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Lifelong Federated Reinforcement learning: A learning Architecture for Navigation in Cloud Robotic Systems

IEEE robotics AND automation LETTERS 2019年第4期4卷 4555-4562页

作者： Liu, Boyi Wang, Lujia Liu, Ming Chinese Acad Sci Shenzhen Inst Adv Technol Cloud Comp Lab Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

This letter was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement learning (LFRL). In the letter, we propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website to provide the service based on LFRL: ***.

关键词： Navigation Reinforcement learning Task analysis Cloud computing Training Robot kinematics deep learning in robotics and automation autonomous vehicle navigation AI-based methods

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Vision-Based High-Speed Driving With a deep Dynamic Observer

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1564-1571页

作者： Drews, Paul Williams, Grady Goldfain, Brian Theodorou, Evangelos A. Rehg, James M. Georgia Inst Technol IRIM Atlanta GA 30332 USA

In this letter, we present a framework for combining deep learning-based road detection, particle filters, and model predictive control (MPC) to drive aggressively using only amonocular camera, IMU, and wheel speed sensors. This framework uses deep convolutional neural networks combined with LSTMs to learn a local cost map representation of the track in front of the vehicle. A particle filter uses this dynamic observation model to localize in a schematic map, and MPC is used to drive aggressively using this particle filter based state estimate. We show extensive real world testing results and demonstrate reliable operation of the vehicle at the friction limits on a complex dirt track. We reach speeds above 27 m/h (12 m/s) on a dirt track with a 105 ft (32 m) long straight using our 1: 5 scale test vehicle.

关键词： deep learning in robotics and automation autonomous vehicle navigation localization computer vision for transportation

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Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3-D Pedestrian Pose and Gait Prediction

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1501-1508页

作者： Du, Xiaoxiao Vasudevan, Ram Johnson-Roberson, Matthew Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

In applications, such as autonomous driving, it is important to understand, infer, and anticipate the intention and future behavior of pedestrians. This ability allows vehicles to avoid collisions and improve ride safety and quality. This letter proposes a biomechanically inspired recurrent neural network that can predict the location and three-dimensional (3-D) articulated body pose of pedestrians in a global coordinate frame, given 3-D poses and locations estimated in prior frames with inaccuracy. The proposed network is able to predict poses and global locations for multiple pedestrians simultaneously, for pedestrians up to 45 m from the cameras (urban intersection scale). The outputs of the proposed network are full-body 3-D meshes represented in skinned multiperson linear model parameters. The proposed approach relies on a novel objective function that incorporates the periodicity of human walking (gait), the mirror symmetry of the human body, and the change of ground reaction forces in a human gait cycle. This letter presents prediction results on the PedX dataset, a large-scale, in-the-wild data set collected at real urban intersections with heavy pedestrian traffic. Results showthat the proposed network can successfully learn the characteristics of pedestrian gait and produce accurate and consistent 3-D pose predictions.

关键词： deep learning in robotics and automation gesture posture and facial expressions kinematics long short-term memory (LSTM) pedestrian gait prediction

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Self-Supervised Drivable Area and Road Anomaly Segmentation Using RGB-D Data For Robotic Wheelchairs

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IEEE robotics AND automation LETTERS 2019年第4期4卷 4386-4393页

作者： Wang, Hengli Sun, Yuxiang Liu, Ming Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Clear Water Bay Hong Kong Peoples R China

The segmentation of drivable areas and road anomalies are critical capabilities to achieve autonomous navigation for robotic wheelchairs. The recent progress of semantic segmentation using deep learning techniques has presented effective results. However, the acquisition of large-scale datasets with hand-labeled ground truth is time-consuming and labor-intensive, making the deep learning-based methods often hard to implement in practice. We contribute to the solution of this problem for the task of drivable area and road anomaly segmentation by proposing a self-supervised learning approach. We develop a pipeline that can automatically generate segmentation labels for drivable areas and road anomalies. Then, we train RGB-D data-based semantic segmentation neural networks and get predicted labels. Experimental results show that our proposed automatic labeling pipeline achieves an impressive speed-up compared to manual labeling. In addition, our proposed self-supervised approach exhibits more robust and accurate results than the state-of-the-art traditional algorithms as well as the state-of-the-art self-supervised algorithms.

