Conflict Detection and Resolution in Table Top Scenarios for Human-Robot Interaction
This work tackles interaction failures for robots in table-top scenarios, but appears incremental as it outlines a framework without demonstrated improvements.
The paper addresses conflict detection and resolution in human-robot interaction, focusing on failures like object misidentification and perceptual ambiguity from the robot's perspective, but provides no concrete results or numbers.
As in any interaction process, misunderstandings, ambiguity, and failures to correctly understand the interaction partner are bound to happen in human-robot interaction. We term these failures 'conflicts' and are interested in both conflict detection and conflict resolution. In that, we focus on the robot's perspective. For the robot, conflicts may occur because of errors in its perceptual processes or because of ambiguity stemming from human input. This poster presents a brief system overview, and details Here, we briefly outline the project's motivation and setting, introduce the general processing framework, and then present two kinds of conflicts in some more detail: 1) a failure to identify a relevant object at all; 2) ambiguity emerging from multiple matches in scene perception.