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Memo enables efficient memory formation and usage for long-horizon embodied RL tasks, improving generalization and efficiency.

We present a single neural network architecture composed of task-agnostic components (ViTs, convolutions, and LSTMs) that achieves state-of-art results on both the ImageNav and ObjectNav without task-specific modules.