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handle: 2117/129368 , 10261/179442
In recent years the topic of combining motion and symbolic planning to perform complex tasks in the field of robotics has received a lot of attention. The underlying idea is to have access at once to the reasoning capabilities of a task planner and to the ability of the motion planner to verify that the plan is feasible from a physical and geometrical point of view. The present work describes a framework to perform manipulation tasks that require the use of two robotic manipulators. To do so we employ a Hierarchical Task Network (HTN) planner interleaved with geometric constraint verification. In this framework we also consider observation actions and handle noisy perceptions from a probabilistic perspective. These ideas are put into practice by means of an experimental set-up in which two Barrett WAM robots have to cooperatively solve a geometric puzzle. Our findings provide further evidence that considering explicitly physical constraints during task planning, rather than deferring their validation to the moment of execution, is advantageous in terms of execution time and breadth of situations that can be handled.
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Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial, Uncertainty, Shape, Cameras, Manipulators, Planning, Task analysis, :Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC], :Cybernetics::Artificial intelligence [Classificació INSPEC], Classificació INSPEC::Cybernetics::Artificial intelligence, Task analysis, Planning, Uncertainty, Shape, Manipulators, Cameras, planning (artificial intelligence)
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial, Uncertainty, Shape, Cameras, Manipulators, Planning, Task analysis, :Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC], :Cybernetics::Artificial intelligence [Classificació INSPEC], Classificació INSPEC::Cybernetics::Artificial intelligence, Task analysis, Planning, Uncertainty, Shape, Manipulators, Cameras, planning (artificial intelligence)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 7 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |