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Grasping systems that build upon meticulously planned hand postures rely on precise knowledge of object geometry, mass and frictional properties — assumptions which are often violated in practice. In this work, we propose an alternative solution to the problem of grasp acquisition in simple autonomous pick and place scenarios, by utilizing the concept of grasp envelopes: sets of constraints on gripper...
This paper presents a probabilistic approach for task-specific grasping of novel objects from a known category. RGB-D imaging is used to establish an initial estimate of the target object's shape and pose, which is used to plan an optimal grasp over the uncertain estimate. Tactile information is then used for incrementally improving the estimate and sequentially replanning better grasps. The resulting...
Grasp planning in multi-robot systems is usually studied in a centralized setting with all robots sharing common knowledge about the overall system. Relaxing this assumption would allow multiple mobile manipulators to cooperate even without strict and precise coordination. Moreover, most typical tasks for cooperative settings, such as transporting heavy objects, require certain forces/torques to be...
Mobile manipulation of large objects can benefit greatly from the use of multiple cooperating robots. Multi-robot coordination of decentralized systems is, however, challenging due to the nature of such systems. For this reason, many planning problems are yet unexplored. This paper proposes two decentralized approaches for cooperative grasp planning. In our setting, agents do not have information...
Manipulating unknown objects in a cluttered environment is difficult because object composition is uncertain. Because of this uncertainty, earlier work has concentrated on finding the “best” object composition and based on this composition decided on manipulation actions. Contrary to earlier work, we 1) utilize different possible object compositions in decision making, 2) take advantage of object...
In this work, we propose to reconstruct a complete 3-D model of an unknown object by fusion of visual and tactile information while the object is grasped. Assuming the object is symmetric, a first hypothesis of its complete 3-D shape is generated from a single view. This initial model is used to plan a grasp on the object which is then executed with a robotic manipulator equipped with tactile sensors...
In this paper, we present a novel probabilistic framework for grasping. In the framework, grasp and object attributes, on-line sensor information and the stability of a grasp are all considered through probabilistic models. We describe how sensor-based grasp planning can be formulated in a probabilistic framework and how information about object attributes can be updated simultaneously using on-line...
The grasping skill is an indispensable quality for general service robotics. In a home-like natural environment, manipulated objects may be unknown in advance, which prevents the use of a combination of traditional grasp planning and visual pose estimation to realize grasping. Stereo vision is an inexpensive and relatively general sensor for 3-D objects. However, the quality of the sensor data from...
An important ability of a robot that interacts with the environment and manipulates objects is to deal with the uncertainty in sensory data. Sensory information is necessary to, for example, perform online assessment of grasp stability. We present methods to assess grasp stability based on haptic data and machine-learning methods, including AdaBoost, support vector machines (SVMs), and hidden Markov...
In this paper, the problem of learning grasp stability in robotic object grasping based on tactile measurements is studied. Although grasp stability modeling and estimation has been studied for a long time, there are few robots today able of demonstrating extensive grasping skills. The main contribution of the work presented here is an investigation of probabilistic modeling for inferring grasp stability...
The adoption of robots for service tasks in natural environments calls for the use of sensors to allow manipulation of objects under imperfect environment knowledge and the use of knowledge transfer from humans. This paper addresses these challenges by proposing a new abstraction architecture for embodiment independent sensor-based control of manipulation. The aim is to address three specific challenges:...
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