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HMFP-DBRNN: Real-Time Hand Motion Filtering and Prediction via deep Bidirectional RNN

IEEE robotics AND automation LETTERS 2019年第2期4卷 1061-1068页

作者： Shahtalebi, Soroosh Atashzar, Seyed Farokh Patel, Rajni V. Mohammadi, Arash Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ H3G 1M8 Canada Imperial Coll London Dept Bioengn London SW7 2AZ England Univ Western Ontario Dept Elect & Comp Engn London ON N6A 3K7 Canada

Pathological hand tremor (PHT) is among the most common movement symptoms of several neurological disorders including Parkinson's disease and essential tremor. Extracting PHT is of paramount importance in several engineering and clinical applications such as assistive and robotic rehabilitation technologies. In such systems, PHT is modeled as the input noise to the system and thus there is a surge of interest in estimation an compensation of the noise. Although various works in the literature have attempted to estimate and extract the PHT, in this letter, first, we argue that the ground truth signal used in existing works to optimize the performance of tremor extraction techniques is not accurate enough, and thus the performance measures for the prior techniques are not perfectly reliable. In addition, most of the existing tremor extraction techniques impose unrealistic assumptions, which are, typically, violated in practical settings. This letter proposes a novel technique that for the first time incorporates deep bidirectional recurrent neural networks as a processing tool for PHT extraction. Moreover, we devise an intuitively pleasing training strategy that enables the network to perform not only online estimation but also online prediction of the voluntary hand motion in a myopic fashion, which is currently a significantly important unmet need for rehabilitative and assistive robotic technologies designed for patients with pathological tremor.

关键词： deep learning in robotics and automation medical robots and systems rehabilitation robotics

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Weakly Supervised Fruit Counting for Yield Estimation Using Spatial Consistency

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IEEE robotics AND automation LETTERS 2019年第3期4卷 2348-2355页

作者： Bellocchio, Enrico Ciarfuglia, Thomas A. Costante, Gabriele Valigi, Paolo Univ Perugia Dept Engn I-06125 Perugia Italy

Fruit counting is a fundamental component for yield estimation applications. Most of the existing approaches address this problem by relying on fruit models (i.e., by using object detectors) or by explicitly learning to count. Despite the impressive results achieved by these approaches, all of them need strong supervision information during the training phase. In agricultural applications, manual labeling may require a huge effort or, in some cases, it could be impossible to acquire fine-grained ground truth labels. In this letter, we tackle this problem by proposing a weakly supervised framework that learns to count fruits without the need for task-specific supervision labels. In particular, we devise a novel convolutional neural network architecture that requires only a simple image level binary classifier to detect whether the image contains instances of the fruits or not and combines this information with image spatial consistency constraints. The result is an architecture that learns to count without task-specific labels (e.g., object bounding boxes or the multiplicity of fruit instances in the image). The experiments on three different varieties of fruits (i.e., olives, almonds, and apples) show that our approach reaches performances that are comparable with SotA approaches based on the supervised paradigm.

关键词： Agricultural automation computer vision for other robotic applications deep learning in robotics and automation robotics in agriculture and forestry visual learning

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Real-Time Instrument Segmentation in Robotic Surgery Using Auxiliary Supervised deep Adversarial learning

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IEEE robotics AND automation LETTERS 2019年第2期4卷 2188-2195页

作者： Islam, Mobarakol Atputharuban, Daniel Anojan Ramesh, Ravikiran Ren, Hongliang Natl Univ Singapore NUS Grad Sch Integrat Sci & Engn Singapore 117581 Singapore Natl Univ Singapore Dept Biomed Engn Singapore 117581 Singapore Univ Moratuwa Dept Elect & Telecommun Moratuwa 10400 Sri Lanka Natl Inst Technol Trichy Dept Instrumentat & Control Engn Tiruchirappalli 620015 Tamil Nadu India

Robot-assisted surgery is an emerging technology that has undergone rapid growth with the development of robotics and imaging systems. Innovations in vision, haptics, and accurate movements of robot arms have enabled surgeons to perform precise minimally invasive surgeries. Real-time semantic segmentation of the robotic instruments and tissues is a crucial step in robot-assisted surgery. Accurate and efficient segmentation of the surgical scene not only aids in the identification and tracking of instruments but also provides contextual information about the different tissues and instruments being operated with. For this purpose, we have developed a light-weight cascaded convolutional neural network to segment the surgical instruments from high-resolution videos obtained from a commercial robotic system. We propose a multi-resolution feature fusion module to fuse the feature maps of different dimensions and channels from the auxiliary and main branch. We also introduce a novel way of combining auxiliary loss and adversarial loss to regularize the segmentation model. Auxiliary loss helps the model to learn low-resolution features, and adversarial loss improves the segmentation prediction by learning higher order structural information. The model also consists of a lightweight spatial pyramid pooling unit to aggregate rich contextual information in the intermediate stage. We show that our model surpasses existing algorithms for pixelwise segmentation of surgical instruments in both prediction accuracy and segmentation time of high-resolution videos.

