  Learning and inference movement is a very challenging problem due to its high
dimensionality and dependency to varied environments or tasks. In this paper,
we propose an effective probabilistic method for learning and inference of
basic movements. The motion planning problem is formulated as learning on a
directed graphic model and deep generative model is used to perform learning
and inference from demonstrations. An important characteristic of this method
is that it flexibly incorporates the task descriptors and context information
for long-term planning and it can be combined with dynamic systems for robot
control. The experimental validations on robotic approaching path planning
tasks show the advantages over the base methods with limited training data.
