We applied the MMC approach to a population-code based representation – this allows to decompose complex robotic structures into simple, local geometric relations that can be easily solved in parallel. The population-code representation shall be utilized in the future to allow for integration of additional sensory signals. The paper has been presented at the IJCNN in Ireland, 2015:
Population based encodings allow to represent probabilistic and fuzzy state estimates. Such a representation will be introduced and applied for the case of a redundant manipulator. Following the Mean of Multiple Computations principle, a neural network model (PbMMC) is presented in which the overall complexity is divided into multiple local relationships. This allows to solve inverse, forward and mixed kinematic problems. The local transformations in between the kinematic variables can be sufficiently well learned by small single MLP layers. … the model as such is quite flexible as it can keep track of multiple possible solutions at the same time.
Citation: Baum, M., Meier, M., and Schilling, M. (2015), “Population based Mean of Multiple Computations Networks: A Building Block for Kinematic Models”. International Joint Conference on Neural Networks 2015, Killarney (Ireland).