检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Zhang, Yulun Fontaine, Matthew C. Bhatt, Varun Nikolaidis, Stefanos Li, Jiaoyang Robotics Institute Carnegie Mellon University United States Thomas Lord Department of Computer Science University of Southern California United States

We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective method for optimizing the environments of automated warehouses. However, these approaches optimize only relatively small environments, falling short when it comes to replicating real-world warehouse sizes. The challenge arises from the exponential increase in the search space as the environment size increases. Additionally, the previous methods have only been tested with up to 350 robots in simulations, while practical warehouses could host thousands of robots. In this paper, instead of optimizing environments, we propose to optimize Neural Cellular Automata (NCA) environment generators via QD algorithms. We train a collection of NCA generators with QD algorithms in small environments and then generate arbitrarily large environments from the generators at test time. We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2,350 robots. Additionally, we demonstrate that our method scales a single-agent reinforcement learning policy to arbitrarily large environments with similar patterns. We include the source code at https://***/lunjohnzhang/warehouse_ env_gen_nca_public. Copyright © 2023, The Authors. All rights reserved.

关键词： Multipurpose robots

来源：评论

学校读者我要写书评

暂无评论

Non-smooth Control Barrier Functions for Stochastic Dynamical Systems

arXiv

引用

arXiv 2023年

作者： Vahs, Matti Tumova, Jana The Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

Uncertainties arising in various control systems, such as robots that are subject to unknown disturbances or environmental variations, pose significant challenges for ensuring system safety, such as collision avoidance. At the same time, safety specifications are getting more and more complex, e.g., by composing multiple safety objectives through Boolean operators resulting in non-smooth descriptions of safe sets. Control Barrier Functions (CBFs) have emerged as a control technique to provably guarantee system safety. In most settings, they rely on an assumption of having deterministic dynamics and smooth safe sets. This paper relaxes these two assumptions by extending CBFs to encompass control systems with stochastic dynamics and safe sets defined by non-smooth functions. By explicitly considering the stochastic nature of system dynamics and accommodating complex safety specifications, our method enables the design of safe control strategies in uncertain and complex systems. We provide formal guarantees on the safety of the system by leveraging the theoretical foundations of stochastic CBFs and non-smooth safe sets. Numerical simulations demonstrate the effectiveness of the approach in various scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Robust Humanoid Walking on Compliant and Uneven Terrain with Deep Reinforcement Learning

Robust Humanoid Walking on Compliant and Uneven Terrain with...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Rohan P. Singh Mitsuharu Morisawa Mehdi Benallegue Zhaoming Xie Fumio Kanehiro CNRS-AIST JRL (Joint Robotics Laboratory) IRL National Institute of Advanced Industrial Science and Technology (AIST) Japan University of Tsukuba Ibaraki Japan Department of Computer Science Stanford University USA

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

For the deployment of legged robots in real-world environments, it is essential to develop robust locomotion control methods for challenging terrains that may exhibit unexpected deformability and irregularity. In this paper, we explore the application of sim-to-real deep reinforcement learning (RL) for the design of bipedal locomotion controllers for humanoid robots on compliant and uneven terrains. Our key contribution is to show that a simple training curriculum for exposing the RL agent to randomized terrains in simulation can achieve robust walking on a real humanoid robot using only proprioceptive feedback. We train an end-to-end bipedal locomotion policy using the proposed approach, and show extensive real-robot demonstration on the HRP-5P humanoid over several difficult terrains inside and outside the lab environment. Further, we argue that the robustness of a bipedal walking policy can be improved if the robot is allowed to exhibit aperiodic motion with variable stepping frequency. We propose a new control policy to enable modification of the observed clock signal, leading to adaptive gait frequencies depending on the terrain and command velocity. Through simulation experiments, we show the effectiveness of this policy specifically for walking over challenging terrains by controlling swing and stance durations. The code for training and evaluation is available online. https://***/rohanpsingh/LearningHumanoidWalking

关键词： Legged locomotion Training Codes Humanoid robots Propioception Deep reinforcement learning Robustness Frequency control Clocks

来源：评论

学校读者我要写书评

暂无评论

VARIQuery: VAE Segment-Based Active Learning for Query Selection in Preference-Based Reinforcement Learning

VARIQuery: VAE Segment-Based Active Learning for Query Selec...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Daniel Marta Simon Holk Christian Pek Jana Tumova Iolanda Leite Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

