Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models
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 ...
| Authors | Houze Xu, Jizhong Li, Ziyi Ye |
|---|---|
| Primary category | cs.RO |
| Published | 5 August 2026 |
| Last updated at source | 17 September 2026 |
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