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GFPNet: A deep Network for learning Shape Completion in Generic Fitted Primitives

IEEE robotics AND automation LETTERS 2020年第3期5卷 4493-4500页

作者： Cocias, Tiberiu Razvant, Alexandru Grigorescu, Sorin Transilvania Univ Brasov Elektrobit Automot & Robot Vis & Control Lab Brasov 500036 Romania

In this letter, we propose an object reconstruction apparatus that uses the so-called Generic Primitives (GP) to complete shapes. A GP is a 3D point cloud depicting a generalized shape of a class of objects. To reconstruct the objects in a scene we first fit a GP onto each occluded object to obtain an initial raw structure. Secondly, we use a model-based deformation technique to fold the surface of the GP over the occluded object. The deformation model is encoded within the layers of a deep Neural Network (DNN), coined GFPNet. The objective of the network is to transfer the particularities of the object from the scene to the raw volume represented by the GP. We show that GFPNet competes with state of the art shape completion methods by providing performance results on the ModelNet and KITTI benchmarking datasets.

关键词： Shape completion surface modeling deep learning in robotics and automation GFPNet

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Low to High Dimensional Modality Hallucination Using Aggregated Fields of View

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1983-1990页

作者： Gunasekar, Kausic Qiu, Qiang Yang, Yezhou Arizona State Univ Tempe AZ 85281 USA Duke Univ Durham NC 27708 USA

Real-world robotics systems deal with data from a multitude of modalities, especially for tasks such as navigation and recognition. The performance of those systems can drastically degrade when one or more modalities become inaccessible, due to factors such as sensors' malfunctions or adverse environments. Here, we argue modality hallucination as one effective way to ensure consistent modality availability and thereby reduce unfavorable consequences. While hallucinating data from a modality with richer information, e.g., RGB to depth, has been researched extensively, we investigate the more challenging low-to-high modality hallucination with interesting use cases in robotics and autonomous systems. We present a novel hallucination architecture that aggregates information from multiple fields of view of the local neighborhood to recover the lost information from the extant modality. The process is implemented by capturing a non-linear mapping between the data modalities and the learned mapping is used to aid the extant modality to mitigate the risk posed to the system in the adverse scenarios which involve modality loss. We also conduct extensive classification and segmentation experiments on UWRGBD and NYUD datasets and demonstrate that hallucination allays the negative effects of the modality loss. Implementation and models: https://***/kausic94/Hallucination.

关键词： deep learning in robotics and automation robot safety modality hallucination computer vision for other robotic applications

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CorsNet: 3D Point Cloud Registration by deep Neural Network

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IEEE robotics AND automation LETTERS 2020年第3期5卷 3960-3966页

作者： Kurobe, Akiyoshi Sekikawa, Yusuke Ishikawa, Kohta Saito, Hideo Keio Univ Dept Sci & Technol Yokohama Kanagawa 2238522 Japan Denso IT Lab Inc Tokyo 1500002 Japan

Point cloud registration is a key problem for robotics and computer vision communities. This represents estimating a rigid transform which aligns one point cloud to another. Iterative closest point (ICP) is a well-known classical method for this problem, yet it generally achieves high alignment only when the source and template point cloud are mostly pre-aligned. If each point cloud is far away or contains a repeating structure, the registration often fails because of being fallen into a local minimum. Recently, inspired by PointNet, several deep learning-based methods have been developed. PointNetLK is a representative approach, which directly optimizes the distance of aggregated features using gradient method by Jacobian. In this paper, we propose a point cloud registration system based on deep learning: CorsNet. Since CorsNet concatenates the local features with the global features and regresses correspondences between point clouds, not directly pose or aggregated features, more useful information is integrated than the conventional approaches. For comparison, we also developed a novel deep learning approach (DirectNet) that directly regresses the pose between point clouds. Through our experiments, we show that CorsNet achieves higher accuracy than not only the classic ICP method, but also the recently proposed learning-based proposal PointNetLK and DirectNet, including on seen and unseen categories.

