  This work presents an efficient framework to generate a motion plan of a
robot with high degrees of freedom (e.g., a humanoid robot).
High-dimensionality of the robot configuration space often leads to
difficulties in utilizing the widely-used motion planning algorithms, since the
volume of the decision space increases exponentially with the number of
dimensions. To handle complications arising from the large decision space, and
to solve a corresponding motion planning problem efficiently, two key concepts
are adopted in this work: First, the Gaussian process latent variable model
(GP-LVM) is utilized for low-dimensional representation of the original
configuration space. Second, an approximate inference algorithm is used,
exploiting through the duality between control and estimation, to explore the
decision space and to compute a high-quality motion trajectory of the robot.
Utilizing the GP-LVM and the duality between control and estimation, we
construct a fully probabilistic generative model with which a high-dimensional
motion planning problem is transformed into a tractable inference problem.
Finally, we compute the motion trajectory via an approximate inference
algorithm based on a variant of the particle filter. The resulting motions can
be viewed in the supplemental video. ( https://youtu.be/kngEaOR4Esc )
