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IEEE robotics AND automation LETTERS 2020年第2期5卷 1742-1749页

作者： de Geus, Daan Meletis, Panagiotis Dubbelman, Gijs Eindhoven Univ Technol Dept Elect Engn SPS VCA Grp Mobile Percept Syst Res lab NL-5600 MB Eindhoven Netherlands

In this work, we present an end-to-end network for fast panoptic segmentation. This network, called Fast Panoptic Segmentation Network (FPSNet), does not require computationally costly instance mask predictions or rule-based merging operations. This is achieved by casting the panoptic task into a custom dense pixel-wise classification task, which assigns a class label or an instance id to each pixel. We evaluate FPSNet on the Cityscapes and Pascal VOC datasets, and find that FPSNet is faster than existing panoptic segmentation methods, while achieving better or similar panoptic segmentation performance. On the Cityscapes validation set, we achieve a Panoptic Quality score of 55.1%, at prediction times of 114 milliseconds for images with a resolution of 1024 x 2048 pixels. For lower resolutions of the Cityscapes dataset and for the Pascal VOC dataset, FPSNet achieves prediction times as low as 45 and 28 milliseconds, respectively.

关键词： Semantic scene understanding object detection segmentation and categorization deep learning in robotics and automation

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learning Transformable and Plannable se(3) Features for Scene Imitation of a Mobile Service Robot

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1664-1671页

作者： Park, J. Hyeon Kim, Jigang Jang, Youngseok Jang, Inkyu Kim, H. Jin Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Automat & Syst Res Inst ASRI Seoul 08826 South Korea

deep neural networks facilitate visuosensory inputs for robotic systems. However, the features encoded in a network without specific constraints have little physical meaning. In this research, we add constraints on the network so that the trained features are forced to represent the actual twist coordinates of interactive objects in a scene. The trained coordinates describe 6d-pose of the objects, and transformation is applied to change the coordinate system. This algorithm is developed for a mobile service robot that imitates an object-oriented task by watching human demonstrations. As the robot has mobility, the video demonstrations are collected from different viewpoints. Our feature trajectories of twist coordinates are synthesized in the global coordinate after transformation is applied according to robot localization. Then, the trajectories are trained as probabilistic model and imitated by the robot with geometric dynamics of . Our main contribution is to develop a trainable robot with visually demonstrated human performances. Additionally, our algorithmic contribution is to design a scene interpretation network where constraints are incorporated to estimate 6d-pose of objects.

关键词： deep learning in robotics and automation visual learning learning from demonstration mobile manipulation

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Robot-Assisted Training in Laparoscopy Using deep Reinforcement learning

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IEEE robotics AND automation LETTERS 2019年第2期4卷 485-492页

作者： Tan, Xiaoyu Chng, Chin-Boon Su, Ye Lim, Kah-Bin Chui, Chee-Kong Natl Univ Singapore Dept Mech Engn Singapore 119077 Singapore

Minimally invasive surgery (MIS) is increasingly becoming a vital method of reducing surgical trauma and significantly improving postoperative recovery. However, skillful handling of surgical instruments used in MIS, especially for laparoscopy, requires a long period of training and depends highly on the experience of surgeons. This letter presents a new robot-assisted surgical training system which is designed to improve the practical skills of surgeons through intrapractice feedback and demonstration from both human experts and reinforcement learning (RL) agents. This system utilizes proximal policy optimization to learn the control policy in simulation. Subsequently, a generative adversarial imitation learning agent is trained based on both expert demonstrations and learned policies in simulation. This agent then generates demonstration policies on the robot-assisted device for trainees and produces feedback scores during practice. To further acquire surgical tools coordinates and encourage self-oriented practice, a mask region-based convolution neural network is trained to perform the semantic segmentation of surgical tools and targets. To the best of our knowledge, this system is the first robot-assisted laparoscopy training system which utilizes actual surgical tools and leverages deep reinforcement learning to provide demonstration training from both human expert perspectives and RL criterion.

关键词： Surgical robotics laparoscopy learning from demonstration deep learning in robotics and automation AI-based methods

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Denoising IMU Gyroscopes With deep learning for Open-Loop Attitude Estimation

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IEEE robotics AND automation LETTERS 2020年第3期5卷 4796-4803页

作者： Brossard, Martin Bonnabel, Silvere Barrau, Axel PSL Res Univ MINES ParisTech Ctr Robot F-75006 Paris France Univ New Caledonia ISEA Noumea 98800 New Caledonia Safran Tech Grp Safran F-78117 Magny Les Hameaux Chateaufort France