关键词： deep learning in robotics and automation visual tracking object detection segmentation and categorization

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SilhoNet: An RGB Method for 6D Object Pose Estimation

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IEEE robotics AND automation LETTERS 2019年第4期4卷 3727-3734页

作者： Billings, Gideon Johnson-Roberson, Matthew Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA

Autonomous robot manipulation involves estimating the translation and orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose. Methods using RGB-D data have shown great success in solving this problem. However, there are situations where cost constraints or the working environment may limit the use of RGB-D sensors. When limited to monocular camera data only, the problem of object pose estimation is very challenging. In this letter, we introduce a novel method called SilhoNet that predicts 6D object pose from monocular images. We use a convolutional neural network pipeline that takes in region of interest proposals to simultaneously predict an intermediate silhouette representation for objects with an associated occlusion mask and a 3D translation vector. The 3D orientation is then regressed fromthe predicted silhouettes. We show that our method achieves better overall performance on theYCB-Video dataset than two networks for 6D pose estimation from monocular image input.

关键词： Computer vision for automation computer vision for other robotic applications deep learning in robotics and automation recognition visual learning

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Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks

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IEEE robotics AND automation LETTERS 2019年第4期4卷 3153-3160页

作者： Parsa, Behnoosh Samani, Ekta U. Hendrix, Rose Devine, Cameron Singh, Shashi M. Devasia, Santosh Banerjeee, Ashis G. Univ Washington Dept Mech Engn Seattle WA 98195 USA Indian Inst Technol Gandhinagar Dept Mech Engn Gandhinagar 382355 India Univ Washington Dept Ind & Syst Engn Seattle WA 98195 USA

Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human-robot collaboration systems for logistics and manufacturing applications. We present a foundational paradigm to address this challenge by formulating the problem as one of action segmentation from RGB-D camera videos. Spatial features are first learned using a deep convolutional model from the video frames, which are then fed sequentially to temporal convolutional networks to semantically segment the frames into a hierarchy of actions, which are either ergonomically safe, require monitoring, or need immediate attention. For performance evaluation, in addition to an open-source kitchen dataset, we collected a new dataset comprising 20 individuals picking up and placing objects of varying weights to and from cabinet and table locations at various heights. Results show very high (87%-94%) F1 overlap scores among the ground truth and predicted frame labels for videos lasting over 2 min and consisting of a large number of actions.

关键词： deep learning in robotics and automation human-centered automation computer vision for automation action segmentation ergonomic safety

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3-D Deformable Object Manipulation Using deep Neural Networks

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IEEE robotics AND automation LETTERS 2019年第4期4卷 4255-4261页

作者： Hu, Zhe Han, Tao Sun, Peigen Pan, Jia Manocha, Dinesh City Univ Hong Kong Dept Biomed Engn Hong Kong Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Univ Maryland Dept Comp Sci College Pk MD 20742 USA

Due to its high dimensionality, deformable object manipulation is a challenging problem in robotics. In this letter, we present a deep neural network based controller to servo control the position and shape of deformable objects with unknown deformation properties. In particular, a multi-layer neural network is used to map between the robotic end-effector's movement and the object's deformation measurement using an online learning strategy, In addition, we introduce a novel feature to describe deformable objects' deformation used in visual servoing. This feature is directly extracted from the 3-D point cloud rather from the 2-D image as in previous work. In addition, we perform simultaneous tracking and reconstruction for the deformable object to resolve the partial observation problem during the deformable object manipulation. We validate the performance of our algorithm and controller on a set of deformable object manipulation tasks and demonstrate that our method can achieve effective and accurate servo control for general deformable objects with a wide variety of goal settings. Experiment videos are available at https://***/view/mso-deep.

关键词： deep learning in robotics and automation Visual Servoing Dual Arm Manipulation RGB-D Perception Model learning for Control

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GCNv2: Efficient Correspondence Prediction for Real-Time SLAM

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IEEE robotics AND automation LETTERS 2019年第4期4卷 3505-3512页

作者： Tang, Jiexiong Ericson, Ludvig Folkesson, John Jensfelt, Patric KTH Royal Inst Technol Ctr Autonomous Syst SE-10044 Stockholm Sweden

In this letter, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors. GCNv2 is built on our previous method, GCN, a network trained for 3D projective geometry. GCNv2 is designed with a binary descriptor vector as the ORB feature so that it can easily replace ORB in systems such as ORB-SLAM2. GCNv2 significantly improves the computational efficiency over GCN that was only able to run on desktop hardware. We show how a modified version of ORB-SLAM2 using GCNv2 features runs on a Jetson TX2, an embedded low-power platform. Experimental results show that GCNv2 retains comparable accuracy as GCN and that it is robust enough to use for control of a flying drone.

