Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Robust Automatic C-Arm Calibration for Fluoroscopy-Based Navigation: A Practical Approach
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Intelligent execution monitoring in dynamic environments
Fundamenta Informaticae
On-line robot adaptation to environmental change
On-line robot adaptation to environmental change
Color learning and illumination invariance on mobile robots: A survey
Robotics and Autonomous Systems
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We present a general-purpose framework for updating a robot’s observation model within the context of planning and execution. Traditional plan execution relies on monitoring plan step transitions through accurate state observations obtained from sensory data. In order to gather meaningful state data from sensors, tedious and time-consuming calibration methods are often required. To address this problem we introduce Reverse Monitoring, a process of learning an observation model through the use of plans composed of scripted actions. The automatically acquired observation models allow the robot to adapt to changes in the environment and robustly execute arbitrary plans. We have fully implemented the method in our AIBO robots, and our empirical results demonstrate its effectiveness.