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Extracting Traffic Primitives Directly From Naturalistically Logged data for Self-Driving Applications

IEEE robotics AND automation LETTERS 2018年第2期3卷 1223-1229页

作者： Wang, Wenshuo Zhao, Ding Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA Beijing Inst Technol Dept Mech Engn Beijing 100081 Peoples R China Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

Developing an automated vehicle, that can handle complicated driving scenarios and appropriately interact with other road users, requires the ability to semantically learn and understand driving environment, oftentimes, based on analyzing massive amounts of naturalistic driving data. An important paradigm that allows automated vehicles to both learn from human drivers and gain insights is understanding the principal compositions of the entire traffic, termed as traffic primitives. However, the exploding data growth presents a great challenge in extracting primitives from high-dimensional time-series traffic data with various types of road users engaged. Therefore, automatically extracting primitives is becoming one of the cost-efficient ways to help autonomous vehicles understand and predict the complex traffic scenarios. In addition, the extracted primitives from raw data should 1) be appropriate for automated driving applications and also 2) be easily used to generate new traffic scenarios. However, existing literature does not provide a method to automatically learn these primitives from large-scale traffic data. The contribution of this letter has two manifolds. The first one is that we proposed a new framework to generate new traffic scenarios from a handful of limited traffic data. The second one is that, we introduce a nonparametric Bayesian learning method-a sticky hierarchical Dirichlet process hidden Markov model-to automatically extract primitives from multidimensional traffic datawithout prior knowledge of the primitive settings. The developed method is then validated using one day of naturalistic driving data. Experiment results show that the nonparametric Bayesian learning method is able to extract primitives from traffic scenarios where both the binary and continuous events coexist.

关键词： Intelligent transportation systems big data in robotics and automation

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Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile

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IEEE robotics AND automation LETTERS 2020年第4期5卷 5010-5017页

作者： Li, Ziyue Yan, Hao Zhang, Chen Tsung, Fugee Hong Kong Univ Sci & Technol Dept Ind Engn & Decis Analyt Hong Kong Peoples R China Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA Tsinghua Univ Ind Engn Beijing 100084 Peoples R China

Spatiotemporal data are very common in many applications, such as manufacturing systems and transportation systems. Given the intrinsic complex spatial and temporal correlations of such data, short-term and long-term prediction for spatiotemporal data is often very challenging. Most of the traditional statistical models fail to preserve innate features in data alongside their complex correlations. In this paper, we focus on a tensor-based prediction method and propose several practical techniques to improve both long-term and short-term prediction accuracy. For long-term prediction, we propose the "tensor decomposition + 2-Dimensional Auto-Regressive Moving Average (2D-ARMA)" model, and an effective way to update prediction in real-time;For short-term prediction, we propose to conduct tensor completion based on tensor clustering to avoid oversimplification and ensure accuracy. A case study based on the metro passenger flow data is conducted to demonstrate the improved performance.

关键词： Intelligent transportation system probability and statistical methods big data in robotics and automation

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LVDiffusor: Distilling Functional Rearrangement Priors From Large Models Into Diffusor

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IEEE robotics AND automation LETTERS 2024年第10期9卷 8258-8265页

作者： Zeng, Yiming Wu, Mingdong Yang, Long Zhang, Jiyao Ding, Hao Cheng, Hui Dong, Hao Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Peking Univ Sch CS Hyperplane Lab Beijing 100871 Peoples R China Peking Univ Natl Key Lab Multimedia Informat Proc Beijing 100871 Peoples R China Beijing Acad Artificial Intelligence Beijing 100084 Peoples R China

Object rearrangement, a fundamental challenge in robotics, demands versatile strategies to handle diverse objects, configurations, and functional needs. To achieve this, the AI robot needs to learn functional rearrangement priors to specify precise goals that meet the functional requirements. Previous methods typically learn such priors from either laborious human annotations or manually designed heuristics, which limits scalability and generalization. In this letter, we propose a novel approach that leverages large models to distill functional rearrangement priors. Specifically, our approach collects diverse arrangement examples using both LLMs and VLMs and then distills the examples into a diffusion model. During test time, the learned diffusion model is conditioned on the initial configuration and guides the positioning of objects to meet functional requirements. In this way, we balance zero-shot generalization with time efficiency. Extensive experiments in multiple domains, including real-world scenarios, demonstrate the effectiveness of our approach in generating compatible goals for object rearrangement tasks, significantly outperforming baseline methods.

