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Active Visual Exploration (AVE) optimizes the utilization of roboticresources in real-world scenarios by sequentially selecting the mostinformative observations. However, modern methods require a high computationalbudget due to processing the same observations multiple times through theautoencoder transformers.  As a remedy, we introduce a novel approach to AVE called TOken REcycling(TORE). It divides the encoder into extractor and aggregator components. Theextractor processes each observation separately, enabling the reuse of tokenspassed to the aggregator. Moreover, to further reduce the computations, wedecrease the decoder to only one block.  Through extensive experiments, we demonstrate that TORE outperformsstate-of-the-art methods while reducing computational overhead by up to 90\%.

TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration