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120                                     Application Examples in the Robotics Domain


                          simple augmentation of the embedding space X with extraneous compo-
                          nents (note, they do not affect the normal operation.) Those can be used
                          to formulate additional cost function terms and can be activated when-
                          ever desired. The cost function terms can be freely constructed in various
                          functional dependencies and are supplied during the learning phase of the
                          PSOM.

                             The best-match location s is under-constrained, since jIj                                  m
                          (in contrast to the cases described in Sec. 5.6.) Certainly, the standard best-
                          match search algorithm will find one possible solution — but it can be any
                          compatible solution and it will depend on the initial start condition s t   .
                             Here, the PSOM offers a versatile way of formulating extra goals or
                          constraints, which can be turned on and off, depending on the situation
                          and the user's desire. For example, of particular interest are:


                          Minimal joint movement: “fast” or “lazy” robot. One practical goal can
                                be: reaching the next target position with minimum motor action.
                                This translates into finding the shortest path from the current joint

                                configuration   curr  to a new   compatible with the desired Cartesian
                                position  .
                                         r
                                Since the PSOM is constructed on a hyper-lattice in  , finding the
                                shortest route s in S is equivalent to finding the shortest path in  .
                                Thus, all we need to do is to start the best-match search at the best-
                                match position s   curr  belonging to the current position, and the steep-
                                est gradient descent procedure will solve the problem.


                          Orientation preference: the “traditional solution”. If a certain end effec-
                                tor approach direction, for example a top–down orientation, is pre-
                                ferred, the problem transforms into the standard mixed position /
                                orientation task, as described above.

                          Maximum mobility reserve: “comfortable configuration”. If no further
                                orientation constraints are given, it might be useful to gain a large
                                joint mobility reserve — a reserve for further actions and re-actions
                                to unforeseen events.


                             Here, the latter case is of particular interest. A high mobility reserve
                          means to stay away from configurations close to any range limits. We
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