关键词： Three-dimensional displays Feature extraction Transforms Computer architecture Estimation deep learning Network architecture Computer vision for other robotic applications deep learning in robotics and automation perception for grasping and manipulation

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Localization of Inspection Device Along Belt Conveyors With Multiple Branches Using deep Neural Networks

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2921-2928页

作者： Yasutomi, Andre Yuji Enoki, Hideo Hitachi Ltd Robot Res Dept Ctr Technol Innovat Mech Engn Res & Dev Grp Hitachinaka Ibaraki 3120034 Japan

Regular inspections of belt conveyors are required to prevent the damage of transported objects. Nevertheless, inspections can be troublesome for belt conveyors composed of a plurality of belt lines with multiple branches. To improve the inspection process, an inspection device composed of an inertial measurement unit (IMU) inside a transported object was introduced in our previous study jointly with an algorithm to detect anomalies in the joints of the belt lines. When belt conveying this device for inspection, however, it is required to not only detect the anomaly but also know its position. This study presents a novel method to estimate the position of the inspection device along the belt conveyor using a deep neural network (DNN). The DNN uses the IMU data to detect seven types of features (passage through five types of joints, device stoppage and regular transport), which, when matched to data on a belt conveyor position database, can correctly be translated into positions. Additionally, the proposed method enables the detection of changes of routes along the belt conveyor that occur when a belt line branches into two output streams. To enhance the DNN feature detections, two original algorithms for DNN output post-processing are also introduced. Experiments with a complex belt conveyor demonstrate this method can successfully detect the position of the inspected device more accurately and more cost-effectively than conventional methods.

关键词： Belts Inspection Prediction algorithms Feature extraction Estimation Databases Neural networks Localization deep learning in robotics and automation maintenance and supply chains manufacturing

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learning to Assemble: Estimating 6D Poses for Robotic Object-Object Manipulation

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1159-1166页

作者： Stevsic, Stefan Christen, Sammy Hilliges, Otmar Swiss Fed Inst Technol Dept Comp Sci Adv Interact Technol Lab CH-8092 Zurich Switzerland

In this letter we propose a robotic vision task with the goal of enabling robots to execute complex assembly tasks in unstructured environments using a camera as the primary sensing device. We formulate the task as an instance of 6D pose estimation of template geometries, to which manipulation objects should be connected. In contrast to the standard 6D pose estimation task, this requires reasoning about local geometry that is surrounded by arbitrary context, such as a power outlet embedded into a wall. We propose a deep learning based approach to solve this task alongside a novel dataset that will enable future work in this direction and can serve as a benchmark. We experimentally show that state-of-the-art 6D pose estimation methods alone are not sufficient to solve the task but that our training procedure significantly improves the performance of deep learning techniques in this context.

关键词： deep learning in robotics and automation perception for grasping and manipulation computer vision for automation

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A Multimodal Target-Source Classifier With Attention Branches to Understand Ambiguous Instructions for Fetching Daily Objects

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IEEE robotics AND automation LETTERS 2020年第2期5卷 532-539页

作者： Magassouba, Aly Sugiura, Komei Kawai, Hisashi Natl Inst Informat & Commun Technol 3-5 Hikaridai Kyoto 6190289 Japan

In this study, we focus on multimodal language understanding for fetching instructions in the domestic service robots context. This task consists of predicting a target object, as instructed by the user, given an image and an unstructured sentence, such as "Bring me the yellow box (from the wooden cabinet)." This is challenging because of the ambiguity of natural language, i.e., the relevant information may be missing or there might be several candidates. To solve such a task, we propose the multimodal target-source classifier model with attention branches (MTCM-AB), which is an extension of the MTCM. Our methodology uses the attention branch network (ABN) to develop a multimodal attention mechanism based on linguistic and visual inputs. Experimental validation using a standard dataset showed that the MTCM-AB outperformed both state-of-the-art methods and the MTCM. In particular, the MTCM-AB accuracy was 90.1 on average while human performance was 90.3 on the PFN-PIC dataset.

关键词： deep learning in robotics and automation domestic robots

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Robot Navigation in Crowds by Graph Convolutional Networks With Attention Learned From Human Gaze

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2754-2761页

作者： Chen, Yuying Liu, Congcong Shi, Bertram E. Liu, Ming Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task. Previous work has shown the power of deep reinforcement learning frameworks to train efficient policies. However, their performance deteriorates when the crowd size grows. We suggest that this can be addressed by enabling the network to identify and pay attention to the humans in the crowd that are most critical to navigation. We propose a novel network utilizing a graph representation to learn the policy. We first train a graph convolutional network based on human gaze data that accurately predicts human attention to different agents in the crowd as they perform a navigation task based on a top down view of the environment. We incorporate the learned attention into a graph-based reinforcement learning architecture. The proposed attention mechanism enables the assignment of meaningful weightings to the neighbors of the robot, and has the additional benefit of interpretability. Experiments on real-world dense pedestrian datasets with various crowd sizes demonstrate that our model outperforms state-of-art methods, increasing task completion rate by 18.4% and decreasing navigation time by 16.4%.

