EvoMem-VLA: State-Evolution Memory for Long-Horizon Robot Manipulation
Most vision-language-action (VLA) models rely on current observations and lose task-relevant evidence once it leaves view, limiting performance on long-horizon, memory-dependent tasks. Existing efforts incorporate compressed historical features or sparse visual keyframes. However, isolated snapshots can leave the policy uncertain about what changed during past interactions and which action should follow. To overcome this limitation, we propose EvoMem-VLA, which constructs state-evolution memory by explicitly encoding and retaining observed changes between historical states. These change representations preserve evidence of interaction outcomes, allowing the policy to track task progress b...
| Authors | Yuheng Na, Zhide Zhong, Junjie He et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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