Human-in-the-loop reinforcement learning (RL) methods actively integrate human knowledge to create reward functions for various robotic tasks. Learning from preferences shows promise as alleviates the requirement of demonstrations by querying humans on state-action sequences. However, the limited granularity of sequence-based approaches complicates temporal credit assignment. The amount of human querying is contingent on query quality, as redundant queries result in excessive human involvement. This paper addresses the often-overlooked aspect of query selection, which is closely related to active learning (AL). We propose a novel query selection approach that leverages variational autoencoder (VAE) representations of state sequences. In this manner, we formulate queries that are diverse in nature while simultaneously taking into account reward model estimations. We compare our approach to the current state-of-the-art query selection methods in preference-based RL, and find ours to be either on-par or more sample efficient through extensive benchmarking on simulated environments relevant to robotics. Lastly, we conduct an online study to verify the effectiveness of our query selection approach with real human feedback and examine several metrics related to human effort.

关键词：

来源：评论

学校读者我要写书评

暂无评论

High-precision Calibration of Camera and IMU on Manipulator for Bio-inspired Robotic System

引用

Journal of Bionic Engineering 2022年第2期19卷 299-313页

作者： Yinlong Zhang Wei Liang Sichao Zhang Xudong Yuan Xiaofang Xia Jindong Tan Zhibo Pang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China The School of Computer Science and Technology Xidian UniversityXi’an 710071China Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKonxville 37996USA Department of Automation Technology ABB Corporate Research Sweden72178 VästerasSweden

Inspired by box jellyfish that has distributed and complementary perceptive system,we seek to equip manipulator with a camera and an Inertial Measurement Unit(IMU)to perceive ego motion and surrounding unstructured *** robot perception,a reliable and high-precision calibration between camera,IMU and manipulator is a critical *** paper introduces a novel calibration ***,we seek to correlate the spatial relationship between the sensing units and manipulator in a joint ***,the manipulator moving trajectory is elaborately designed in a spiral pattern that enables full excitations on yaw-pitch-roll rotations and x-y-z translations in a repeatable and consistent *** calibration has been evaluated on our collected visual inertial-manipulator *** systematic comparisons and analysis indicate the consistency,precision and effectiveness of our proposed calibration method.

关键词： Bio-inspired robotic system Monocular camera IMU Manipulator Calibration

来源：评论

学校读者我要写书评

暂无评论

A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

引用

computer Systems science & Engineering 2024年第3期48卷 571-608页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Abdulatif Alabdulatif Ruba Abu Khurma Maribel Garcia-Arenas Pedro A.Castillo University Institute of Computing Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Chandigarh UniversityPunjab143001India Department of Computer Science and Engineering Uttaranchal UniversityUttarakhand248007India Department of Computer Science College of Computer Qassim UniversityBuraydah52571Saudi Arabia MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

As cloud computing usage grows,cloud data centers play an increasingly important *** maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance *** purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data *** aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management *** review revealed three task allocation research topics and seven performance management *** allocation research areas are resource allocation,load-Balancing,and *** management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network *** study proposes new techniques to enhance cloud computing work allocation and performance ***-comings in each approach can guide future *** research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and *** methodologies can steer future research to fill gaps in the literature.

关键词： Cloud computing data centre task allocation performance management resource utilization

来源：评论

学校读者我要写书评

暂无评论

Fourier Boundary Features Network with Wider Catchers for Glass Segmentation

arXiv

引用

arXiv 2024年

作者： Qin, Xiaolin Liu, Jiacen Wang, Qianlei Zhang, Shaolin Zhu, Fei Yi, Zhang Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610213 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences 999077 Hong Kong School of Computer Science Sichuan University Chengdu610065 China

Glass largely blurs the boundary between the real world and the reflection. The special transmittance and reflectance quality have confused the semantic tasks related to machine vision. Therefore, how to clear the boundary built by glass, and avoid over-capturing features as false positive information in deep structure, matters for constraining the segmentation of reflection surface and penetrating glass. We proposed the Fourier Boundary Features Network with Wider Catchers (FBWC), which might be the first attempt to utilize sufficiently wide horizontal shallow branches without vertical deepening for guiding the fine granularity segmentation boundary through primary glass semantic information. Specifically, we designed the Wider Coarse-Catchers (WCC) for anchoring large area segmentation and reducing excessive extraction from a structural perspective. We embed fine-grained features by Cross Transpose Attention (CTA), which is introduced to avoid the incomplete area within the boundary caused by reflection noise. For excavating glass features and balancing high-low layers context, a learnable Fourier Convolution Controller (FCC) is proposed to regulate information integration robustly. The proposed method has been validated on three different public glass segmentation datasets. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art (SOTA) methods in glass image segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Multi-Alpha Soft Actor-Critic: Overcoming Stochastic Biases in Maximum Entropy Reinforcement Learning

