform. The results demonstrate that explicit language memory improves the success rate and robustness of VLA models on complex long-horizon tasks while providing an interpretable semantic account of the decision process.

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