January 25, 2020

3248 words 16 mins read

Paper Group ANR 1656

OCTNet: Trajectory Generation in New Environments from Past Experiences. MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding. Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery. Simultaneous Contact, Gait and Motion Planning for Robust Multi- …

OCTNet: Trajectory Generation in New Environments from Past Experiences


Title	OCTNet: Trajectory Generation in New Environments from Past Experiences
Authors	Weiming Zhi, Tin Lai, Lionel Ott, Gilad Francis, Fabio Ramos
Abstract	Being able to safely operate for extended periods of time in dynamic environments is a critical capability for autonomous systems. This generally involves the prediction and understanding of motion patterns of dynamic entities, such as vehicles and people, in the surroundings. Many motion prediction methods in the literature can learn a function, mapping position and time to potential trajectories taken by people or other dynamic entities. However, these predictions depend only on previously observed trajectories, and do not explicitly take into consideration the environment. Trends of motion obtained in one environment are typically specific to that environment, and are not used to better predict motion in other environments. In this paper, we address the problem of generating likely motion dynamics conditioned on the environment, represented as an occupancy map. We introduce the Occupancy Conditional Trajectory Network (OCTNet) framework, capable of generalising the previously observed motion in known environments, to generate trajectories in new environments where no observations of motion has not been observed. OCTNet encodes trajectories as a fixed-sized vector of parameters and utilises neural networks to learn conditional distributions over parameters. We empirically demonstrate our method’s ability to generate complex multi-modal trajectory patterns in different environments.
Tasks	motion prediction
Published	2019-09-25
URL	https://arxiv.org/abs/1909.11337v1
PDF	https://arxiv.org/pdf/1909.11337v1.pdf
PWC	https://paperswithcode.com/paper/octnet-trajectory-generation-in-new
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MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding


Title	MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding
Authors	Wenbo Zhang, Osbert Bastani, Vijay Kumar
Abstract	Reinforcement learning is a promising approach to learning control policies for performing complex multi-agent robotics tasks. However, a policy learned in simulation often fails to guarantee even simple safety properties such as obstacle avoidance. To ensure safety, we propose multi-agent model predictive shielding (MAMPS), an algorithm that provably guarantees safety for an arbitrary learned policy. In particular, it operates by using the learned policy as often as possible, but instead uses a backup policy in cases where it cannot guarantee the safety of the learned policy. Using a multi-agent simulation environment, we show how MAMPS can achieve good performance while ensuring safety.
Tasks	Multi-agent Reinforcement Learning
Published	2019-10-25
URL	https://arxiv.org/abs/1910.12639v2
PDF	https://arxiv.org/pdf/1910.12639v2.pdf
PWC	https://paperswithcode.com/paper/mamps-safe-multi-agent-reinforcement-learning
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Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery


Title	Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery
Authors	Yona Falinie A. Gaus, Neelanjan Bhowmik, Samet Akçay, Paolo M. Guillen-Garcia, Jack W. Barker, Toby P. Breckon
Abstract	X-ray baggage security screening is widely used to maintain aviation and transport security. Of particular interest is the focus on automated security X-ray analysis for particular classes of object such as electronics, electrical items, and liquids. However, manual inspection of such items is challenging when dealing with potentially anomalous items. Here we present a dual convolutional neural network (CNN) architecture for automatic anomaly detection within complex security X-ray imagery. We leverage recent advances in region-based (R-CNN), mask-based CNN (Mask R-CNN) and detection architectures such as RetinaNet to provide object localisation variants for specific object classes of interest. Subsequently, leveraging a range of established CNN object and fine-grained category classification approaches we formulate within object anomaly detection as a two-class problem (anomalous or benign). While the best performing object localisation method is able to perform with 97.9% mean average precision (mAP) over a six-class X-ray object detection problem, subsequent two-class anomaly/benign classification is able to achieve 66% performance for within object anomaly detection. Overall, this performance illustrates both the challenge and promise of object-wise anomaly detection within the context of cluttered X-ray security imagery.
Tasks	Anomaly Detection, Object Detection
Published	2019-04-10
URL	http://arxiv.org/abs/1904.05304v1
PDF	http://arxiv.org/pdf/1904.05304v1.pdf
PWC	https://paperswithcode.com/paper/evaluation-of-a-dual-convolutional-neural
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Simultaneous Contact, Gait and Motion Planning for Robust Multi-Legged Locomotion via Mixed-Integer Convex Optimization


