Parameterizing actions to have the appropriate effects

Robots that are to perform their tasks reliably and skillfully in complex domains such as a human household need to apply both, qualitative and quantitative reasoning to achieve their goals. Consider a robot whose task is to make pancakes, and part of the plan is to put down the bottle with pancake mix after pouring it on the pan. The put-down location of the bottle is heavily under-specified but has a critical influence on the overall performance of the plan. For instance, when it places it at a location where it occludes other objects, the robot cannot see and grasp the occluded objects anymore unless the bottle is removed again. Other important aspects include stability and reachability. Objects should not flip over or fall. A badly chosen put-down location can “block” trajectories for grasping other objects that were valid before and can even prevent the robot from reaching these objects. In this paper, we show a lightweight and fast reasoning system that integrates qualitative and quantitative reasoning based on Prolog. We demonstrate how we implement predicates that make use of OpenGL, the Bullet physics engine and inverse kinematics calculation. Equipped with generative models yielding pose candidates, our system allows for the generation of action parameters such as put down locations under the constraints of the current and future actions in real time.