检索结果-内蒙古大学图书馆

2025 IEEE International Conference on Simulation, Modeling, and Programming for autonomous Robots, SIMPAR 2025

作者： Bacchin, Alberto Marangoni, Fabio Gottardi, Alberto Menegatti, Emanuele University of Padova Intelligent Autonomous System Lab Department of Information Engineering Padua35131 Italy IT+Robotics Srl Vicenza36100 Italy

ISBN: (纸本)9798331516857

The growing volumes of solid waste present a significant challenge for sustainable management. An efficient robotics system for sorting waste materials is essential to improving recycling and contamination removal, but it is often limited by the extreme variation of items to recognize in a cluttered and dirty environment. To bring robots in such scenario, the system must be able to recognize and manipulate different objects, adapting to a high degree of variability. A system based on deep learning can achieve high performance and fulfill these requirements. However, learning models require extensive labeled data, which limits their applicability in this context. Indeed, the complexity of the real-world environment presents a significant challenge to effective data collection, highlighting the importance of data augmentation techniques for creating a suitable dataset for training models for object recognition in this context. To address this challenge, our study investigates the use of Generative Adversarial Networks (GANs) for synthetic data generation in waste-sorting systems. GANs are employed to produce synthetic images of waste streams with corresponding labels that accurately reflect the complexity and diversity of real-world waste. A primary con-cern in synthetic data generation is ensuring alignment between generated images and their labels. To address this, we introduce a novel method for controlling the GAN generation process, which enforces semantic coherence and preserves the intended structure of the labeled data. The experiments demonstrate that semantic segmentation models trained on datasets augmented with these synthetic images perform better in the semantic segmentation of waste. © 2025 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Learning to Model Diverse Driving Behaviors in Highly Interactive autonomous Driving Scenarios With Multiagent Reinforcement Learning

引用

IEEE Systems Journal 2025年第1期19卷 317-326页

作者： Liu, Weiwei Hu, Wenxuan Jing, Wei Lei, Lanxin Gao, Lingping Liu, Yong Zhejiang University The Advanced Perception on Robotics and Intelligent Learning Lab College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Zhejiang University Zhejiang 310027 China Alibaba DAMO Academy Autonomous Driving Lab Zhejiang 311121 China Huzhou University College of Information Engineering Zhejiang 313000 China

autonomous vehicles trained through multiagent reinforcement learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually operate under the assumption of fully cooperative behavior among all agents and focus on maximizing team rewards during training. To address this issue, we introduce the personality modeling network (PeMN), which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios. The PeMN also enables the training of a background traffic flow with diverse behaviors, thereby improving the performance and generalization of the ego vehicle. Our extensive experimental studies, which incorporate different personality parameters in high-interactive driving scenarios, demonstrate that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization than traditional MARL methods. © 2007-2012 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Image Data Augmentation through Generative Adversarial Networks for Waste Sorting

Image Data Augmentation through Generative Adversarial Netwo...

引用

IEEE International Conference on Simulation, Modeling, and Programming for autonomous Robots (SIMPAR)

作者： Alberto Bacchin Fabio Marangoni Alberto Gottardi Emanuele Menegatti Department of Information Engineering Intelligent Autonomous System Lab University of Padova Padua Italy IT+Robotics srl Vicenza Italy

ISBN: (数字)9798331516857

ISBN: (纸本)9798331516864

关键词： Waste materials Semantic segmentation Semantics Diversity reception Generative adversarial networks Data augmentation Generators Data models Sorting Synthetic data

来源：评论

学校读者我要写书评

暂无评论

FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matching

arXiv

引用

arXiv 2025年

作者： Nguyen, Khang Le, An T. Pham, Tien Huber, Manfred Peters, Jan Vu, Minh Nhat Learning and Adaptive Robotics Lab University of Texas Arlington United States Intelligent Autonomous Systems Lab TU Darmstadt Germany Cognitive Robotics Lab University of Manchester United Kingdom SAIROL Darmstadt Germany Hessian.AI Darmstadt Germany TU Wien Vienna Austria GmbH Vienna Austria