Title	Simultaneous Contact, Gait and Motion Planning for Robust Multi-Legged Locomotion via Mixed-Integer Convex Optimization
Authors	Bernardo Aceituno-Cabezas, Carlos Mastalli, Hongkai Dai, Michele Focchi, Andreea Radulescu, Darwin G. Caldwell, Jose Cappelletto, Juan C. Grieco, Gerardo Fernandez-Lopez, Claudio Semini
Abstract	Traditional motion planning approaches for multi-legged locomotion divide the problem into several stages, such as contact search and trajectory generation. However, reasoning about contacts and motions simultaneously is crucial for the generation of complex whole-body behaviors. Currently, coupling theses problems has required either the assumption of a fixed gait sequence and flat terrain condition, or non-convex optimization with intractable computation time. In this paper, we propose a mixed-integer convex formulation to plan simultaneously contact locations, gait transitions and motion, in a computationally efficient fashion. In contrast to previous works, our approach is not limited to flat terrain nor to a pre-specified gait sequence. Instead, we incorporate the friction cone stability margin, approximate the robot’s torque limits, and plan the gait using mixed-integer convex constraints. We experimentally validated our approach on the HyQ robot by traversing different challenging terrains, where non-convexity and flat terrain assumptions might lead to sub-optimal or unstable plans. Our method increases the motion generality while keeping a low computation time.
Tasks	Motion Planning
Published	2019-04-09
URL	http://arxiv.org/abs/1904.04595v1
PDF	http://arxiv.org/pdf/1904.04595v1.pdf
PWC	https://paperswithcode.com/paper/simultaneous-contact-gait-and-motion-planning
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Planning under non-rational perception of uncertain spatial costs


Title	Planning under non-rational perception of uncertain spatial costs
Authors	Aamodh Suresh, Sonia Martinez
Abstract	This work investigates the design of motion planning strategies that can incorporate non-rational perception of risks associated with uncertain spatial costs. Our proposed method employs the Cumulative Prospect Theory (CPT) model to generate a perceived risk function across a given environment, which is scalable to high dimensional space. Using this, CPT-like perceived risks and path-length metrics are combined to define a cost function that is compliant with the requirements of asymptotic optimality of sampling-based motion planners (RRT*). The modeling capabilities of CPT are demonstrated in simulation by producing a rich set of meaningful paths, capturing a range of different risk perceptions in a custom environment. Furthermore, using a simultaneous perturbation stochastic approximation (SPSA) method, we investigate the capacity of these CPT-based risk-perception planners to approximate arbitrary paths drawn in the environment. We compare this adaptability with Conditional Value at Risk (CVaR), another popular risk perception model. Our simulations show that CPT is richer and able to capture a larger class of paths as compared to CVaR and expected risk in our setting.
Tasks	Motion Planning
Published	2019-04-05
URL	https://arxiv.org/abs/1904.02851v2
PDF	https://arxiv.org/pdf/1904.02851v2.pdf
PWC	https://paperswithcode.com/paper/planning-under-risk-and-uncertainty-based-on
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Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning


Title	Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning
Authors	Jiachen Li, Hengbo Ma, Masayoshi Tomizuka
Abstract	In order to enable high-quality decision making and motion planning of intelligent systems such as robotics and autonomous vehicles, accurate probabilistic predictions for surrounding interactive objects is a crucial prerequisite. Although many research studies have been devoted to making predictions on a single entity, it remains an open challenge to forecast future behaviors for multiple interactive agents simultaneously. In this work, we take advantage of the Generative Adversarial Network (GAN) due to its capability of distribution learning and propose a generic multi-agent probabilistic prediction and tracking framework which takes the interactions among multiple entities into account, in which all the entities are treated as a whole. However, since GAN is very hard to train, we make an empirical research and present the relationship between training performance and hyperparameter values with a numerical case study. The results imply that the proposed model can capture both the mean, variance and multi-modalities of the groundtruth distribution. Moreover, we apply the proposed approach to a real-world task of vehicle behavior prediction to demonstrate its effectiveness and accuracy. The results illustrate that the proposed model trained by adversarial learning can achieve a better prediction performance than other state-of-the-art models trained by traditional supervised learning which maximizes the data likelihood. The well-trained model can also be utilized as an implicit proposal distribution for particle filtered based Bayesian state estimation.
Tasks	Autonomous Vehicles, Decision Making, Motion Planning
Published	2019-04-04
URL	http://arxiv.org/abs/1904.02390v1
PDF	http://arxiv.org/pdf/1904.02390v1.pdf
PWC	https://paperswithcode.com/paper/interaction-aware-multi-agent-tracking-and
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Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments


