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Robots operating in household environments need to interact with food containers of different types. Whether a container is filled with milk, juice, yogurt or coffee may affect the way robots grasp and manipulate the container. In this paper, we concentrate on the problem of identifying what kind of content is in a container based on tactile and/or visual feedback in combination with grasping. In...
We present an extension to the Hierarchical Dirichlet Process (HDP), which allows for the inclusion of supervision. Our model marries the non-parametric benefits of HDP with those of Supervised Latent Dirichlet Allocation (SLDA) to enable learning the topic space directly from data while simultaneously including the labels within the model. The proposed model is learned using variational inference...
In this paper, we propose an approach for vision-based pose estimation of a robot hand or full-body pose. The method is based on virtual visual servoing using a CAD model of the robot and it combines 2-D image features with depth features. The method can be applied to estimate either the pose of a robot hand or pose of the whole body given that its joint configuration is known. We present experimental...
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 human-like 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...
We study visual servoing in a framework of detection and grasping of unknown objects. Classically, visual servoing has been used for applications where the object to be servoed on is known to the robot prior to the task execution. In addition, most of the methods concentrate on aligning the robot hand with the object without grasping it. In our work, visual servoing techniques are used as building...
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...
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