关键词： Autonomous vehicle navigation deep learning in robotics and automation social human-robot interaction

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Motor Synergy Development in High-Performing deep Reinforcement learning Algorithms

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1271-1278页

作者： Chai, Jiazheng Hayashibe, Mitsuhiro Tohoku Univ Grad Sch Engn Dept Robot Neurorobot Lab Sendai Miyagi 9808579 Japan

As human motor learning is hypothesized to use the motor synergy concept, we investigate if this concept could also be observed in deep reinforcement learning for robotics. From this point of view, we carried out a joint-space synergy analysis on multi-joint running agents in simulated environments trained using two state-of-the-art deep reinforcement learning algorithms. Although a synergy constraint has never been encoded into the reward function, the synergy emergence phenomenon could be observed statistically in the learning agent. To our knowledge, this is the first attempt to quantify the synergy development in detail and evaluate its emergence process during deep learning motor control tasks. We then demonstrate that there is a correlation between our synergy-related metrics and the performance and energy efficiency of a trained agent. Interestingly, the proposed synergy-related metrics reflected a better learning capability of SAC over TD3. It suggests that these metrics could be additional new indices to evaluate deep reinforcement learning algorithms for motor learning. It also indicates that synergy is required for multi-joints robots to move energy-efficiently.

关键词： deep learning in robotics and automation performance evaluation and benchmarking motor synergy

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learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1032-1038页

作者： Marchal, Nicolas Moraldo, Charlotte Blum, Hermann Siegwart, Roland Cadena, Cesar Gawel, Abel Swiss Fed Inst Technol Autonomous Syst Lab CH-8092 Zurich Switzerland

deep learning has enabled remarkable advances in scene understanding, particularly in semantic segmentation tasks. Yet, current state of the art approaches are limited to a closed set of classes, and fail when facing novel elements, also known as out of distribution (OoD) data. This is a problem as autonomous agents will inevitably come across a wide range of objects, all of which cannot be included during training. We propose a novel method to distinguish any object (foreground) from empty building structure (background) in indoor environments. We use normalizing flow to estimate the probability distribution of high-dimensional background descriptors. Foreground objects are therefore detected as areas in an image for which the descriptors are unlikely given the background distribution. As our method does not explicitly learn the representation of individual objects, its performance generalizes well outside of the training examples. Our model results in an innovative solution to reliably segment foreground from background in indoor scenes, which opens the way to a safer deployment of robots in human environments.

关键词： deep learning in robotics and automation Semantic Scene Understanding Visual learning

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Real-Time Soft Body 3D Proprioception via deep Vision-Based Sensing

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3382-3389页

作者： Wang, Ruoyu Wang, Shiheng Du, Songyu Xiao, Erdong Yuan, Wenzhen Feng, Chen NYU Tandon Sch Engn Brooklyn NY 11201 USA Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

Soft bodies made from flexible and deformable materials are popular in many robotics applications, but their proprioceptive sensing has been a long-standing challenge. In other words, there has hardly been a method to measure and model the high-dimensional 3D shapes of soft bodies with internal sensors. We propose a framework to measure the high-resolution 3D shapes of soft bodies in real-time with embedded cameras. The cameras capture visual patterns inside a soft body, and a convolutional neural network (CNN) produces a latent code representing the deformation state, which can then be used to reconstruct the body's 3D shape using another neural network. We test the framework on various soft bodies, such as a Baymax-shaped toy, a latex balloon, and some soft robot fingers, and achieve real-time computation (<= 2.5 ms/frame) for robust shape estimation with high Precision (<= 1% relative error) and high resolution. We believe the method could be applied to soft robotics and human-robot interaction for proprioceptive shape sensing. Our code is available at: https://***/deepSoRo.

关键词： Modeling control and learning for soft robots deep learning in robotics and automation 3D deep learning

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