Recent advances in both anthropomorphic robots and bimanual industrial manipulators had led to an increased interest in the specific problems pertaining to dual arm manipulation. For the future, we foresee robots performing humanlike tasks in both domestic and industrial settings. It is therefore natural to study specifics of dual arm manipulation in humans and methods for using the resulting knowledge in robot control. The related scientific problems range from low-level control to high level task planning and execution. This review aims to summarize the current state of the art from the heterogenous range of fields that study the different aspects of these problems specifically in dual arm manipulation.

We consider the problem of grasp and manipulation planning when the state of the world is only partially observable. Specifically, we address the task of picking up unknown objects from a table top. The proposed approach to object shape prediction aims at closing the knowledge gaps in the robot's understanding of the world. A completed state estimate of the environment can then be provided to a simulator in which stable grasps and collision-free movements are planned.The proposed approach is based on the observation that many objects commonly in use in a service robotic scenario possess symmetries. We search for the optimal parameters of these symmetries given visibility constraints. Once found, the point cloud is completed and a surface mesh reconstructed.Quantitative experiments show that the predictions are valid approximations of the real object shape. By demonstrating the approach on two very different robotic platforms its generality is emphasized.

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We propose a system for markerless pose estimation and tracking of a robot manipulator. By tracking the manipulator, we can obtain an accurate estimate of its position and orientation necessary in many object grasping and manipulation tasks. Tracking the manipulator allows also for better collision avoidance. The method is based on the notion of virtual visual servoing. We also propose the use of distance transform in the control loop, which makes the performance independent of the feature search window.