Increasing the robustness of grasping actions and the recovery from failure is key to improving a robot’s autonomy. Endowing robots with the ability to robustly grasp and manipulate unknown difficult objects such as stones is required for sample collection in unknown environments. In this paper, we present a complete system for robust grasping of stones, which integrates stone segmentation based on depth information, the generation of grasp hypotheses and pushing actions as well as their execution. In particular, our system has been designed to solve these tasks on robots with limited computing resources. We evaluate the performance in real robot experiments in the context of stone sample collection. The results show that such a challenging task is achievable under computing resource constraints.

Tactile sensing of surface normals is essential for exploration of unknown objects. Many tactile sensors have been developed for contact measurement. However, few of these sensors provide surface orientation, and only up to a limited degree. This paper presents a novel contact and surface orientation sensor concept and its application for surface reconstruction of unknown objects. The sensor is comprised of an Inertial Measurement Unit (IMU) and a pressure sensor to accurately estimate the surface orientation in a wide range, while at the same time measuring contact force. We describe the developed sensor prototype and evaluate its performance regarding contact detection capability and normal estimation accuracy. We use this to reconstruct the surface of unknown objects using the humanoid robot ARMAR-III resulting in a mean reconstruction accuracy of 3.6 mm.