Deep Visual Heuristics: Learning Feasibility of Mixed-Integer Programs for Manipulation Planning
Danny Driess,Ozgur Oguz,Jung-Su Ha,Marc Toussaint,Danny Driess,Ozgur Oguz,Jung-Su Ha,Marc Toussaint
In this paper, we propose a deep neural network that predicts the feasibility of a mixed-integer program from visual input for robot manipulation planning. Integrating learning into task and motion planning is challenging, since it is unclear how the scene and goals can be encoded as input to the learning algorithm in a way that enables to generalize over a variety of tasks in environments with ch...
