Humanoids and Intelligence Systems Lab - Institute for Anthropomatics and Robotics

Grasp Planning

Various approach approach directions of the hand towards the object. Each sphere indicates the wrist position of the hand for a certain grasp. Bigger spheres indicate more stable grasps.

Grasping with humanoid robots

Since our humanoid robots ARMAR-IIIa and ARMAR-IIIb operate in complex daily household environments, they have to deal with a variety of problems. One major challenge lies in grasping and manipulation of objects within the scene. Therefore, a sophisticated grasp planning strategy is essential, in order to guarantee collision-free and stable grasping of household objects. A crucial component of grasp planning is the offline grasp analysis performed to find stable grasps by means of using a simulation environment.

OpenGRASP toolkit

Since available grasping simulators do not provide a modular architecture and thus are difficult to extend and to integrate with other tools and control frameworks, we started the development of the OpenGRASP toolkit. OpenGRASP is based on the modularly structured open source simulation environment OpenRAVE, which allows for loading models of various robots and objects and offers interfaces to scripting languages such as Matlab, Octave and Python. In OpenGRASP it is possible to simulate various different kinds of actuators and sensors and exchange physics engines. OpenGRASP also supports standard file formats such as COLLADA, as well as the functionality to import and export robot and object models from and to standard representations. In addition, OpenGRASP incorporates a robot editor which allows for generation of robot models in an easy and intuitive manner.

Offline grasp analysis

Finding stable grasps on 3D objects is considered to be one of the hardest problems in robotics, since many parameters such as hand kinematics, object geometry, material properties and forces as well as obstacles in the environment have to be taken into account. This results in a high-dimensional space of possible grasps that cannot be searched exhaustively. A possible solution is grasp planning in simulation. This means that models of the robot hand and the object are loaded into a simulation environment in order to test various grasps on the object. The testing process is organized as follows. The robot hand is placed at different starting positions and orientations relative to the object. Then the hand approaches the object. Once collision between hand and object is detected, the fingers of the hand close until all finger links have contact with the object or cannot move any more. The coordinates of the contact points are used to calculate a grasp stability criterion. This way a multitude of different candidate grasps can be automatically tested and rated. However, collision detection and stability testing are computationally intensive. Considering the complexity of grasp planning, the heuristics used for generating starting positions and orientations of the hand are of major interest. In order to reduce the complexity of grasp planning it is necessary to find heuristics that maximize the fraction of stable grasps among the generated candidate grasps. In this context a promising approach is the evaluation of Medial Axis data of objects that is investigated at KIT.