Title	Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments
Authors	Xin Huang, Sungkweon Hong, Andreas Hofmann, Brian C. Williams
Abstract	A crucial challenge to efficient and robust motion planning for autonomous vehicles is understanding the intentions of the surrounding agents. Ignoring the intentions of the other agents in dynamic environments can lead to risky or over-conservative plans. In this work, we model the motion planning problem as a partially observable Markov decision process (POMDP) and propose an online system that combines an intent recognition algorithm and a POMDP solver to generate risk-bounded plans for the ego vehicle navigating with a number of dynamic agent vehicles. The intent recognition algorithm predicts the probabilistic hybrid motion states of each agent vehicle over a finite horizon using Bayesian filtering and a library of pre-learned maneuver motion models. We update the POMDP model with the intent recognition results in real time and solve it using a heuristic search algorithm which produces policies with upper-bound guarantees on the probability of near colliding with other dynamic agents. We demonstrate that our system is able to generate better motion plans in terms of efficiency and safety in a number of challenging environments including unprotected intersection left turns and lane changes as compared to the baseline methods.
Tasks	Autonomous Vehicles, Motion Planning
Published	2019-04-04
URL	http://arxiv.org/abs/1904.02341v1
PDF	http://arxiv.org/pdf/1904.02341v1.pdf
PWC	https://paperswithcode.com/paper/online-risk-bounded-motion-planning-for
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Context-aware Embedding for Targeted Aspect-based Sentiment Analysis


Title	Context-aware Embedding for Targeted Aspect-based Sentiment Analysis
Authors	Bin Liang, Jiachen Du, Ruifeng Xu, Binyang Li, Hejiao Huang
Abstract	Attention-based neural models were employed to detect the different aspects and sentiment polarities of the same target in targeted aspect-based sentiment analysis (TABSA). However, existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. This may result in targets or aspects having the same vector representations in different contexts and losing the context-dependent information. To address this problem, we propose a novel method to refine the embeddings of targets and aspects. Such pivotal embedding refinement utilizes a sparse coefficient vector to adjust the embeddings of target and aspect from the context. Hence the embeddings of targets and aspects can be refined from the highly correlative words instead of using context-independent or randomly initialized vectors. Experiment results on two benchmark datasets show that our approach yields the state-of-the-art performance in TABSA task.
Tasks	Aspect-Based Sentiment Analysis, Sentiment Analysis
Published	2019-06-17
URL	https://arxiv.org/abs/1906.06945v1
PDF	https://arxiv.org/pdf/1906.06945v1.pdf
PWC	https://paperswithcode.com/paper/context-aware-embedding-for-targeted-aspect
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Demonstration of a Neural Machine Translation System with Online Learning for Translators


Title	Demonstration of a Neural Machine Translation System with Online Learning for Translators
Authors	Miguel Domingo, Mercedes García-Martínez, Amando Estela, Laurent Bié, Alexandre Helle, Álvaro Peris, Francisco Casacuberta, Manuerl Herranz
Abstract	We introduce a demonstration of our system, which implements online learning for neural machine translation in a production environment. These techniques allow the system to continuously learn from the corrections provided by the translators. We implemented an end-to-end platform integrating our machine translation servers to one of the most common user interfaces for professional translators: SDL Trados Studio. Our objective was to save post-editing effort as the machine is continuously learning from human choices and adapting the models to a specific domain or user style.
Tasks	Machine Translation
Published	2019-06-21
URL	https://arxiv.org/abs/1906.09000v1
PDF	https://arxiv.org/pdf/1906.09000v1.pdf
PWC	https://paperswithcode.com/paper/demonstration-of-a-neural-machine-translation
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Using Local Experiences for Global Motion Planning


