Memory is a critical component in making agent systems usable and reliable. But what should an embodied agent actually remember? Our new research found that embodied agents prefer images.
We built MeMoREval to test this: 53 memory-dependent tasks across 4 simulated environments.
A robot constantly receives multimodal information—textual states, camera images, 3D maps—but what is the “best” modality to save in memory? Is text enough?
Introducing MeMoREval, my internship project at @DexmateAI, where we evaluate memory modality for embodied agents.
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