关键词： AI-enabled robotics deep learning methods big data in robotics and automation AI-enabled robotics deep learning methods big data in robotics and automation

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Ground Segmentation From Large-Scale Terrestrial Laser Scanner data of Industrial Environments

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IEEE robotics AND automation LETTERS 2017年第4期2卷 1948-1955页

作者： Giorgini, M. Barbieri, F. Aleotti, J. Elettr 80 Spa I-42030 Viano RE Italy Univ Parma Dept Engn & Architecture I-43121 Parma Italy

Inmany 3-D perception applications, ground segmentation is a necessary preprocessing phase together with point cloud cleaning and outlier removal. This letter presents a method for ground segmentation in large-scale point clouds of industrial environments acquired using a terrestrial laser scanner (TLS). TLSs provide high-precision, dense 3-D measurements, and therefore, such instruments are becoming the state of the art technology for surveying tasks. In contrast to many previousworks, where ground segmentation has been investigated using a single scan (e.g., in LiDAR-equipped vehicles), experiments have been performed in large-scale point clouds that contain over 1010 points measured from multiple scan stations. The proposed solution is based on a robust estimation of points belonging to the ground below each scan station and it can be applied even in challenging scenarios with nonplanar regions.

关键词： big data in robotics and automation categorization factory automation logistics object detection range sensing segmentation

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LaND: Learning to Navigate From Disengagements

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IEEE robotics AND automation LETTERS 2021年第2期6卷 1872-1879页

作者： Kahn, Gregory Abbeel, Pieter Levine, Sergey Univ Calif Berkeley Berkeley CA 94710 USA

Consistently testing autonomous mobile robots in real world scenarios is a necessary aspect of developing autonomous navigation systems. Each time the human safety monitor disengages the robot's autonomy system due to the robot performing an undesirable maneuver, the autonomy developers gain insight into how to improve the autonomy system. However, we believe that these disengagements not only show where the system fails, which is useful for troubleshooting, but also provide a direct learning signal by which the robot can learn to navigate. We present a reinforcement learning approach for learning to navigate from disengagements, or LaND. LaND learns a neural network model that predicts which actions lead to disengagements given the current sensory observation, and then at test time plans and executes actions that avoid disengagements. Our results demonstrate LaND can successfully learn to navigate in diverse, real world sidewalk environments, outperforming both imitation learning and reinforcement learning approaches. Videos, code, and other material are available on our website https://***/view/sidewalk-learning.

关键词： big data in robotics and automation field robots machine learning for robot control

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Language Models as Zero-Shot Trajectory Generators

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IEEE robotics AND automation LETTERS 2024年第7期9卷 6728-6735页

作者： Kwon, Teyun Di Palo, Norman Johns, Edward Imperial Coll London Robot Learning Lab London SW7 2AZ England

Large Language Models (LLMs) have recently shown promise as high-level planners for robots when given access to a selection of low-level skills. However, it is often assumed that LLMs do not possess sufficient knowledge to be used for the low-level trajectories themselves. In this work, we address this assumption thoroughly, and investigate if an LLM (GPT-4) can directly predict a dense sequence of end-effector poses for manipulation tasks, when given access to only object detection and segmentation vision models. We designed a single, task-agnostic prompt, without any in-context examples, motion primitives, or external trajectory optimisers. Then we studied how well it can perform across 30 real-world language-based tasks, such as "open the bottle cap" and "wipe the plate with the sponge", and we investigated which design choices in this prompt are the most important. Our conclusions raise the assumed limit of LLMs for robotics, and we reveal for the first time that LLMs do indeed possess an understanding of low-level robot control sufficient for a range of common tasks, and that they can additionally detect failures and then re-plan trajectories accordingly.

关键词： Task analysis Robots Trajectory End effectors Codes Robot kinematics Object detection AI-based methods big data in robotics and automation deep learning in grasping and manipulation

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DALL-E-Bot: Introducing Web-Scale Diffusion Models to robotics

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IEEE robotics AND automation LETTERS 2023年第7期8卷 3956-3963页

作者： Kapelyukh, Ivan Vosylius, Vitalis Johns, Edward Imperial Coll London Robot Learning Lab London SW7 2AZ England Imperial Coll London Dyson Robot Lab London SW7 2AZ England

We introduce the first work to explore web-scale diffusion models for robotics. DALL-E-Bot enables a robot to rearrange objects in a scene, by first inferring a text description of those objects, then generating an image representing a natural, human-like arrangement of those objects, and finally physically arranging the objects according to that goal image. We show that this is possible zero-shot using DALL-E, without needing any further example arrangements, data collection, or training. DALL-E-Bot is fully autonomous and is not restricted to a pre-defined set of objects or scenes, thanks to DALL-E's web-scale pre-training. Encouraging real-world results, with both human studies and objective metrics, show that integrating web-scale diffusion models into robotics pipelines is a promising direction for scalable, unsupervised robot learning.

