Substitute selection for a missing tool using robot-centric conceptual knowledge of objects

When performing task in open-ended environment, a robot may not have access to conventional tools required in the task. In such case, an ideal response for the robot would be to find a suitable substitute. In this paper, we present an approach to tool substitution where robot-centric knowledge is generated in an unsupervised manner about objects from multi-modal sensory data captured by a robot. This robot-centric knowledge is subsequently exploited to identify a substitute from available objects observed in the environment.

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