This paper addresses the problem of mixed initiative, shared control for master-slave grasping and manipulation. We propose a novel system, in which an autonomous agent assists a human in teleoperating a remote slave arm gripper, using a haptic master device. Our system is designed to exploit the human operator's expertise in selecting stable grasps (still an open research topic in autonomous robotics). Meanwhile, a-priori knowledge of: i) the slave robot kinematics, and ii) the desired post-grasp manipulative trajectory, are fed to an autonomous agent which transmits force cues to the human, to encourage maximally manipulable grasp pose selections. Specifically, the autonomous agent provides force cues to the human, during the reach-to-grasp phase, which encourage the human to select grasp poses which maximise manipulation capability during the post-grasp object manipulation phase. We introduce a task-relevant velocity manipulability cost function (TOV), which is used to identify the maximum kinematic capability of a manipulator during post-grasp motions, and feed this back as force cues to the human during the pre-grasp phase. We show that grasps which minimise TOV result in significantly reduced control effort of the manipulator, compared to other feasible grasps. We demonstrate the effectiveness of our approach by experiments with both real and simulated robots.
Grasping and manipulative motion planning have been widely studied in the robotics literature @cite_22 @cite_5 . Most of the studies, however, focused either on the first phase of grasping and manipulation, namely approach phase @cite_28 , or on the second phase, namely autonomously manoeuvring the manipulator @cite_6 .
@cite_28 @cite_5 @cite_22 @cite_6
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We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. In this work, we apply a deep learning approach to solve this problem, which avoids time-consuming hand-design of features. This presents two main challenges. First, we need to evaluate a huge number of candidate grasps. In order to make detection fast and robust, we present a two-step cascaded system with two deep networks, where the top detections from the first are re-evaluated by the second. The first network has fewer features, is faster to run, and can effectively prune out unlikely candidate grasps. The second, with more features, is slower but has to run only on the top few detections. Second, we need to handle multimodal inputs effectively, for which we present a method that applies structured regularization on the weights based on multimodal group regularization. We show that our method improves performance on an RGBD robotic grasping dataset, and can be used to successfully execute grasps on two different robotic platforms. We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real time to achieve successful grasps. To train our network, we collected over 800,000 grasp attempts over the course of two months, using between 6 and 14 robotic manipulators at any given time, with differences in camera placement and hardware. Our experimental evaluation demonstrates that our method achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing. In this paper, we present a novel framework to drive automatic robotic grasp by matching camera captured RGB-D data with 3D meshes, on which prior knowledge for grasp is pre-defined for each object type. The proposed framework consists of two modules, namely, pre-defining grasping knowledge for each type of object shape on 3D meshes, and automatic robotic grasping by matching RGB-D data with pre-defined 3D meshes. In the first module, we scan 3D meshes for typical object shapes and pre-define grasping regions for each 3D shape surface, which will be considered as the prior knowledge for guiding automatic robotic grasp. In the second module, for each RGB-D image captured by a depth camera, we recognize 2D shape of the object in it by an SVM classifier, and then segment it from background using depth data. Next, we propose a new algorithm to match the segmented RGB-D shape with predefined 3D meshes to guide robotic self-location and grasp by an automatic way. Our experimental results show that the proposed framework is particularly useful to guide camera based robotic grasp.