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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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Fully Convolutional Networks With Sequential Information for Robust Crop and Weed Detection in Precision Farming

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IEEE robotics AND automation LETTERS 2018年第4期3卷 2870-2877页

作者： Lottes, Philipp Behley, Jens Milioto, Andres Stachniss, Cyrill Univ Bonn Inst Geodesy & Geoinformat D-53113 Bonn Germany

Reducing the use of agrochemicals is an important component toward sustainable agriculture. Robots that can perform targeted weed control offer the potential to contribute to this goal, for example, through specialized weeding actions such as selective spraying or mechanical weed removal. A prerequisite of such systems is a reliable and robust plant classification system that is able to distinguish crop and weed in the field. A major challenge in this context is the fact that different fields show a large variability. Thus, classification systems have to robustly cope with substantial environmental changes with respect to weed pressure and weed types, growth stages of the crop, visual appearance, and soil conditions. In this letter, we propose a novel crop-weed classification system that relies on a fully convolutional network with an encoder-decoder structure and incorporates spatial information by considering image sequences. Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i.e., a semantic segmentation. We provide a thorough experimental evaluation, which shows that our system generalizes well to previously unseen fields under varying environmental conditions-a key capability to actually use such systems in precision framing. We provide comparisons to other state-of-the-art approaches and show that our system substantially improves the accuracy of crop-weed classification without requiring a retraining of the model.

关键词： deep learning in robotics and automation robotics in agriculture and forestry

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learning to Segment Generic Handheld Objects Using Class-Agnostic deep Comparison and Segmentation Network

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3844-3851页

作者： Chaudhary, Krishneel Wada, Kentaro Chen, Xiangyu Kimura, Kohei Okada, Kei Inaba, Masayuki Univ Tokyo Grad Sch Informat Sci & Technol JSK Lab Tokyo 1138656 Japan

learning unknown objects in the environment is important for detection and manipulation tasks. Prior to learning the unknown objects the ground-truth labels have to be provided. The data annotation or labeling can be achieved in a number of ways but the most widely used method is still manual annotation. Although manual annotation has shown superior performance, it limits robots' capabilities to known object instances and is also a time consuming task. This letter considers the aforementioned limitations and presents a method that allows robots to autonomously annotate objects from observations of human-object interactions. Specifically, we present a novel method that segments handheld objects in real-time using the class-agnostic deep comparison and segmentation network. The inputs to the network are the RGB-D data of known object template and a search space, and it outputs a pixel-wise label of the object and an objectness score. The score indicates the likelihood that the same object is present in both the inputs. The object template is manually initialized in the first frame and thereafter, the object is segmented and the template is updated online. The template is strategically updated using the likelihood score. The segmented object regions are accumulated as pseudo-ground-truth labels, which are used for object learning. The approach efficiently handles both rigid and highly deformable objects.

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

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Sequence-to-Sequence Model for Trajectory Planning of Nonprehensile Manipulation Including Contact Model

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3606-3613页

作者： Kutsuzawa, Kyo Sakaino, Sho Tsuji, Toshiaki Saitama Univ Grad Sch Sci & Engn Saitama 3388570 Japan Saitama Univ JST PRESTO Saitama 3388570 Japan

Nonprehensile manipulation is necessary for robots to operate in humans' daily lives. As nonprehensile manipulation should satisfy both kinematics and dynamics requirements simultaneously, it is difficult to manipulate objects along given paths. Previous studies have considered the problems with sequence-to-sequence models, which are neural networks for time-series conversion. However, they did not consider nonlinear contact models, such as friction models. When we train the seq2seq models using end-to-end backpropagation, training losses vanish owing to static friction. In this letter, we realize sequence-to-sequence models for trajectory planning of nonprehensile manipulation including contact models between the robots and target objects. This letter proposes a training curriculum that commences training without contact models to bring the seq2seq models outside of the gradient-vanishing zone. This letter discusses sliding manipulation, which includes a friction model between objects and tools, such as frying pans fixed onto the robots. We validated the proposed curriculum through a simulation. In addition, we observed that the trained seq2seq models could handle parameter fluctuations that did not exist during training.

