3 accepted papers
Many indoor workspaces are quasi-static: their global geometric layout is stable, but local semantics change continually, producing repetitive geometry, dynamic clutter, and perceptual noise that defeat standard vision-based localization. We present ShelfAware, a semantic particle filter for robust
While abstract knowledge like cause-and-effect relations enables robots to problem-solve in new environments, acquiring such knowledge remains out of reach for many traditional machine learning techniques. In this work, we introduce a method for a robot to learn an explicit model of cause-and-effect…
Research in human teamwork shows that a key element of fluid and fluent interactions is the interpretation of implicit verbal and non-verbal cues in context. This poses an issue to robotic platforms, however, as they have historically worked best when controlled through explicit commands that have e…