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Robotics is the science that attempts to forge an intelligent,
computational connection between perception and action. Perhaps the
most fundamental problems in robotics today are uncertainty and
error in control, sensing, and modelling. In this monograph the
author provides what is perhaps the first systematic treatment of
the uncertainty problem. This book descibes the theory he developed
for planning compliant motions for tasks such as robotic assembly.
The planner can synthesize robot control programs that are robust
in the face of uncertainty in the control system, the robot
sensors, and variation in the geometry of the assembly. Perhaps the
deepest contribution lies in a new theory of Error Detection and
Recovery (EDR). While EDR is largely motivated by the problem of
uncertainty its applicability may be quite broad. EDR has been a
persistent but ill-defined theme in AI and robotics research. The
author gives a constructive, geometric definition for EDR
strategies, and shows how they may be computed. This theory
represents an elegant mathematical attack on the problem of error
detection and recovery based on geometric and physical reasoning.
Finally, algorithms for the automatic synthesis of EDR strategies
are described, and new results on their computational complexity
are analyzed.
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