Prior flow matching methods in robotics have primarily learned velocity fields to morph one distribution of trajectories into another. In this work, we extend flow matching to capture second-order trajectory dynamics, incorporating acceleration effects either explicitly in the model or implicitly through the learning objective. Unlike diffusion models, which rely on a noisy forward process and iterative denoising steps, flow matching trains a continuous transformation (flow) that directly maps a simple prior distribution to the target trajectory distribution without any denoising procedure. By modeling trajectories with second-order dynamics, our approach ensures that generated robot motions are smooth and physically executable, avoiding the jerky or dynamically infeasible trajectories that first-order models might produce. We empirically demonstrate that this second-order conditional flow matching yields superior performance on motion planning benchmarks, achieving smoother trajectories and higher success rates than baseline planners. These findings highlight the advantage of learning acceleration-aware motion fields, as our method outperforms existing motion planning methods in terms of trajectory quality and planning success. Copyright © 2025, The Authors. All rights reserved.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Enabling Service Robots to Open Self-Closing Doors using Deep RL and Generative Models

Research Square

引用

Research Square 2025年

作者： Sun, Yufeng Zhang, Lin Ma, Ou Intelligent Robotics and Autonomous Systems Lab University of Cincinnati CincinnatiOH45221 United States Department of Physics and Astronomy University of Central Arkansas 201 Donaghey Avenue ConwayAR72035 United States

Various mobile service robots have been applied in human-centered indoor environments such as hospitals, schools, workshops, and office buildings for tasks like food or material delivery, surveillance, cleaning, and disinfection. However, they still require human assistance to access rooms with doors, particularly self-closing doors. In this paper, we propose a deep reinforcement learning (DRL)-based solution to enhance existing service robots, enabling them to autonomously open self-closing doors using a simple and inexpensive assistive device. This device is utilized to unlatch doors through the integration of the latest object detection and sensor technologies. Focusing on pulling open self-closing doors, we train an end-to-end control policy in a simplified simulation environment and deploy it to a real-world robot. Additionally, generative models, a variational autoenoder (VAE) and a cycle-consistent adversarial network (CycleGAN), are incorporated into both the training and deployment stages to improve the robustness of the door-opening control policy. The proposed solution for opening self-closing doors was validated in both simulation and real-world experiments. © 2025, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A 3-Step Optimization Framework with Hybrid Models for a Humanoid Robot's Jump Motion

arXiv

引用

arXiv 2025年

作者： Qi, Haoxiang Yu, Zhangguo Chen, Xuechao Liu, Yaliang Yi, Chuanku Dong, Chencheng Meng, Fei Huang, Qiang The Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China The International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing100081 China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China

High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a humanoid robot. To improve iteration speed and achieve ideal performance, the framework comprises three sub-optimizations. The first optimization incorporates momentum, inertia, and center of pressure (CoP), treating the robot as a static reaction momentum pendulum (SRMP) model to generate corresponding trajectories. The second optimization maps these trajectories to joint space using effective Quadratic Programming (QP) solvers. Finally, the third optimization generates whole-body joint trajectories utilizing trajectories generated by previous parts. With the combined consideration of momentum and inertia, the robot achieves agile forward jump motions. A simulation and experiments (Fig. 1) of forward jump with a distance of 1.0 m and 0.5 m height are presented in this paper, validating the applicability of the proposed framework. Note to Practitioners-The motivation of this paper stems from the need to improve jumping performance of humanoid robots. By comprehensively considering factors such as robot posture, centroidal angular momentum, and landing foot placement, the algorithm enhances the robot's ability to navigate complex environments. This capability is crucial for applications that require overcoming obstacles, such as in search and rescue or inspection tasks. Improved jumping ability can significantly boost environmental adaptability, allowing robots to perform effectively in diverse conditions, and it also represents an exploration of the high-dynamic motion capabilities of humanoid robots. Future research will focus on integrating visual and perceptual information to enhance decision-making. Copyright © 2025, The Authors. All rights reserve

