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Then, we show how to extract the key points used as partial goals in the optimization-based motion planner to achieve the assembly task.
These points are used as partial goals in the optimization-based motion planner that generates the robot arms' trajectories which minimize the object's deformation.
These points will be used as partial goals (which we call key points) to the optimization-based motion planner developed in our previous work [4].
From these trajectories, key positions (points) are extracted (as many points as needed to guarantee that the assembly task is successfully accomplished) and used as partial goals for the motion planner that minimizes the ring-shaped object's deformation.
In this paper, we discuss a novel strategy for generating the key positions used as partial goals in the motion planning algorithm that generates a collision-free trajectory for the robot and minimizes the deformation of the object.
In the previous section we showed that carefully extracting points from the hand trajectory recorded by a human, and using this points as partial goals for our optimization-based motion planner, a dual-arm robot was able to achieve the assembly of ring-shaped objects.
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Our planner first computes a key position (or middle point) based on the object's position, and then this key position is used as a partial goal in an optimization-based motion planner which computes a collision-free trajectory that at the same time minimizes the ring-shaped object's deformation through an elastic energy based objective function.
Until 1999, GAU were progressively established in all general hospitals with a partial goal of alleviating the congestion of the emergency room by elderly patients.
Dörner and Wearing (1995) emphasize that the process of action regulation within problem solving can be subdivided into the following partial activities: goal elaboration and definition; information seeking and recording; forecasting, and planning; decision-making; performing goal-oriented operations; and monitoring and reflection of outcomes.
The two dimensions define the executive's cognitive biases in: (1) selection of goals (partial vs. total), and (2) orientation toward action (form vs. substance).
This means that neither negative goals, partial initial states nor multi-valued state variables increase the expressiveness of "standard" propositional STRIPS.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com