MIT proposes robot memory error-correction system UQ-DAAAM
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The MIT research team has proposed the UQ-DAAAM memory system, which enables robots to quantify their uncertainty about what they see by comparing multiple descriptions of the same object from different viewpoints, and to select additional viewpoints to correct erroneous annotations. MIT News states that the system can resolve ambiguity for about 92% of objects in a robot's environment, requiring only two additional images per object, and improves object description accuracy by 5% over the state of the art; it can update uncertain memory entries in real time on a mobile robot in under one second.
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- MIT News AIClearing up a robot’s fuzzy memory
MIT researchers developed UQ-DAAAM, a memory system that quantifies a robot's uncertainty about object descriptions by comparing multiple viewpoints and fusing them into more accurate captions, resolving ambiguities for about 92 percent of objects with only two additional captions each. The system improved object description accuracy by 5 percent over the state of the art and produced more accurate answers about objects, their locations, and when they were seen. The work, led by senior author Luca Carlone with co-lead authors Harry Zhang and Nicolas Gorlo, will be presented at the Conference on Neural Information Processing Systems.
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