Robotic grasping in unstructured environments requires the ability to select grasps for unknown objects and execute them while dealing with uncertainty due to sensor noise or calibration errors. In this work, we propose a simple but robust approach to grasp selection for unknown objects, and a reactive adjustment approach to deal with uncertainty in object location and shape. The grasp selection method uses 3D sensor data directly to determine a ranked set of grasps for objects in a scene, using heuristics based on both the overall shape of the object and its local features. The reactive grasping approach uses tactile feedback from fingertip sensors to execute a compliant robust grasp. We present experimental results to validate our approach by grasping a wide range of unknown objects. Our results show that reactive grasping can correct for a fair amount of uncertainty in the measured position or shape of the objects, and that our grasp selection approach is successful in grasping objects with a variety of shapes.
Abstract-We propose a planning method for grasping in cluttered environments, a method where the robot can make simultaneous contact with multiple objects. With this method, the robot reaches for and grasps the target while simultaneously contacting and moving aside objects to clear a desired path. We use a physics-based analysis of pushing to compute the motion of each object in the scene in response to a set of possible robot motions. Our method enables multiple robotobject interactions, interactions that can be pre-computed and cached. However, our method avoids object-object interactions to make the problem computationally tractable. Through tests on large sets of simulated scenes, we show that our planner produces more successful grasps in more complex scenes than versions that avoid any interaction with surrounding clutter. We validate our method on a real robot, a PR2, and show that it accurately predicts the outcome of a grasp. We also show that our approach, in conjunction with state-of-the-art object recognition tools, is applicable in real-life scenes that are highly cluttered and constrained.
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