Dual Latent Memory in Vision-Language-Action Models for Robotic Manipulation
Mainstream Vision-Language-Action (VLA) models predict actions primarily from the current observation under a Markovian assumption, thus struggling with long-horizon, temporally dependent tasks. Existing memory-augmented VLAs either expand the observation window or retrieve history from the memory bank as auxiliary policy-side context. However, they leave memory outside the native latent embedding space of VLA reasoning, preventing historical experience from being fluidly interleaved with multimodal reasoning and action formation. To this end, we introduce LaMem-VLA, a latent-memory-native framework that reconstructs historical experience into latent memory tokens and directly interweaves...
| Authors | Hongyu Qu, Jianzhe Gao, Xiaobin Hu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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