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Planning contact motion is important for many robotic tasks but dicult in general due to high variability and geometrical complexity of contact states. It is desirable to decompose the problem into simpler subproblems. A promising decomposition treats the problem as consisting of (1) automatic generation of a discrete contact state graph, and (2) planning(More)
Random sampling strategies play critical roles in ran-domized motion planners, which are promising and practical for motion planning problems with many degrees of freedom (dofs). In this paper, we explore random sampling in a constrained connguration space { the contact connguration space between two polyhedra, motivated by the need for generating contact(More)
A divide-and-merge approach is introduced for automatic generation of high-level, discrete contact state space, represented as contact state graphs, between two contacting polyhedral solids from their geometric models. Based on the fact that a contact state graph is the union of the subgraphs called a goal-contact relaxation (GCR) graph, the approach(More)
Many robotic tasks require compliant motions, but planning such motions poses special challenges not present in collision-free motion planning. One challenge is how to achieve exactness, that is, how to make sure that a planned path is exactly compliant to a desired contact state, especially when the configuration manifold of such a contact state is hard to(More)
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