Inline Memory Meets Reusable Skills: Memory-centric Framework for Vision-Language-Action Model
Vision-Language-Action (VLA) models have shown strong promise for general-purpose robotic manipulation, yet adapting them to new tasks and domains remains inefficient: existing methods often rely on parameter tuning, incurring substantial costs and risking catastrophic forgetting of previously learned tasks. To address this, we propose \textbf{Optimus-R}, a memory-centric VLA framework that formulates robotic adaptation as explicit query-skill memory tuning. Optimus-R introduces: (i) An \textbf{Inline Memory Interface for skill extraction}. It inserts learnable memory tokens into the VLA prefix stream, allowing the backbone to derive control-aware query and skill representations within th...
| Authors | Zaijing Li, Rui Shao, Bing Hu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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