关键词： Semantic scene understanding deep learning in robotics and automation RCB-D perception

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deepIG: Multi-Robot Information Gathering With deep Reinforcement learning

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IEEE robotics AND automation LETTERS 2019年第3期4卷 3059-3066页

作者： Viseras, Alberto Garcia, Ricardo German Aerosp Ctr DLR Inst Commun & Nav D-82234 Oberplaffenholen Germany Univ Politecn Madrid ETSI Telecomunicac E-28040 Madrid Spain

State-of-the-art multi-robot information gathering (MR-IG) algorithms often rely on a model that describes the structure of the information of interest to drive the robots motion. This causes MR-IG algorithms to fail when they are applied to new IG tasks, as existing models cannot describe the information of interest. Therefore, we propose in this letter a MR-IG algorithm that can be applied to new IG tasks with little algorithmic changes. To this end, we introduce deepIG: a MR-IG algorithm that uses deep reinforcement learning to allow robots to learn how to gather information. Nevertheless, there are IG tasks for which accurate models have been derived. Therefore, we extend deepIG to exploit existing models for such IG tasks. This algorithm we term it model-based deepIG (MB-deepIG). First, we evaluate deepIG in simulations, and in an indoor experiment with three quadcopters that autonomously map an unknown terrain profile built in our lab. Results demonstrate that deepIG can be applied to different IG tasks without algorithmic changes, and that it is robust to measurement noise. Then, we benchmark MB-deepIG against state-of-the-art information-driven Gaussian-processes-based IG algorithms. Results demonstrate that MB-deepIG outperforms the considered benchmarks.

关键词： Multi-robot systems deep learning in robotics and automation

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Autonomous 3-D Reconstruction, Mapping, and Exploration of Indoor Environments With a Robotic Arm

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IEEE robotics AND automation LETTERS 2019年第4期4卷 3340-3347页

作者： Wang, Yiming James, Stuart Stathopoulou, Elisavet Konstantina Beltran-Gonzalez, Carlos Konishi, Yoshinori Del Bue, Alessi IIT VGM Lab I-16163 Genoa Italy IIT CCHT I-30175 Venice Italy Bruno Kessler Fdn FBK 3D Opt Metrol Unit I-38123 Trento Italy IIT Pattern Anal & Comp Vis PAVIS Dept I-16163 Genoa Italy OMRON Res Kyoto 6008530 Japan

We propose a novel information gain metric that combines hand-crafted and data-driven metrics to address the next best view problem for autonomous 3-D mapping of unknown indoor environments. For the hand-crafted metric, we propose an entropy-based information gain that accounts for the previous view points to avoid the camera to revisit the same location and to promote the motion toward unexplored or occluded areas. However, for the learnt metric, we adopt a convolutional neural network (CNN) architecture and formulate the problem as a classification problem. The CNN takes the current depth image as input and outputs the motion direction that suggests the largest unexplored surface. We train and test the CNN using a new synthetic dataset based on the SUNCG dataset. The learnt motion direction is then combined with the proposed hand-crafted metric to help handle situations where using only the hand-crafted metric tends to face ambiguities. We finally evaluate the autonomous paths over several real and synthetic indoor scenes including complex industrial and domestic settings and prove that our combined metric is able to further improve the exploration coverage compared to using only the proposed hand-crafted metric.

