In this paper, we tackle the problem of 6-DoF grasp detection which is crucial for robot grasping in cluttered real-world scenes. Unlike existing approaches which synthesize 6-DoF grasp data sets and train grasp quality networks with input grasp representations based on point clouds, we rather take a novel hierarchical approach which does not use any 6-DoF grasp data. We cast the 6-DoF grasp detection problem as a robot arm approaching direction selection problem using the existing 4-DoF grasp detection algorithm, by exploiting a fully convolutional grasp quality network for evaluating the quality of an approaching direction. To select the best approaching direction with the highest grasp quality, we propose an approaching direction selection method which leverages a geometry-based prior and a derivative-free optimization method. Specifically, we optimize the direction iteratively using the cross entropy method with initial samples of surface normal directions. Our algorithm efficiently finds diverse 6-DoF grasps by the novel way of evaluating and optimizing approaching directions. We validate that the proposed method outperforms other selection methods in scenarios with cluttered objects in a physics-based simulator. Finally, we show that our method outperforms the state-of-the-art grasp detection method in real-world experiments with robots.

We propose an online compensation scheme for rollover prevention of mobile manipulators based on the invariance control framework, and that makes use of recursively computed analytic gradients of the zero-moment point (ZMP) function. Our controller relaxes many of the assumptions made in existing approaches, and enhances robustness as well as effectiveness through the use of exact gradient information. Several case studies demonstrate the improved performance of our controller over existing rollover prevention schemes.