Title	Using Local Experiences for Global Motion Planning
Authors	Constantinos Chamzas, Anshumali Shrivastava, Lydia E. Kavraki
Abstract	Sampling-based planners are effective in many real-world applications such as robotics manipulation, navigation, and even protein modeling. However, it is often challenging to generate a collision-free path in environments where key areas are hard to sample. In the absence of any prior information, sampling-based planners are forced to explore uniformly or heuristically, which can lead to degraded performance. One way to improve performance is to use prior knowledge of environments to adapt the sampling strategy to the problem at hand. In this work, we decompose the workspace into local primitives, memorizing local experiences by these primitives in the form of local samplers, and store them in a database. We synthesize an efficient global sampler by retrieving local experiences relevant to the given situation. Our method transfers knowledge effectively between diverse environments that share local primitives and speeds up the performance dramatically. Our results show, in terms of solution time, an improvement of multiple orders of magnitude in two traditionally challenging high-dimensional problems compared to state-of-the-art approaches.
Tasks	Motion Planning
Published	2019-03-20
URL	http://arxiv.org/abs/1903.08693v1
PDF	http://arxiv.org/pdf/1903.08693v1.pdf
PWC	https://paperswithcode.com/paper/using-local-experiences-for-global-motion
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Heuristic Approach for Jointly Optimizing FeICIC and UAV Locations in Multi-Tier LTE-Advanced Public Safety HetNet


Title	Heuristic Approach for Jointly Optimizing FeICIC and UAV Locations in Multi-Tier LTE-Advanced Public Safety HetNet
Authors	Abhaykumar Kumbhar, Hamidullah Binol, Simran Singh, Ismail Guvenc, Kemal Akkaya
Abstract	UAV enabled communications and networking can enhance wireless connectivity and support emerging services. However, this would require system-level understanding to modify and extend the existing terrestrial network infrastructure. In this paper, we integrate UAVs both as user equipment and base stations into existing LTE-Advanced heterogeneous network (HetNet) and provide system-level insights of this three-tier LTE-Advanced air-ground HetNet (AG-HetNet). This AG-HetNet leverages cell range expansion (CRE), ICIC, 3D beamforming, and enhanced support for UAVs. Using system-level understanding and through brute-force technique and heuristics algorithms, we evaluate the performance of AG-HetNet in terms of fifth percentile spectral efficiency (5pSE) and coverage probability. We compare 5pSE and coverage probability, when aerial base-stations (UABS) are deployed on a fixed hexagonal grid and when their locations are optimized using genetic algorithm (GA) and elitist harmony search algorithm based on genetic algorithm (eHSGA). Our simulation results show the heuristic algorithms outperform the brute-force technique and achieve better peak values of coverage probability and 5pSE. Simulation results also show that trade-off exists between peak values and computation time when using heuristic algorithms. Furthermore, the three-tier hierarchical structuring of FeICIC provides considerably better 5pSE and coverage probability than eICIC.
Tasks
Published	2019-12-07
URL	https://arxiv.org/abs/2001.02760v1
PDF	https://arxiv.org/pdf/2001.02760v1.pdf
PWC	https://paperswithcode.com/paper/heuristic-approach-for-jointly-optimizing
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Back to Simplicity: How to Train Accurate BNNs from Scratch?


Title	Back to Simplicity: How to Train Accurate BNNs from Scratch?
Authors	Joseph Bethge, Haojin Yang, Marvin Bornstein, Christoph Meinel
Abstract	Binary Neural Networks (BNNs) show promising progress in reducing computational and memory costs but suffer from substantial accuracy degradation compared to their real-valued counterparts on large-scale datasets, e.g., ImageNet. Previous work mainly focused on reducing quantization errors of weights and activations, whereby a series of approximation methods and sophisticated training tricks have been proposed. In this work, we make several observations that challenge conventional wisdom. We revisit some commonly used techniques, such as scaling factors and custom gradients, and show that these methods are not crucial in training well-performing BNNs. On the contrary, we suggest several design principles for BNNs based on the insights learned and demonstrate that highly accurate BNNs can be trained from scratch with a simple training strategy. We propose a new BNN architecture BinaryDenseNet, which significantly surpasses all existing 1-bit CNNs on ImageNet without tricks. In our experiments, BinaryDenseNet achieves 18.6% and 7.6% relative improvement over the well-known XNOR-Network and the current state-of-the-art Bi-Real Net in terms of top-1 accuracy on ImageNet, respectively.
Tasks	Quantization
Published	2019-06-19
URL	https://arxiv.org/abs/1906.08637v1
PDF	https://arxiv.org/pdf/1906.08637v1.pdf
PWC	https://paperswithcode.com/paper/back-to-simplicity-how-to-train-accurate-bnns
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Parametrization of stochastic inputs using generative adversarial networks with application in geology