关键词： Visual-based navigation SLAM deep learning in robotics and automation

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learning From Humans How to Grasp: A Data-Driven Architecture for Autonomous Grasping With Anthropomorphic Soft Hands

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1533-1540页

作者： Della Santina, Cosimo Arapi, Visar Averta, Giuseppe Damiani, Francesca Fiore, Gaia Settimi, Alessandro Catalano, Manuel G. Bacciu, Davide Bicchi, Antonio Bianchi, Matteo Univ Pisa Ctr Ric Enrico Piaggio I-56126 Pisa Italy Univ Pisa Dipartimento Ingn Informaz I-56126 Pisa Italy Fdn Ist Italiano Tecnol Soft Robot Human Cooperat & Rehabil I-16163 Genoa Italy Univ Pisa Dipartimento Informat I-56127 Pisa Italy

Soft hands are robotic systems that embed compliant elements in their mechanical design. This enables an effective adaptation with the items and the environment, and ultimately, an increase in their grasping performance. These hands come with clear advantages in terms of ease-to-use and robustness if compared with classic rigid hands, when operated by a human. However, their potential for autonomous grasping is still largely unexplored, due to the lack of suitable control strategies. To address this issue, in this letter, we propose an approach to enable soft hands to autonomously grasp objects, starting from the observations of human strategies. A classifier realized through a deep neural network takes as input the visual information on the object to be grasped, and predicts which action a human would perform to achieve the goal. This information is hence used to select one among a set of human-inspired primitives, which define the evolution of the soft hand posture as a combination of anticipatory action and touch-based reactive grasp. The architecture is completed by the hardware component, which consists of an RGB camera to look at the scene, a 7-DoF manipulator, and a soft hand. The latter is equipped with inertial measurement units at the fingernails for detecting contact with the object. We extensively tested the proposed

关键词： Natural Machine Motion deep learning in robotics and automation Modeling Control learning for Soft Robots Grasping

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DispSegNet: Leveraging Semantics for End-to-End learning of Disparity Estimation From Stereo Imagery

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1162-1169页

作者： Zhang, Junming Skinner, Katherine A. Vasudevan, Ram Johnson-Roberson, Matthew Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Michigan Robot Program Ann Arbor MI 48109 USA Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA

Recentwork has shown that convolutional neural networks (CNNs) can be applied successfully in disparity estimation, but these methods still suffer from errors in regions of low texture, occlusions, and reflections. Concurrently, deep learning for semantic segmentation has shown great progress in recent years. In this letter, we design a CNN architecture that combines these two tasks to improve the quality and accuracy of disparity estimation with the help of semantic segmentation. Specifically, we propose a network structure in which these two tasks are highly coupled. One key novelty of this approach is the two-stage refinement process. Initial disparity estimates are refined with an embedding learned from the semantic segmentation branch of the network. The proposed model is trained using an unsupervised approach, in which images from one half of the stereo pair are warped and compared against images from the other camera. Another key advantage of the proposed approach is that a single network is capable of outputting disparity estimates and semantic labels. These outputs are of great use in autonomous vehicle operation;with real-time constraints being key, such performance improvements increase the viability of driving applications. Experiments on KITTI and Cityscapes datasets show that our model can achieve state-of-the-art results and that leveraging embedding learned from semantic segmentation improves the performance of disparity estimation.

关键词： Computer vision for transportation deep learning in robotics and automation

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Sparse2Dense: From Direct Sparse Odometry to Dense 3-D Reconstruction

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IEEE robotics AND automation LETTERS 2019年第2期4卷 530-537页

作者： Tang, Jiexiong Folkesson, John Jensfelt, Patric KTH Royal Inst Technol Ctr Autonomous Syst SE-10044 Stockholm Sweden

In this letter, we proposed a new deep learning based dense monocular simultaneous localization and mapping (SLAM) method. Compared to existing methods, the proposed framework constructs a dense three-dimensional (3-D) model via a sparse to dense mapping using learned surface normals. With single view learned depth estimation as prior for monocular visual odometry, we obtain both accurate positioning and high-quality depth reconstruction. The depth and normal are predicted by a single network trained in a tightly coupled manner. Experimental results show that our method significantly improves the performance of visual tracking and depth prediction in comparison to the state-of-the-art in deep monocular dense SLAM.

关键词： Visual-based navigation SLAM deep learning in robotics and automation

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