This article proposes a learning method for denoising gyroscopes of Inertial Measurement Units (IMUs) using ground truth data, and estimating in real time the orientation (attitude) of a robot in dead reckoning. The obtained algorithm outperforms the state-of-the-art on the (unseen) test sequences. The obtained performances are achieved, thanks to a well-chosen model, a proper lass function for orientation increments, and through the identification of key points when training with high-frequency inertial data. Our approach builds upon a neural network based on dilated convolutions, without requiring any recurrent neural network. We demonstrate how efficient our strategy is for 3D attitude estimation on the EuRoC and TUM-VI datasets. Interestingly, we observe our dead reckoning algorithm manages to beat top-ranked visual-inertial odometry systems in terms of attitude estimation although it does not use vision sensors. We believe this article offers new perspectives for visual-inertial localization and constitutes a step toward more efficient learning methods involving IMUs. Our open-source implementation is available at https : / /github. com/mbrossar/denoise - imu - gyro.

关键词： Localization deep learning in robotics and automation autonomous systems navigation

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Convolutional Autoencoder for Feature Extraction in Tactile Sensing

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IEEE robotics AND automation LETTERS 2019年第4期4卷 3671-3678页

作者： Polic, Marsela Krajacic, Ivona Lepora, Nathan Orsag, Matko Univ Zagreb Fac Elect Engn & Comp Lab Robot & Intelligent Control Syst Zagreb 10000 Croatia Univ Bristol Dept Engn Math Bristol BS8 1UB Avon England Univ Bristol Bristol Robot Lab Bristol BS8 1UB Avon England

A common approach in the field of tactile robotics is the development of a new perception algorithm for each new application of existing hardware solutions. In this letter, we present a method of dimensionality reduction of an optical-based tactile sensor image output using a convolutional neural network encoder structure. Instead of using various complex perception algorithms, and/ or manually choosing task-specific data features, this unsupervised feature extraction method allows simultaneous online deployment of multiple simple perception algorithms on a common set of black-box features. The method is validated on a set of benchmarking use cases. Contact object shape, edge position, orientation, and indentation depth are estimated using shallowneural networks and machine learning models. Furthermore, a contact force estimator is trained, affirming that the extracted features contain sufficient information on both spatial and mechanical characteristics of the manipulated object.

关键词： Force and tactile sensing deep learning in robotics and automation soft sensors and actuators perception for grasping and manipulation

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Real-Time Nonlinear Model Predictive Control of Robots Using a Graphics Processing Unit

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1468-1475页

作者： Hyatt, Phillip Killpack, Marc D. Brigham Young Univ Mech Engn Dept Provo UT 84602 USA

In past robotics applications, Model Predictive Control (MPC) has often been limited to linear models and relatively short time horizons. In recent years however, research in optimization, optimal control, and simulation has enabled some forms of nonlinear model predictive control which find locally optimal solutions. The limiting factor for applying nonlinear MPC for robotics remains the computation necessary to solve the optimization, especially for complex systems and for long time horizons. This letter presents a new solution method which addresses computational concerns related to nonlinear MPC called nonlinear Evolutionary MPC (NEMPC), and then we compare it to several existing methods. These comparisons include simulations on torque-limited robots performing a swing-up task and demonstrate that NEMPC is able to discover complex behaviors to accomplish the task. Comparisons with state-of-the-art nonlinear MPC algorithms show that NEMPC finds high quality control solutions very quickly using a global, instead of local, optimization method. Finally, an application in hardware (a 24 state pneumatically actuated continuum soft robot) demonstrates that this method is tractable for real-time control of high degree of freedom systems.

关键词： Optimization and optimal control control architectures and programming deep learning in robotics and automation model learning for control

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learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1143-1150页

作者： Amini, Alexander Gilitschenski, Igor Phillips, Jacob Moseyko, Julia Banerjee, Rohan Karaman, Sertac Rus, Daniela MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02142 USA MIT Lab Informat & Decis Syst Cambridge MA 02139 USA

In this work, we present a data-driven simulation and training engine capable of learning end-to-end autonomous vehicle control policies using only sparse rewards. By leveraging real, human-collected trajectories through an environment, we render novel training data that allows virtual agents to drive along a continuum of new local trajectories consistent with the road appearance and semantics, each with a different view of the scene. We demonstrate the ability of policies learned within our simulator to generalize to and navigate in previously unseen real-world roads, without access to any human control labels during training. Our results validate the learned policy onboard a full-scale autonomous vehicle, including in previously un-encountered scenarios, such as new roads and novel, complex, near-crash situations. Our methods are scalable, leverage reinforcement learning, and apply broadly to situations requiring effective perception and robust operation in the physical world.

