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GOSELO: Goal-Directed Obstacle and Self-Location Map for Robot Navigation Using Reactive Neural Networks

IEEE robotics AND automation LETTERS 2018年第2期3卷 696-703页

作者： Kanezaki, Asako Nitta, Jirou Sasaki, Yoko Natl Inst Adv Ind Sci & Technol Tokyo 1350064 Japan

Robot navigation using deep neural networks has been drawing a great deal of attention. Although reactive neural networks easily learn expert behaviors and are computationally efficient, they suffer from generalization of policies learned in specific environments. As such, reinforcement learning and value iteration approaches for learning generalized policies have been proposed. However, these approaches are more costly. In this letter, we tackle the problem of learning reactive neural networks that are applicable to general environments. The key concept is to crop, rotate, and rescale an obstacle map according to the goal location and the agent's current location so that the map representation will be better correlated with self-movement in the general navigation task, rather than the layout of the environment. Furthermore, in addition to the obstacle map, we input a map of visited locations that contains the movement history of the agent, in order to avoid failures that the agent travels back and forth repeatedly over the same location. Experimental results reveal that the proposed network outperforms the state-of-the-art value iteration network in the grid-world navigation task. We also demonstrate that the proposed model can be well generalized to unseen obstacles and unknown terrain. Finally, we demonstrate that the proposed system enables a mobile robot to successfully navigate in a real dynamic environment.

关键词： deep learning in robotics and automation visual-based navigation recognition

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TACTO: A Fast, Flexible, and Open-Source Simulator for High-Resolution Vision-Based Tactile Sensors

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IEEE robotics AND automation LETTERS 2022年第2期7卷 3930-3937页

作者： Wang, Shaoxiong Lambeta, Mike Chou, Po-Wei Calandra, Roberto Meta AI Menlo Pk CA 94025 USA MIT Cambridge MA 02139 USA

Simulators perform an important role in prototyping, debugging, and benchmarking new advances in robotics and learning for control. Although many physics engines exist, some aspects of the real world are harder than others to simulate. One of the aspects that have so far eluded accurate simulation is touch sensing. To address this gap, we present TACTO - a fast, flexible, and open-source simulator for vision-based tactile sensors. This simulator allows to render realistic high-resolution touch readings at hundreds of frames per second, and can be easily configured to simulate different vision-based tactile sensors, including DIGIT and OmniTact. In this letter, we detail the principles that drove the implementation of TACTO and how they are reflected in its architecture. We demonstrate TACTO on a perceptual task, by learning to predict grasp stability using touch from 1 million grasps, and on a marble manipulation control task. Moreover, we provide a proof-of-concept that TACTO can be successfully used for Sim2Real applications. We believe that TACTO is a step towards the widespread adoption of touch sensing in robotic applications, and to enable machine learning practitioners interested in multi-modal learning and control.

关键词： Robot sensing systems Tactile sensors Cameras Task analysis Physics Engines Rendering (computer graphics) Simulation and animation deep learning in robotics and automation force and tactile sensing learning and adaptive systems perception for grasping and manipulation

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learning to See the Wood for the Trees: deep Laser Localization in Urban and Natural Environments on a CPU

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1327-1334页

作者： Tinchev, Georgi Penate-Sanchez, Adrian Fallon, Maurice Univ Oxford Oxford Robot Inst Dynam Syst Grp Oxford OX2 6NN England

Localization in challenging, natural environments, such as forests or woodlands, is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this letter, we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from three-dimensional point clouds by comparing triplets (anchor, positive, and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored toward loop closure detection resulting in a small model that can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payloads, such as drones, quadrupeds, or Unmanned Ground Vehicles (UGVs).

