Robot task teaching has during the past years received significant
attention and it has been recognized
that more natural teaching methods are necessary so to allow ordinary
users to teach robots new tasks by simply demonstrating them. From
the viewpoint of task learning in humans it is known that such a
strategy where a teacher's demonstration is used as a starting point
of learning significantly speeds up the process and reduces the amount
of trial-and-error steps. In robotics, such an approach to learning
has been considered in frameworks of Learning by Imitation or
Programming by Demonstration (PbD).
An important issue to consider is that the initial task setting will
change between the demonstration and execution time. A robot that has
to set-up a dinner table may have to plan the order of handling
plates, cutlery and glasses in a different way that previously
demonstrated by a human teacher. Hence, it is not sufficient to just
replicate the human movements but the robot i)~must have the ability
to recognize what parts of the whole task can be segmented and
considered as subtasks so to ii)~perform online planning for task
execution given the current state of the environment. The important
problem here is how to instruct or teach the robot the essential order
of the subtasks for which the execution order may or may not be
crucial. As an example, the main dish plate should always be under the
appetizer or a soup plate and the order in which these are placed on
the table is important. One way of addressing this problem is to
demonstrate a task to the robot multiple times and let the robot learn
which order of the subtasks is essential.
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Task Learning Using Graphical Programming and Human Demonstrations
(S. Ekvall, D. Aarno and D. Kragic)
In RO-MAN 06: The 15th IEEE International Symposium on Robot and Human Interactive Communication
University of Hertfordshire, UK
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Learning Task Models from Multiple Human Demonstrations
(S. Ekvall and D. Kragic )
In RO-MAN 06: The 15th IEEE International Symposium on Robot and Human Interactive Communication
University of Hertfordshire, UK
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Integrating Object and Grasp Recognition for Dynamic Scene Interpretation
(S. Ekvall and D. Kragic)
In IEEE International Conference on Advanced Robotics,
2005. ICAR'05, Seattle, USA
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Grasp Recognition for Programming by Demonstration Tasks
(S. Ekvall and D. Kragic)
In IEEE International Conference on Robotics and Automation,
2005. ICRA'05, Barcelona, Spain
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Interactive Grasp Learning Based on Human Demonstration
(S. Ekvall and D. Kragic)
In IEEE
International Conference on Robotics and Automation , 2004. ICRA'04,
New Orleans, USA
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