Title	Parametrization of stochastic inputs using generative adversarial networks with application in geology
Authors	Shing Chan, Ahmed H. Elsheikh
Abstract	We investigate artificial neural networks as a parametrization tool for stochastic inputs in numerical simulations. We address parametrization from the point of view of emulating the data generating process, instead of explicitly constructing a parametric form to preserve predefined statistics of the data. This is done by training a neural network to generate samples from the data distribution using a recent deep learning technique called generative adversarial networks. By emulating the data generating process, the relevant statistics of the data are replicated. The method is assessed in subsurface flow problems, where effective parametrization of underground properties such as permeability is important due to the high dimensionality and presence of high spatial correlations. We experiment with realizations of binary channelized subsurface permeability and perform uncertainty quantification and parameter estimation. Results show that the parametrization using generative adversarial networks is very effective in preserving visual realism as well as high order statistics of the flow responses, while achieving a dimensionality reduction of two orders of magnitude.
Tasks	Dimensionality Reduction
Published	2019-04-07
URL	http://arxiv.org/abs/1904.03677v2
PDF	http://arxiv.org/pdf/1904.03677v2.pdf
PWC	https://paperswithcode.com/paper/parametrization-of-stochastic-inputs-using
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Automatic Delineation of Kidney Region in DCE-MRI


Title	Automatic Delineation of Kidney Region in DCE-MRI
Authors	Santosh Tirunagari, Norman Poh, Kevin Wells, Miroslaw Bober, Isky Gorden, David Windridge
Abstract	Delineation of the kidney region in dynamic contrast-enhanced magnetic resonance Imaging (DCE-MRI) is required during post-acquisition analysis in order to quantify various aspects of renal function, such as filtration and perfusion or blood flow. However, this can be obfuscated by the Partial Volume Effect (PVE), caused due to the mixing of any single voxel with two or more signal intensities from adjacent regions such as liver region and other tissues. To avoid this problem, firstly, a kidney region of interest (ROI) needs to be defined for the analysis. A clinician may choose to select a region avoiding edges where PV mixing is likely to be significant. However, this approach is time-consuming and labour intensive. To address this issue, we present Dynamic Mode Decomposition (DMD) coupled with thresholding and blob analysis as a framework for automatic delineation of the kidney region. This method is first validated on synthetically generated data with ground-truth available and then applied to ten healthy volunteers’ kidney DCE-MRI datasets. We found that the result obtained from our proposed framework is comparable to that of a human expert. For example, while our result gives an average Root Mean Square Error (RMSE) of 0.0097, the baseline achieves an average RMSE of 0.1196 across the 10 datasets. As a result, we conclude automatic modelling via DMD framework is a promising approach.
Tasks
Published	2019-05-26
URL	https://arxiv.org/abs/1905.11387v1
PDF	https://arxiv.org/pdf/1905.11387v1.pdf
PWC	https://paperswithcode.com/paper/190511387
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ADD-Lib: Decision Diagrams in Practice


Title	ADD-Lib: Decision Diagrams in Practice
Authors	Frederik Gossen, Alnis Murtovi, Philip Zweihoff, Bernhard Steffen
Abstract	In the paper, we present the ADD-Lib, our efficient and easy to use framework for Algebraic Decision Diagrams (ADDs). The focus of the ADD-Lib is not so much on its efficient implementation of individual operations, which are taken by other established ADD frameworks, but its ease and flexibility, which arise at two levels: the level of individual ADD-tools, which come with a dedicated user-friendly web-based graphical user interface, and at the meta level, where such tools are specified. Both levels are described in the paper: the meta level by explaining how we can construct an ADD-tool tailored for Random Forest refinement and evaluation, and the accordingly generated Web-based domain-specific tool, which we also provide as an artifact for cooperative experimentation. In particular, the artifact allows readers to combine a given Random Forest with their own ADDs regarded as expert knowledge and to experience the corresponding effect.
Tasks
Published	2019-12-24
URL	https://arxiv.org/abs/1912.11308v1
PDF	https://arxiv.org/pdf/1912.11308v1.pdf
PWC	https://paperswithcode.com/paper/add-lib-decision-diagrams-in-practice
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