This paper explores combining task and manipulation planning for humanoid robots. Existing methods tend to either take prohibitively long to compute for humanoids or artificially limit the physical capabilities of the humanoid platform by restricting the robot's actions to predetermined trajectories. We present a hybrid planning system which is able to scale well for complex tasks without relying on predetermined robot actions. Our system utilizes the hybrid backward-forward planning algorithm for high-level task planning combined with humanoid primitives for standing and walking motion planning. These primitives are designed to be efficiently computable during planning, despite the large amount of complexity present in humanoid robots, while still informing the task planner of the geometric constraints present in the problem. Our experiments apply our method to simulated pick-and-place problems with additional gate constraints impacting navigation using the DRC-HUBO1 robot. Our system is able to solve puzzle-like problems on a humanoid within a matter of minutes.
['Michael X. Grey', 'Caelan Reed Garrett', 'C. Karen Liu', 'Aaron D. Ames', 'Andrea Lockerd Thomaz']
Humanoid manipulation planning using backward-forward search