Stance selection for humanoid grasping tasks by inverse reachability maps

Abstract

In grasping tasks carried out with humanoids, knowledge about the robot's reachable workspace is important. Without this knowledge, it might be necessary to repeatedly adapt the stance location and call an inverse kinematics solver before a valid robot configuration to reach a given grasping pose can be found. In this paper, we present an approach to select an optimal stance location in SE(2) for a humanoid robot's feet relative to a desired grasp pose. We use a precomputed representation of the robot's reachable workspace that stores quality information in addition to spatial data. By inverting this representation we obtain a so-called inverse reachability map (IRM) containing a collection of potential stance poses for the robot. The generated IRM can subsequently be used to select a statically stable, collision-free stance configuration to reach a given grasping target. We evaluated our approach with a Nao humanoid in simulation and in experiments with the real robot. As the experiments show, using our approach optimal stance poses can easily be obtained. Furthermore, the IRM leads to a substantially increased success rate of reaching grasping poses compared to other meaningful foot placements within the vicinity of the desired grasp.