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Interactive robotic grasping using natural language is one of the mostfundamental tasks in human-robot interaction. However, language can be a sourceof ambiguity, particularly when there are ambiguous visual or linguisticcontents. This paper investigates the use of object attributes indisambiguation and develops an interactive grasping system capable ofeffectively resolving ambiguities via dialogues. Our approach first predictstarget scores and attribute scores through vision-and-language grounding. Tohandle ambiguous objects and commands, we propose an attribute-guidedformulation of the partially observable Markov decision process (Attr-POMDP)for disambiguation. The Attr-POMDP utilizes target and attribute scores as theobservation model to calculate the expected return of an attribute-based (e.g.,"what is the color of the target, red or green?") or a pointing-based (e.g.,"do you mean this one?") question. Our disambiguation module runs in real timeon a real robot, and the interactive grasping system achieves a 91.43\%selection accuracy in the real-robot experiments, outperforming severalbaselines by large margins.

Interactive Robotic Grasping with Attribute-Guided Disambiguation