关键词： Localization deep learning in robotics and automation visual learning SLAM field robots

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Real-Time 3-D Shape Instantiation From Single Fluoroscopy Projection for Fenestrated Stent Graft Deployment

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IEEE robotics AND automation LETTERS 2018年第2期3卷 1314-1321页

作者： Zhou, Xiao-Yun Lin, Jianyu Riga, Celia Yang, Guang-Zhong Lee, Su-Lin Imperial Coll London Hamlyn Ctr Robot Surg London SW7 2AZ England St Marys Hosp Reg Vasc Unit London W2 1NY England Imperial Coll London Acad Div Surg London SW7 2AZ England

Robot-assisted deployment of fenestrated stent grafts in fenestrated endovascular aortic repair (FEVAR) requires accurate geometrical alignment. Currently, this process is guided by two-dimensional (2-D) fluoroscopy, which is insufficiently informative and error prone. In this letter, a real-time framework is proposed to instantiate the 3-D shape of a fenestrated stent graft by utilizing only a single low-dose 2-D fluoroscopic image. First, markers were placed on the fenestrated stent graft. Second, the 3-D pose of each stent segment was instantiated by the robust perspective-n-point method. Third, the 3-D shape of the whole stent graft was instantiated via graft gap interpolation. Focal UNet was proposed to segment the markers from 2-D fluoroscopic images to achieve semiautomatic marker detection. The proposed framework was validated on five patient-specific 3-D printed aortic aneurysm phantoms and three stent grafts with new marker placements, showing an average distance error of 1-3mm and an average angular error of 4 degrees Shape instantiation codes are available online.

关键词： deep learning in robotics and automation computer vision for medical robotics surgical robotics: planning visual-based navigation motion and path planning

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Generative Localization With Uncertainty Estimation Through Video-CT Data for Bronchoscopic Biopsy

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IEEE robotics AND automation LETTERS 2020年第1期5卷 258-265页

作者： Zhao, Cheng Shen, Mali Sun, Li Yang, Guang-Zhong Imperial Coll London Hamlyn Ctr London SW7 2AZ England Univ Oxford Oxford Robot Inst Oxford OX1 2JD England Shanghai Jiao Tong Univ Inst Med Shanghai 200240 Peoples R China

Robot-assisted endobronchial intervention requires accurate localization based on both intra- and pre-operative data. Most existing methods achieve this by registering 2D videos with 3D CT models according to a defined similarity metric with local features. Instead, we formulate the bronchoscopic localization as a learning-based global localisation using deep neural networks. The proposed network consists of two generative architectures and one auxiliary learning component. The cycle generative architecture bridges the domain variance between the real bronchoscopic videos and virtual views derived from pre-operative CT data so that the proposed approach can be trained through a large number of generated virtual images but deployed through real images. The auxiliary learning architecture leverages complementary relative pose regression to constrain the search space, ensuring consistent global pose predictions. Most importantly, the uncertainty of each global pose is obtained through variational inference by sampling within the learned underlying probability distribution. Detailed validation results demonstrate the localization accuracy with reasonable uncertainty achieved and its potential clinical value. A demonstration video demo can be found on the website https://***/ci9LMY49aF8.

关键词： Computer vision for medical robotics medical robots and systems localization visual learning deep learning in robotics and automation

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Distributed Perception by Collaborative Robots

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3709-3716页

作者： Hadidi, Ramyad Cao, Jiashen Woodward, Matthew Ryoo, Michael S. Kim, Hyesoon Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA Georgia Inst Technol Dept Elect Engn Atlanta GA 30332 USA EgoVid Inc Ulsan 44919 South Korea

Recognition ability and, more broadly, machine learning techniques enable robots to perform complex tasks and allow them to function in diverse situations. In fact, robots can easily access an abundance of sensor data that are recorded in real time such as speech, image, and video. Since such data are time sensitive, processing them in real time is a necessity. Moreover, machine learning techniques are known to be computationally intensive and resource hungry. As a result, an individual resource-constrained robot, in terms of computation power and energy supply, is often unable to handle such heavy real-time computations alone. To overcome this obstacle, we propose a framework to harvest the aggregated computational power of several low-power robots for enabling efficient, dynamic, and real-time recognition. Our method adapts to the availability of computing devices at runtime and adjusts to the inherit dynamics of the network. Our framework can be applied to any distributed robot system. To demonstrate, with several Raspberry-Pi3-based robots (up to 12) each equipped with a camera, we implement a state-of-the-art action recognition model for videos and two recognition models for images. Our approach allows a group of multiple low-power robots to obtain a similar performance (in terms of the number of images or video frames processed per second) compared to a high-end embedded platform, Nvidia Tegra TX2.

