MEM: Multi-Scale Embodied Memory for Vision Language Action Models
Conventionally, memory in end-to-end robotic learning involves inputting a sequence of past observations into the learned policy. However, in complex multi-stage real-world tasks, the robot's memory must represent past events at multiple levels of granularity: from long-term memory that captures abstracted semantic concepts (e.g., a robot cooking dinner should remember which stages of the recipe are already done) to short-term memory that captures recent events and compensates for occlusions (e.g., a robot remembering the object it wants to pick up once its arm occludes it). In this work, our main insight is that an effective memory architecture for long-horizon robotic control should com...
| Authors | Marcel Torne, Karl Pertsch, Homer Walke et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 March 2026 |
| Last updated at source | 8 March 2026 |
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