Task maps in humanoid robot manipulation

This paper presents an integrative approach to solve the coupled problem of reaching and grasping an object in a cluttered environment with a humanoid robot. While finding an optimal grasp is often treated independently from reaching to the object, in most situations it depends on how the robot can reach a pregrasp pose while avoiding obstacles. We tackle this problem by introducing the concept of task maps which represent the manifold of feasible grasps for an object. Rather than defining a single end-effector goal position, a task map defines a goal hyper volume in the task space. We show how to efficiently learn such maps using the rapidly exploring random tree algorithm. Further, we generalise a previously developed motion optimisation scheme, based on a sequential attractor representation of motion, to cope with such task maps. The optimisation procedure incorporates the robotpsilas redundant whole body controller and uses analytic gradients to jointly optimise the motion costs (including criteria such as collision and joint limit avoidance, energy efficiency, etc.) and the choice of the grasp on the manifold of valid grasps. This leads to a preference of grasps which are easy to reach. The approach is demonstrated in two reach-grasp simulation scenarios with the humanoid robot ASIMO.