Recognizing Intentions

Robot systems have been used extensively during the
last decades to provide automation solutions in a
number of areas. Most of the currently deployed
automation systems are limited in that the tasks they
can solve are required to be repetitive and
predicable. Therefore the robotics and artificial
intelligence research communities have made
significant research efforts to produce more
intelligent machines. Although significant progress
has been made towards achieving robots that can
interact in a human environment there are currently
no system that comes close to achieving the reasoning
capabilities of humans.
In order to reduce the complexity of the problem some
researchers have proposed an alternative to creating
fully autonomous robots capable of operating in human
environments. The proposed alternative is to allow
fusion of human and machine capabilities.
Segmentation and recognition of operator generated
motions can be used to provide appropriate assistance
during task execution in teleoperative and
human-machine collaborative settings.
This book describes research towards enabling
improved human-machine collaboration using intention
recognition.

Autorentext
Daniel Aarno completed his Master's degree in Electrical Engineering at the Royal Institute of Technology in 2004. He then continued as a researcher at the Centre for Autonomous Systems and Computer Vision and Active Perception Lab, where he completed his Licentiate of Engineering in Computer Science in 2007.

Klappentext
Robot systems have been used extensively during the last decades to provide automation solutions in a number of areas. Most of the currently deployed automation systems are limited in that the tasks they can solve are required to be repetitive and predicable. Therefore the robotics and artificial intelligence research communities have made significant research efforts to produce more intelligent machines. Although significant progress has been made towards achieving robots that can interact in a human environment there are currently no system that comes close to achieving the reasoning capabilities of humans. In order to reduce the complexity of the problem some researchers have proposed an alternative to creating fully autonomous robots capable of operating in human environments. The proposed alternative is to allow fusion of human and machine capabilities. Segmentation and recognition of operator generated motions can be used to provide appropriate assistance during task execution in teleoperative and human-machine collaborative settings. This book describes research towards enabling improved human-machine collaboration using intention recognition.