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The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning

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arxiv 2405.03164 v1 pith:KN4SA4J3 submitted 2024-05-06 cs.RO cs.AIcs.CV

The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning

classification cs.RO cs.AIcs.CV
keywords uncertaintyrobotsanalyticalapplicationsareabecomesbeencritical
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Uncertainty has long been a critical area of study in robotics, particularly when robots are equipped with analytical models. As we move towards the widespread use of deep neural networks in robots, which have demonstrated remarkable performance in research settings, understanding the nuances of uncertainty becomes crucial for their real-world deployment. This guide offers an overview of the importance of uncertainty and provides methods to quantify and evaluate it from an applications perspective.

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Cited by 2 Pith papers

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