StateMem: Single-State Residual Memory with Adaptive Inference for Vision-Language-Action Policies
Memory-dependent robotic manipulation often requires later actions to use information from earlier interactions. Existing vision-language-action (VLA) policies primarily rely on current observations, limiting historical information retention. Memory-augmented VLAs, such as MemoryVLA, address this limitation with external memory banks but require explicit storage and retrieval. To address these limitations, we propose StateMem, a single-state residual memory framework for VLA policies that uses prediction error to update a persistent memory token through low-rank residuals and to adaptively route cached prefixes. A training-free controller adjusts the routing threshold online, while fast c...
| Authors | Wenzhuo Li, Qiongfeng Shi, Yi Zhou |
|---|---|
| Primary category | cs.RO |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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