关键词： deep learning in robotics and automation motion and path planning nonprehensile manipulation sequence-to-sequence model

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Detect Globally, Label Locally: learning Accurate 6-DOF Object Pose Estimation by Joint Segmentation and Coordinate Regression

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3960-3967页

作者： Nigam, Apurv Penate-Sanchez, Adrian Agapito, Lourdes UCL Dept Comp Sci London NW1 9HZ England ANI Technol Pvt Ltd Dehra Dun 248006 India Univ Oxford Oxford Robot Inst Oxford OX2 6NN England

Coordinate regression has established itself as one of the most successful current trends in model-based 6 degree of freedom (6-DOF) object pace estimation from a single image. The underlying idea is to train a system that can regress the three-dimensional coordinates of an object, given an input RGB or RGB-D image and known object geometry, followed by a robust procedure such as RANSAC to optimize the object pose. These coordinate regression based approaches exhibit state-of-the-art performance by using pixel-level cues to model the probability distribution of object parts within the image. However, they fail to capture global information at the object level to learn accurate foreground/background segmentation. In this letter, we show that combining global features for object segmentation and local features for coordinate regression results in pixel-accurate object boundary detections and consequently a substantial reduction in outliers and an increase in overall performance. We propose a deep architecture with an instance-level object segmentation network that exploits global image information for object/background segmentation and a pixel-level classification network for coordinate regression based on local features. We evaluate our approach on the standard ground-truth 6-DOF pose estimation benchmarks and show that our joint approach to accurate object segmentation and coordinate regression results in the state-of-the-art performance on both RGB and RGB-D 6-DOF pose estimation.

关键词： Object detection segmentation and categorization deep learning in robotics and automation

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Super-High-Purity Seed Sorter Using Low-Latency Image-Recognition Based on deep learning

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3035-3042页

作者： Heo, Young Jin Kim, Se Jin Kim, Dayeon Lee, Keondo Chung, Wan Kyun Pohang Univ Sci & Technol Dept Mech Engn Pohang Si 37673 South Korea

Most commercial optical sorting systems are designed to achieve high throughput, so they use a naive low-latency image processing for object identification. These naive low-latency algorithms have difficulty in accurately identifying objects with various shapes, textures, sizes, and colors, so the purity of sorted objects is degraded. Current deep learning technology enables robust image detection and classification, but its inference latency requires several milliseconds;thus, deep learning cannot be directly applied to such real-time high throughput applications. We therefore developed a super-high purity seed sorting system that uses a low-latency image-recognition based on a deep neural network and removes the seeds of noxious weeds from mixed seed product at high throughput with accuracy. The proposed system partitions the detection task into localization and classification, and applies batch inference only once strategy;it achieved 500-fps throughput image-recognition including detection and tracking. Based on the classified and tracked results, air ejectors expel the unwanted seeds. This proposed system eliminates almost the whole weeds with small loss of desired seeds, and is superior to current commercial optical sorting systems.

关键词： Agricultural automation deep learning in robotics and automation computer vision for automation

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"Elbows Out"-Predictive Tracking of Partially Occluded Pose for Robot-Assisted Dressing

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3598-3605页

作者： Chance, Greg Jevtic, Aleksandar Caleb-Solly, Praminda Alenya, Guillem Torras, Carme Dogramadzi, Sanja Univ West England Bristol Robot Lab Bristol BS16 1QY Avon England UPC CSIC Inst Robot & Informat Ind Barcelona 08028 Spain

Robots that can assist in the activities of daily living, such as dressing, may support older adults, addressing the needs of an aging population in the face of a growing shortage of care professionals. Using depth cameras during robot-assisted dressing can lead to occlusions and loss of user tracking, which may result in unsafe trajectory planning or prevent the planning task proceeding altogether. For the dressing task of putting on a jacket, which is addressed in this letter, tracking of the arm is lost when the user's hand enters the jacket, which may lead to unsafe situations for the user and a poor interaction experience. Using motion tracking data, free from occlusions, gathered from a human human interaction study on an assisted dressing task, recurrent neural network models were built to predict the elbow position of a single arm based on other features of the user pose. The best features for predicting the elbow position were explored by using regression trees indicating the hips and shoulder as possible predictors. Engineered features were also created based on observations of real dressing scenarios and their effectiveness explored. Comparison between position and orientation-based datasets was also included in this study. A 12-fold cross-validation was performed for each feature set and repeated 20 times to improve statistical power. Using position-based data, the elbow position could be predicted with a 4.1 cm error but adding engineered features reduced the error to 2.4 cm. Adding orientation information to the data did not improve the accuracy and aggregating univariate response models failed to make significant improvements. The model was evaluated on Kinect data for a robot dressing task and although not without issues, demonstrates potential for this application. Although this has been demonstrated for jacket dressing, the technique could be applied to a number of different situations during occluded tracking.

