Rosen Diankov

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Society is becoming more automated with robots beginning to perform most tasks in factories and starting to help out in home and office environments. One of the most important functions of robots is the ability to manipulate objects in their environment. Because the space of possible robot designs, sensor modalities, and target tasks is huge, researchers(More)
This paper combines grasp analysis and manipulation planning techniques to perform fast grasp planning in complex scenes. In much previous work on grasping, the object being grasped is assumed to be the only object in the environment. Hence the grasp quality metrics and grasping strategies developed do not perform well when the object is close to obstacles(More)
We describe the architecture, algorithms, and experiments with HERB, an autonomous mobile manipulator that performs useful manipulation tasks in the home. We present new algorithms for searching for objects, learning to navigate in cluttered dynamic indoor scenes, recognizing and registering objects accurately in high clutter using vision, manipulating(More)
We present a novel motion planning algorithm for performing constrained tasks such as opening doors and drawers by robots such as humanoid robots or mobile manipulators. Previous work on constrained manipulation transfers rigid constraints imposed by the target object motion directly into the robot configuration space. This often unnecessarily restricts the(More)
Simulation is essential for different robotic research fields such as mobile robotics, motion planning and grasp planning. For grasping in particular, there are no software simulation packages, which provide a holistic environment that can deal with the variety of aspects associated with this problem. These aspects include development and testing of new(More)
We present a planning algorithm called BiSpace that produces fast plans to complex high-dimensional problems by simultaneously exploring multiple spaces. We specifically focus on finding robust solutions to manipulation and grasp planning problems by using BiSpace’s special characteristics to explore the work and configuration spaces of the environment and(More)
This paper explores the use of statical learning methods on randomized path planning algorithms. A continuous, randomized version of A* is presented along with an empirical analysis showing planning time convergence rates in the robotic manipulation domain. The algorithm relies on several heuristics that capture a manipulator's kinematic feasibility and the(More)
In this paper we present a comprehensive perception system with applications to mobile manipulation and grasping for personal robotics. Our approach makes use of dense 3D point cloud data acquired using stereo vision cameras by projecting textured light onto the scene. To create models suitable for grasping, we extract the supporting planes and model object(More)
We present a vision-centric manipulation framework for reliably performing reach-and-grasp tasks in everyday environments. By combining grasp planning and visual feedback algorithms, and constantly considering sensor visibility, the framework can recover from sensor calibration errors and unexpected changes in the environment. Although many current robot(More)