Having a human in the control loop of a robot plays an important role in today’s robotics applications. Whether for teleoperation, interactive processing, or as a learning resource for automation, human-robot interaction is in need of well-designed interfaces to allow the human-in-the-loop to be as effective as possible for robotic applications with the least effort.
One general framework for human-in-the-loop interaction with robots is annotation, which refers to the inclusion of supplementary information to a dataset or a robot’s perceptual stream that, when properly interpreted, produces valuable semantic information that is difficult for algorithms to infer directly and is also available for repeated use in the future. We focus on annotating 3D vision, a popular and rich means for robot perception in which robots use depth sensors to perceive the environment for recognition, navigation and scene understanding.
Annotation of 3D-scanned environments has been shown to be successful in employing humans-in-the-loop to improve a robot’s extraction of meaningful structure from the visual stream. By relying on human cognition, these semi-autonomous systems may utilize hints - expressed through the annotated cues - as informed suggestions that reduce the complexity of a task and help focus the context of a given situation. These annotations may be used immediately as hints for operation or stored for later use and analysis.
In this work, we present a new scheme for constructing and storing annotation cues, called Point Cloud Scaffolds. Point Cloud Scaffolds are designed to allow fast and precise specification of object shape and manipulation constraints.
In addition, we present the Point Cloud Prototyper, a simple annotation tool designed for constructing Point Cloud Scaffolds and studying how best to design annotation capabilities for three classic tasks in robotics: object reconstruction, Pick-and-Place, and articulated-object manipulation.
We present evidence that this approach is precise and simple enough even for novice users to master quickly. The annotation paradigm is well suited for three critical task types and compares well to other similar techniques developed in the field of annotation for robotics. Point Cloud Scaffolds are versatile tools that show promise as a shared-control counterpart to continuous teleoperation, interactive scene analysis and navigation, and the construction of rich repositories of annotations for complex robotic tasks.
|Commitee:||Drumwright, Evan, Heller, Rachelle, Pless, Robert, Simha, Rahul|
|School:||The George Washington University|
|School Location:||United States -- District of Columbia|
|Source:||DAI-B 80/01(E), Dissertation Abstracts International|
|Keywords:||Annotation, Articulation, Grasping, Human-in-the-loop, Point clouds, Reconstruction|
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