关键词： deep learning in robotics and automation distributed robot system

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Recurrent-OctoMap: learning State-Based Map Refinement for Long-Term Semantic Mapping With 3-D-Lidar Data

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3749-3756页

作者： Sun, Li Yan, Zhi Zaganidis, Anestis Zhao, Cheng Duckett, Tom Univ Lincoln L CAS Lincoln LN6 7TS England UTBM Lab Elect Informat & Image CNRS F-90010 Belfort France

This letter presents a novel semantic mapping approach, Recurrent-OctoMap, learned from long-term three-dimensional (3-D) Lidar data. Most existing semantic mapping approaches focus on improving semantic understanding of single frames, rather than 3-D refinement of semantic maps (i.e. fusing semantic observations). The most widely used approach for the 3-D semantic map refinement is "Bayes update," which fuses the consecutive predictive probabilities following a Markov-chain model. Instead, we propose a learning approach to fuse the semantic features, rather than simply fusing predictions from a classifier. In our approach, we represent and maintain our 3-D map as an OctoMap, and model each cell as a recurrent neural network, to obtain a Recurrent-OctoMap. In this case, the semantic mapping process can he formulated as a sequence-to-sequence encoding-decoding problem. Moreover, in order to extend the duration of observations in our Recurrent-OctoMap, we developed a robust 3-D localization and mapping system for successively mapping a dynamic environment using more than two weeks of data, and the system can he trained and deployed with arbitrary memory length. We validate our approach on the ETH long-term 3-D Lidar dataset. The experimental results show that our proposed approach outperforms the conventional "Bayes update" approach.

关键词： Mapping simultaneous localization and mapping (SLAM) deep learning in robotics and automation object detection segmentation and categorization

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PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent learning

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IEEE robotics AND automation LETTERS 2019年第3期4卷 2378-2385页

作者： Sartoretti, Guillaume Kerr, Justin Shi, YunFei Wagner, Glenn Kumar, T. K. Satish Koenig, Sven Choset, Howie Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA CSIRO Pullenvale Qld 4069 Australia Univ Southern Calif Comp Sci Dept Los Angeles CA 90089 USA

Multi-agent path finding (MAPF) is an essential component of many large-scale, real-world robot deployments, from aerial swarms to warehouse automation. However, despite the community's continued efforts, most state-of-the-art MAPF planners still rely on centralized planning and scale poorly past a few hundred agents. Such planning approaches are maladapted to real-world deployments, where noise and uncertainty often require paths be recomputed online, which is impossible when planning times are in seconds to minutes. We present PRIMAL, a novel framework for MAPF that combines reinforcement and imitation learning to teach fully decentralized policies, where agents reactively plan paths online in a partially observable world while exhibiting implicit coordination. This framework extends our previous work on distributed learning of collaborative policies by introducing demonstrations of an expert MAPF planner during training, as well as careful reward shaping and environment sampling. Once learned, the resulting policy can be copied onto any number of agents and naturally scales to different team sizes and world dimensions. We present results on randomized worlds with up to 1024 agents and compare success rates against state-of-the-art MAPF planners. Finally, we experimentally validate the learned policies in a hybrid simulation of a factory mockup, involving both real world and simulated robots.

关键词： Path planning for multiple mobile robots or agents deep learning in robotics and automation distributed robot systems AI-based methods factory automation

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deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution

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IEEE robotics AND automation LETTERS 2018年第4期3卷 4007-4014页

作者： Rothfuss, Jonas Ferreira, Fabio Aksoy, Eren Erdal Zhou, You Asfour, Tamim Karlsruhe Inst Technol Inst Anthropomat & Robot D-76131 Karlsruhe Germany Halmstad Univ Sch Informat Technol S-30118 Halmstad Sweden

We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory that facilitates encoding, recalling, and predicting action experiences. Our proposed unsupervised deep episodic memory model as follows: First, encodes observed actions in a latent vector space and, based on this latent encoding, second, infers most similar episodes previously experienced, third, reconstructs original episodes, and finally, predicts future frames in an end-to-end fashion. Results show that conceptually similar actions are mapped into the same region of the latent vector space. Based on these results, we introduce an action matching and retrieval mechanism, benchmark its performance on two large-scale action datasets, 20BN-something-something and ActivityNet and evaluate its generalization capability in a real-world scenario on a humanoid robot.

关键词： learning and adaptive systems visual learning deep learning in robotics and automation

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

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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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