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

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Environment-Aware Multi-Target Tracking of Pedestrians

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1831-1837页

作者： Doellinger, Johannes Prabhakaran, Vishnu Suganth Fu, Liangcheng Spies, Markus Bosch Ctr Artificial Intelligence D-71272 Renningen Germany Univ Stuttgart Dept Comp Sci D-70569 Stuttgart Germany Meituan Dianping Grp Beijing 100102 Peoples R China

When navigating mobile robotic systems in dynamic environments, the ability to predict where pedestrians will move in the next few seconds is crucial. To tackle this problem, many solutions have been developed which take the environment's influence on human navigation behavior into account. However, few do this in a way which seamlessly generalizes to new environments where no prior observations of pedestrians are available. In this letter, we propose a novel method that uses convolutional neural networks (CNNs) to predict local statistics about the direction of likely human motion. Specifically, the network takes crops of a floor plan as an input and computes a cell-wise probability distributions over directions conditioned on the direction in the last time step. These environment-aware motion models can be used to improve the performance of existing tracking algorithms. We evaluated our approach with two different existing trackers, a discrete, grid-based filter as well as a particle filter and show that using our proposed method as motion model considerably improves prediction quality in previously unseen test environments. Even though we train the CNN entirely in simulation, our experiments suggest that the learned models generalize to real pedestrian data.

关键词： deep learning in robotics and automation Semantic Scene Understanding Social Human-Robot Interaction

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VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1148-1155页

作者： Zhang, Jingwei Tai, Lei Yun, Peng Xiong, Yufeng Liu, Ming Boedecker, Joschka Burgard, Wolfram Univ Freiburg Dept Comp Sci D-79110 Breisgau Germany Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

In this letter, we deal with the reality gap from a novel perspective, targeting transferring deep reinforcement learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting the common solutions to the problem by increasing the visual fidelity of synthetic images output from simulators during the training phase, we seek to tackle the problem by translating the real-world image streams back to the synthetic domain during the deployment phase, to make the robot feel at home. We propose this as a lightweight, flexible, and efficient solution for visual control, as first, no extra transfer steps are required during the expensive training of DRL agents in simulation;second, the trained DRL agents will not be constrained to being deployable in only one specific real-world environment;and third, the policy training and the transfer operations are decoupled, and can be conducted in parallel. Besides this, we propose a simple yet effective shift loss that is agnostic to the downstream task, to constrain the consistency between subsequent frames which is important for consistent policy outputs. We validate the shift loss for artistic style transfer for videos and domain adaptation, and validate our visual control approach in indoor and outdoor robotics experiments.

关键词： deep learning in robotics and automation visual-based navigation model learning for control

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Context-Aware Depth and Pose Estimation for Bronchoscopic Navigation

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IEEE robotics AND automation LETTERS 2019年第2期4卷 732-739页

作者： Shen, Mali Gu, Yun Liu, Ning Yang, Guang-Zhong Imperial Coll London Hamlyn Ctr Robot Surg London SW7 2AZ England

Endobronchial intervention is increasingly used as a minimally invasive means of lung intervention. Vision-based localization approaches are often sensitive to image artifacts in bronchoscopic videos. In this letter, a robust navigation system based on a context-aware depth recovery approach for monocular video images is presented. To handle the artifacts, a conditional generative adversarial learning framework is proposed for reliable depth recovery. The accuracy of depth estimation and camera localization is validated on an in vivo dataset. Both quantitative and qualitative results demonstrate that the depth recovered with the proposed method preserves better structural information of airway lumens in the presence of image artifacts, and the improved camera localization accuracy demonstrates its clinical potential for bronchoscopic navigation.

关键词： Visual learning visual-based navigation computer vision for medical robotics deep learning in robotics and automation

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Focal Loss in 3D Object Detection

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1263-1270页

作者： Yun, Peng Tai, Lei Wang, Yuan Liu, Chengju Liu, Ming Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

3D object detection is still an open problem in autonomous driving scenes. When recognizing and localizing key objects from sparse 3D inputs, autonomous vehicles suffer from a larger continuous searching space and higher fore-background imbalance compared to image-based object detection. In this letter, we aim to solve this fore-background imbalance in 3D object detection. Inspired by the recent use of focal loss in image-based object detection, we extend this hard-mining improvement of binary cross entropy to point-cloud-based object detection and conduct experiments to showits performance based on two different 3D detectors: 3D-FCN and VoxelNet. The evaluation results show up to 11.2AP gains through the focal loss in a wide range of hyperparameters for 3D object detection.

关键词： deep learning in robotics and automation Object Detection Segmentation and Categorization Recognition

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