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 robot’s redundant whole body controller and uses analytic gradients to jointly optimise the motion co...

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Michael Gienger, Marc Toussaint, Christian Goerick
• Keywords: Humanoid Robot, Humanoid Robot Asimo, IROS 2008, Robotics, Task Maps