关键词： AI-based methods big data in robotics and automation deep learning in grasping and manipulation

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Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems With Heterogeneous Sensor data

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3509-3516页

作者： Liu, Boyi Wang, Lujia Liu, Ming Xu, Cheng-Zhong Chinese Acad Sci Shenzhen Inst Adv Technol Cloud Comp Lab Shenzhen 518000 Peoples R China Univ Chinese Acad Sci Shenzhen 518000 Peoples R China Hong Kong Univ Sci & Technol Dept ECE Hong Kong Peoples R China Univ Macau Macau 999078 Peoples R China

Humans are capable of learning a new behavior by observing others to perform the skill. Similarly, robots can also implement this by imitation learning. Furthermore, if with external guidance, humans can master the new behavior more efficiently. So, how can robots achieve this? To address the issue, we present a novel framework named FIL. It provides a heterogeneous knowledge fusion mechanism for cloud robotic systems. Then, a knowledge fusion algorithm in FIL is proposed. It enables the cloud to fuse heterogeneous knowledge from local robots and generate guide models for robots with service requests. After that, we introduce a knowledge transfer scheme to facilitate local robots acquiring knowledge from the cloud. With FIL, a robot is capable of utilizing knowledge from other robots to increase its imitation learning in accuracy and efficiency. Compared with transfer learning and meta-learning, FIL is more suitable to be deployed in cloud robotic systems. Finally, we conduct experiments of a self-driving task for robots (cars). The experimental results demonstrate that the shared model generated by FIL increases imitation learning efficiency of local robots in cloud robotic systems.

关键词： Cloud computing Robot sensing systems Task analysis Fuses Microstrip Computational modeling big data in robotics and automation deep learning in robotics and automation motion and path planning

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BADGR: An Autonomous Self-Supervised Learning-Based Navigation System

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IEEE robotics AND automation LETTERS 2021年第2期6卷 1312-1319页

作者： Kahn, Gregory Abbeel, Pieter Levine, Sergey Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94710 USA

Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal. However, a purely geometric view of the world can he insufficient for many navigation problems. For example, a robot navigating based on geometry may avoid a field of tall grass because it believes it is untraversable, and will therefore fail to reach its desired goal. In this work, we investigate how to move beyond these purely geometric-based approaches using a method that learns about physical navigational affordances from experience. Our reinforcement learning approach, which we call BADGR , is an end-to-end learning-based mobile robot navigation system that can be trained with autonomously-labeled off-policy data gathered in real-world environments, without any simulation or human supervision. BADGR can navigate in real-world urban and off-road environments with geometrically distracting obstacles. It can also incorporate terrain preferences, generalize to novel environments, and continue to improve autonomously by gathering more data. Videos, code, and other supplemental material are available on our website https://***/view/badgr

关键词： big data in robotics and automation reinforcement learning autonomous agents

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Effects of Extended Stochastic Gradient Descent Algorithms on Improving Latent Factor-Based Recommender Systems

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IEEE robotics AND automation LETTERS 2019年第2期4卷 618-624页

作者： Luo, Xin Zhou, MengChu Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China Chinese Acad Sci Chongqing Engn Res Ctr Big Data Applicat Smart Ci Chongqing 400714 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

High-dimensional and sparse (HiDS) matrices from recommender systems contain various useful patterns. A latent factor (LF) analysis is highly efficient in grasping these patterns. Stochastic gradient descent (SGD) is a widely adopted algorithm to train an LF model. Can its extensions be capable of further improving an LF models' convergence rate and prediction accuracy for missing data? To answer this question, this work selects two of representative extended SGD algorithms to propose two novel LF models. Experimental results from two HiDS matrices generated by real recommender systems show that compared standard SGD, extended SGD algorithms enable an LF model to achieve a higher prediction accuracy for missing data of an HiDS matrix, a faster convergence rate, and a larger model diversity.

关键词： big data in robotics and automation AI-based methods machine learning

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