On Specifying and Performing Visual Tasks with Qualitative Object Models

Vision-based control has aimed to develop general-purpose, high accuracy systems for manipulating objects. While much of the scientific and technological infrastructure needed to accomplish this aim is now in place, several stumbling blocks still remain. One continuing issue is accuracy, and its relationship to system calibration. We describe a generative task structure for vision-based control of motion that admits a simple, geometric approach to task specification. At the same time, this approach allows one to state precisely what types of miscalibration lead to errors in task performance. A second hurdle has been the programmability of hand-eye systems. However, we argue that a structured object representation sufficient for flexible hand-eye coordination is a possibility. The result is a high-level, object-centered language for expressing hand-eye tasks.

Authors

• Gregory D. Hager
• Zachary Dodds

Keywords

• Calibration
• Robot vision systems
• Control systems
• Computer science
• Robot kinematics
• Libraries
• Artificial intelligence
• Focusing
• Feedback control
• Educational institutions
• Visual Task
• Specific Tasks
• Level Of Knowledge
• Visual Features
• Object Recognition
• Objective Information
• Camera System
• Visual Search
• Set Of Operations
• Error Signal
• Geometric Information
• Camera Calibration
• Camera Model
• Object Appearance
• Object Geometry
• Calibration Uncertainty
• Appearance Information
• Vision-based System
• Object In Question
• Camera Projection
• Robot Workspace
• Calibration Information