In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands. Robot joint angles are directly generated from depth images of the human hand that produce visually similar robot hand poses in an end-to-end fashion. The special structure of TeachNet, combined with a consistency loss function, handles the differences in appearance and anatomy between human and robotic hands. A synchronized human-robot training set is generated from an existing dataset of labeled depth images of the human hand and simulated depth images of a robotic hand. The final training set includes 400K pairwise depth images and joint angles of a Shadow C6 robotic hand. The network evaluation results verify the superiority of TeachNet, especially regarding the high-precision condition. Imitation experiments and grasp tasks teleoperated by novice users demonstrate that TeachNet is more reliable and faster than the state-of-the-art vision-based teleoperation method.
Visual model-based methods, such as @cite_11 @cite_25 , compute continuous 3D positions and orientations of thumb and index finger from segmented images based on a camera system and control a parallel jaw gripper mounted on a six-axis robot arm. Romero @cite_9 classifies human grasps into grasp classes and approaches based on human hand images then maps them to a discrete set of corresponding robot grasp classes following the external observation paradigm.
@cite_9 @cite_25 @cite_11
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This paper presents a vision based method for grasp classification. It is developed as part of a Programming by Demonstration (PbD) system for which recognition of objects and pick-and-place actions represent basic building blocks for task learning. In contrary to earlier approaches, no articulated 3D reconstruction of the hand over time is taking place. The indata consists of a single image of the human hand. A 2D representation of the hand shape, based on gradient orientation histograms, is extracted from the image. The hand shape is then classified as one of six grasps by finding similar hand shapes in a large database of grasp images. The database search is performed using Locality Sensitive Hashing (LSH), an approximate k-nearest neighbor approach. The nearest neighbors also give an estimated hand orientation with respect to the camera. The six human grasps are mapped to three Barret hand grasps. Depending on the type of robot grasp, a precomputed grasp strategy is selected. The strategy is further parameterized by the orientation of the hand relative to the object. To evaluate the potential for the method to be part of a robust vision system, experiments were performed, comparing classification results to a baseline of human classification performance. The experiments showed the LSH recognition performance to be comparable to human performance. This paper presents an egocentric vision (ego-vision) system for hand grasp analysis in unstructured environments. Our goal is to automatically recognize hand grasp types and to discover the visual structures of hand grasps using a wearable camera. In the proposed system, free hand–object interactions are recorded from a first-person viewing perspective. State-of-the-art computer vision techniques are used to detect hands and extract hand-based features. A new feature representation that incorporates hand tracking information is also proposed. Then, grasp classifiers are trained to discriminate among different grasp types from a predefined grasp taxonomy. Based on the trained grasp classifiers, visual structures of hand grasps are learned using an iterative grasp clustering method. In experiments, grasp recognition performance in both laboratory and real-world scenarios is evaluated. The best classification accuracy our system achieves is @math and @math , respectively. System generality to different tasks and users is also verified by the experiments. Analysis in a real-world scenario shows that it is possible to automatically learn intuitive visual grasp structures that are consistent with expert-designed grasp taxonomies. We propose a novel method for performing fine-grained recognition of human hand grasp types using a single monocular image to allow computational systems to better understand human hand use. In particular, we focus on recognizing challenging grasp categories which differ only by subtle variations in finger configurations. While much of the prior work on understanding human hand grasps has been based on manual detection of grasps in video, this is the first work to automate the analysis process for fine-grained grasp classification. Instead of attempting to utilize a parametric model of the hand, we propose a hand parsing framework which leverages a data-driven learning to generate a pixel-wise segmentation of a hand into finger and palm regions. The proposed approach makes use of appearance-based cues such as finger texture and hand shape to accurately determine hand parts. We then build on the hand parsing result to compute high-level grasp features to learn a supervised fine-grained grasp classifier. To validate our approach, we introduce a grasp dataset recorded with a wearable camera, where the hand and its parts have been manually segmented with pixel-wise accuracy. Our results show that our proposed automatic hand parsing technique can improve grasp classification accuracy by over 30 percentage points over a state-of-the-art grasp recognition technique. We present a system for grasping unknown objects, even from piles or cluttered scenes, given a point cloud. Our method is based on the topography of a given scene and abstracts grasp-relevant structures to enable machine learning techniques for grasping tasks. We describe how Height Accumulated Features HAF and their extension, Symmetry Height Accumulated Features, extract grasp relevant local shapes. We investigate grasp quality using an F-score metric. We demonstrate the gain and the expressive power of HAF by comparing its trained classifier with one that resulted from training on simple height grids. An efficient way to calculate HAF is presented. We describe how the trained grasp classifier is used to explore the whole grasp space and introduce a heuristic to find the most robust grasp. We show how to use our approach to adapt the gripper opening width before grasping. In robotic experiments we demonstrate different aspects of our system on three robot platforms: a Schunk seven-degree-of-freedom arm, a PR2 and a Kuka LWR arm. We perform tasks to grasp single objects, autonomously unload a box and clear the table. Thereby we show that our approach is easily adaptable and robust with respect to different manipulators. As part of the experiments we compare our algorithm with a state-of-the-art method and show significant improvements. Concrete examples are used to illustrate the benefit of our approach compared with established grasp approaches. Finally, we show advantages of the symbiosis between our approach and object recognition.