Multi-Alpha Soft Actor-Critic: Overcoming Stochastic Biases ...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Conor Igoe Swapnil Pande Siddarth Venkatraman Jeff Schneider Machine Learning Department School of Computer Science Carnegie Mellon University Pittsburgh PA United States Robotics Institute School of Computer Science Carnegie Mellon University Pittsburgh PA United States

The successful application of robotic control requires intelligent decision-making to handle the long tail of complex scenarios that arise in real-world environments. Recently, Deep Reinforcement Learning (DRL) has provided a data-driven framework to automatically learn effective policies in such complex settings. Since its introduction in 2018, Soft Actor-Critic (SAC) remains as one of the most popular off-policy DRL algorithms and has been used extensively to learn performant robotic control policies. However, in this paper we argue that by relying on the maximum entropy formalism to define learning objectives, previous work introduces a significant bias away from optimal decision making, which often requires near-deterministic behaviour for high-precision tasks. Moreover, we show that when training with the original variants of SAC, overcoming this bias by reducing entropy budgets or entropy coefficients introduces separate issues that lead to slow or unstable learning. We address these shortcomings by treating the entropy coefficient $\alpha$ as a random variable and introduce Multi-Alpha Soft Actor-Critic (MAS). We show how MAS overcomes the stochastic bias of SAC in a variety of robotic control tasks including the CARLA urban-driving simulator, while maintaining the stability and sample efficiency of the original algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Autoencoder-XGBoost Classifier (AeXGB) for Predicting Severity Level of Parkinson's Disease from Spontaneous Speech

Autoencoder-XGBoost Classifier (AeXGB) for Predicting Severi...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wai, Thiri Liao, Yu-Shan Liao, Ting-Yun Lin, Chin-Hsien Hung, Chi-Sheng Fu, Li-Chen National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Biomedical Engineering College of Medical Science and Technology Taipei Medical University National Taiwan University Hospital Bei-Hu Branch Department of Neurology Taipei Taiwan Institute of Molecular Medicine College of Medicine National Taiwan University Taipei Taiwan National Taiwan University Hospital and National Taiwan University College of Medicine Department of Internal Medicine Taipei Taiwan and Advanced Robotics Taiwan

ISBN: (纸本)9781665410205

Parkinson's disease (PD) is the cause of the gradual decline of nerve cells that control movement disorder disease, which is most common among the elderly in the US after Alzheimer's disease. There are several studies on detecting Parkinson's disease from speech using machine learning techniques;however, most of them focus on classifying healthy patients (HC) against Parkinson's disease (PD). This paper focuses on developing a screening system that could detect healthy (HC) vs. mild Parkinson's (MP) vs. severe Parkinson's (SP) from spontaneous speech. Four acoustic feature sets were compared for the screening system. Our proposed AeXGB was also compared with six different classifying approaches. The result has shown that extracting the phonation features and using AeXGB could achieve an accuracy of 92% for classifying HC vs. MP vs. SP, which outperforms the traditional machine learning approach for three class classifications of Parkinson's severity level from spontaneous speech. © 2024 IEEE.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Aligning Human Preferences with Baseline Objectives in Reinforcement Learning

Aligning Human Preferences with Baseline Objectives in Reinf...

引用

IEEE International Conference on robotics and Automation (ICRA)

Practical implementations of deep reinforcement learning (deep RL) have been challenging due to an amplitude of factors, such as designing reward functions that cover every possible interaction. To address the heavy burden of robot reward engineering, we aim to leverage subjective human preferences gathered in the context of human-robot interaction, while taking advantage of a baseline reward function when available. By considering baseline objectives to be designed beforehand, we are able to narrow down the policy space, solely requesting human attention when their input matters the most. To allow for control over the optimization of different objectives, our approach contemplates a multi-objective setting. We achieve human-compliant policies by sequentially training an optimal policy from a baseline specification and collecting queries on pairs of trajectories. These policies are obtained by training a reward estimator to generate Pareto optimal policies that include human preferred behaviours. Our approach ensures sample efficiency and we conducted a user study to collect real human preferences, which we utilized to obtain a policy on a social navigation environment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：