WeaveLA: Event Driven Cross-Subtask Latent Memory Weaving for Repetitive Robot Manipulation

arXiv:2606.17463v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) policies have achieved remarkable single-step manipulation, yet they remain brittle precisely where each stage depends on what was just completed. The core issue is structural: short-window VLAs lack an explicit channel for rouxting information across sub-task boundaries, and existing memory-augmented variants either write at every frame, retrieve from demonstration-time stages, or fire at sub-goal events without performing an explicit sub-task-to-sub-task hand-off into the action expert. We identify the sub-goal completion event as the natural temporal unit for cross-subtask memory hand-off, and present WeaveLA (Weave Latent memory for Vision-Language-Action policies), a cross-subtask memory interface that, on top of a frozen VLA backbone, compresses each completed segment into latent tokens via query-driven attention pooling and routes them directly into the action-generation path of the next sub-task. This event-triggered, action-side design preserves the base policy's short-window interface while adding a lightweight cross-subtask channel. Through stratified evaluation on RoboMME with a pi_{0.5} backbone, WeaveLA's gains land exactly where the channel is needed: on the hardest repetition slice (SwingXtimes, N{=}3), success rises from 0% to 47.8%, while single-execution episodes remain unchanged. Per-episode paired analysis confirms the gains are confined to tasks whose causal structure requires cross-subtask information.

Related

• Dual Latent Memory in Vision-Language-Action Models for Robotic Manipulation
• HAMLET: Switch your Vision-Language-Action Model into a History-Aware Policy
• MemoryVAM: Integrating Memory into Video Action Model for Robot Manipulation
• UniMem: Unifying Multimodal Memory and Control for Vision-Language-Action Models

Source: arXiv cs.CV | 2026-09-09