关键词： deep learning in robotics and automation autonomous agents real world reinforcement learning data-driven simulation

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DroNet: learning to Fly by Driving

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IEEE robotics AND automation LETTERS 2018年第2期3卷 1088-1095页

作者： Loquercio, Antonio Maqueda, Ana I. del-Blanco, Carlos R. Scaramuzza, Davide Univ Zurich Robot & Percept Grp Dept Informat CH-8092 Zurich Switzerland Univ Zurich Dept Neuroinformat CH-8092 Zurich Switzerland ETH CH-8092 Zurich Switzerland Univ Politecn Madrid Grp Tratamiento Imagenes Informat Proc & Telecommun Ctr E-28040 Madrid Spain Univ Politecn Madrid ETSI Telecomunicac E-28040 Madrid Spain

Civilian drones are soon expected to be used in a wide variety of tasks, such as aerial surveillance, delivery, or monitoring of existing architectures. Nevertheless, their deployment in urban environments has so far been limited. Indeed, in unstructured and highly dynamic scenarios, drones face numerous challenges to navigate autonomously in a feasible and safe way. In contrast to traditional "map-localize-plan" methods, this letter explores a data-driven approach to cope with the above challenges. To accomplish this, we propose DroNet: a convolutional neural network that can safely drive a drone through the streets of a city. Designed as a fast eight-layers residual network, DroNet produces two outputs for each single input image: Asteering angle to keep the drone navigating while avoiding obstacles, and a collision probability to let the UAV recognize dangerous situations and promptly react to them. The challenge is however to collect enough data in an unstructured outdoor environment such as a city. Clearly, having an expert pilot providing training trajectories is not an option given the large amount of data required and, above all, the risk that it involves for other vehicles or pedestrians moving in the streets. Therefore, we propose to train a UAV from data collected by cars and bicycles, which, already integrated into the urban environment, would not endanger other vehicles and pedestrians. Although trained on city streets from the viewpoint of urban vehicles, the navigation policy learned by DroNet is highly generalizable. Indeed, it allows a UAV to successfully fly at relative high altitudes and even in indoor environments, such as parking lots and corridors. To share our findings with the robotics community, we publicly release all our datasets, code, and trained networks.

关键词： learning from demonstration deep learning in robotics and automation aerial systems: perception and autonomy

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Making Sense of Vision and Touch: learning Multimodal Representations for Contact-Rich Tasks

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IEEE TRANSACTIONS ON robotics 2020年第3期36卷 582-596页

作者： Lee, Michelle A. Zhu, Yuke Zachares, Peter Tan, Matthew Srinivasan, Krishnan Savarese, Silvio Fei-Fei, Li Garg, Animesh Bohg, Jeannette Stanford Univ Dept Comp Sci Stanford CA 94305 USA Nvidia Res Santa Clara CA 95051 USA Univ Toronto Dept Comp Sci Toronto ON M5S 2E4 Canada

Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is nontrivial to manually design a robot controller that combines these modalities, which have very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to train directly on real robots due to sample complexity. In this article, we use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. Evaluating our method on a peg insertion task, we show that it generalizes over varying geometries, configurations, and clearances, while being robust to external perturbations. We also systematically study different self-supervised learning objectives and representation learning architectures. Results are presented in simulation and on a physical robot.

关键词： Task analysis Haptic interfaces Visualization Robot sensing systems Solid modeling Reinforcement learning deep learning in robotics and automation perception for grasping and manipulation sensor fusion sensor-based control

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UniGrasp: learning a Unified Model to Grasp With Multifingered Robotic Hands

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2286-2293页

作者： Shao, Lin Ferreira, Fabio Jorda, Mikael Nambiar, Varun Luo, Jianlan Solowjow, Eugen Ojea, Juan Aparicio Khatib, Oussama Bohg, Jeannette Stanford Univ SAIL Stanford CA 94305 USA Karlsruhe Inst Technol Inst Anthropomat & Robot D-76131 Karlsruhe Germany Univ Calif Berkeley Dept ME Berkeley CA 94720 USA Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA Siemens Corp Technol Berkeley CA 94709 USA

To achieve a successful grasp, gripper attributes such as its geometry and kinematics play a role as important as the object geometry. The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand. We propose UniGrasp, an efficient data-driven grasp synthesis method that considers both the object geometry and gripper attributes as inputs. UniGrasp is based on a novel deep neural network architecture that selects sets of contact points from the input point cloud of the object. The proposed model is trained on a large dataset to produce contact points that are in force closure and reachable by the robot hand. By using contact points as output, we can transfer between a diverse set of multifingered robotic hands. Our model produces over 90% valid contact points in Top10 predictions in simulation and more than 90% successful grasps in real world experiments for various known two-fingered and three-fingered grippers. Our model also achieves 93%, 83% and 90% successful grasps in real world experiments for an unseen two-fingered gripper and two unseen multi-fingered anthropomorphic robotic hands.

关键词： deep learning in robotics and automation grasping multifingered hands

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