关键词： Physical human-robot interaction human detection and tracking deep learning in robotics and automation robot safety

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Use of deep learning for Characterization of Microfluidic Soft Sensors

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IEEE robotics AND automation LETTERS 2018年第2期3卷 873-880页

作者： Han, Seunghyun Kim, Taekyoung Kim, Dooyoung Park, Yong-Lae Jo, Sungho Korea Adv Inst Sci & Technol Sch Comp Daejeon 305701 South Korea Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Inst Adv Machines & Design Seoul 08826 South Korea

Soft sensors made of highly deformable materials are one of the enabling technologies to various soft robotic systems, such as soft mobile robots, soft wearable robots, and soft grippers. However, major drawbacks of soft sensors compared with traditional sensors are their nonlinearity and hysteresis in response, which are common especially in microfluidic soft sensors. In this research, we propose to address the above issues of soft sensors by taking advantage of deep learning. We implemented a hierarchical recurrent sensing network, a type of recurrent neural network model, to the calibration of soft sensors for estimating the magnitude and the location of a contact pressure simultaneously. The proposed approach in this letter is not only able to model the nonlinear characteristic with hysteresis of the pressure response, but also find the location of the pressure.

关键词： Soft material robotics deep learning in robotics and automation force and tactile sensing

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Inference Over Distribution of Posterior Class Probabilities for Reliable Bayesian Classification and Object-Level Perception

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IEEE robotics AND automation LETTERS 2018年第4期3卷 4329-4336页

作者： Tchuiev, Vladimir Indelman, Vadim Technion Israel Inst Technol Dept Aerosp Engn IL-32000 Haifa Israel

State of the art Bayesian classification approaches typically maintain a posterior distribution over possible classes given available sensor observations (images). Yet, while these approaches fuse all classifier outputs thus far, they do not provide any indication regarding how reliable the posterior classification is, thus limiting its functionality in terms of autonomous systems and robotics. On the other hand, current deep learning based classifiers provide an uncertainty measure, thereby quantifying model uncertainty. However, they do so on a single frame basis and do not consider a sequential framework. In this letter, we develop a novel approach that infers a distribution over posterior class probabilities, while accounting for model uncertainty. This distribution enables reasoning about uncertainty in the posterior classification and, therefore, is of prime importance for robust classification, object-level perception in uncertain and ambiguous scenarios, and for safe autonomy in general. The distribution of the posterior class probability has no known analytical solution;thus, we propose to approximate this distribution via sampling. We evaluate our approach in simulation and using real images fed into a convolutional neural network classifier.

关键词： deep learning in robotics and automation recognition

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Paired Recurrent Autoencoders for Bidirectional Translation Between Robot Actions and Linguistic Descriptions

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3441-3448页

作者： Yamada, Tatsuro Matsunaga, Hiroyuki Ogata, Tetsuya Waseda Univ Dept Intermedia Art & Sci Tokyo 1698555 Japan

We propose a novel deep learning framework for bidirectional translation between robot actions and their linguistic descriptions. Our model consists of two recurrent autoencoders (RAEs). One RAE learns to encode action sequences as fixed-dimensional vectors in a way that allows the sequences to be reproduced from the vectors by its decoder. The other RAE learns to encode descriptions in a similar way. In the learning process, in addition to reproduction losses, we create another loss function whereby the representations of an action and its corresponding description approach each other in the latent vector space. Across the shared representation, the trained model can produce a linguistic description given a robot action. The model is also able to generate an appropriate action by receiving a linguistic instruction, conditioned on the current visual input. Visualization of the latent representations shows that the robot actions are embedded in a semantically compositional way in the vector space by being learned jointly with descriptions.

关键词： deep learning in robotics and automation Al-based methods neurorobotics

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