关键词： Anthropomorphic robots

来源：评论

学校读者我要写书评

暂无评论

Extrinsic RGB-D camera calibration for legged robots

Extrinsic RGB-D camera calibration for legged robots

引用

14th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2011

作者： Hoepflinger, Mark A. David Remy, C. Hutter, Marco Siegwart, Roland Autonomous Systems Lab Institute of Robotics and Intelligent Systems Zurich Switzerland

ISBN: (纸本)981437427X

This paper describes a method to identify the extrinsic parameters of RGB-D cameras mounted on legged robots. Since the calculation of the parameters is based on the detection of the robots feet in the camera images, no special calibration objects are required. Therefore the method is simple to use and can even be applied automatically. Experiments demonstrate the precision and the robustness of the method. © 2012 by World Scientific Publishing Co. Pte. Ltd.

关键词： Calibration

来源：评论

学校读者我要写书评

暂无评论

High compliant series elastic actuation for the robotic leg scarleth

High compliant series elastic actuation for the robotic leg ...

引用

14th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2011

作者： Hutter, Marco David Remy, C. Hoepflinger, Mark A. Siegwart, Roland Autonomous Systems Lab Institute of Robotics and Intelligent Systems Zurich Switzerland

ISBN: (纸本)981437427X

This paper presents the actuation system of the robotic leg ScarlETH. It was developed specifically for a quadrupedal robot and is designed to achieve fast position control as well as accurate joint torque control. It introduces strong passive dynamics to create an efficient running behavior. High spring compliance with low damping in combination with a cascaded, motor velocity based, control structure was successfully tested in simulation and experiments. Final tests with the entire leg demonstrate that the system can perform a hopping motion providing only positive actuator power. © 2012 by World Scientific Publishing Co. Pte. Ltd.

关键词： Position control

来源：评论

学校读者我要写书评

暂无评论

Probabilistic contextual situation analysis

引用

作者： Ramel, Guy Siegwart, Roland Autonomous Systems Lab. Ecole Polytechnique Fédérale de Lausanne Autonomous Systems Lab. Institute of Robotics and Intelligent Systems ETH Zürich

ISBN: (纸本)9783540790068

Mobile robots are gradually appearing in our daily environment. To navigate autonomously in real-world environments and interact with objects and humans, robots face various major technological challenges. Among the required key competencies of such robots is their ability to perceive the environment and reason about it, to plan appropriate actions. However, sensory information perceived from real-world situations is error prone and incomplete and thus often results in ambiguous interpretations. We propose a new approach for object recognition that incorporates visual and range information with spatial arrangement between objects (context information). It is based on using Bayesian networks to fuse and infer information from different *** the proposed framework, we first extract potential objects from the scene image using simple features- characteristics like colour or the relation between height and width. This basic information is easy to extract but often results in ambiguous situations between similar objects. To resolve ambiguities among the detected objects, the relative spatial arrangement (context information) of the objects is used in a second step. Consider, for example, a cola can and a red trash can that are both cylindrical, have similar ratios between width and height and have very similar colours. Depending on their distances from the robot, they may be hard to distinguish. However, if we further consider their spatial arrangement with other objects, e.g. a table, they might be clearly differentiable, the cola can typically standing on the table and the trash can on the floor. This contextual information is therefore a very efficient way to increase drastically the reliability of object recognition and scene interpretation. Moreover, range information from a laser scanner and speech recognition offer complementary information to improve reliability further. Thus, an approach using laser range data to recognize places (such as corridors, crossings

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

OverlapNet: Loop Closing for LiDAR-based SLAM 16th

OverlapNet: Loop Closing for LiDAR-based SLAM

引用

16th robotics: Science and Systems, RSS 2020

作者： Chen, Xieyuanli Vysotska, Olga Läbe, Thomas Haag, Alexandre Milioto, Andres Behley, Jens Röhling, Timo Stachniss, Cyrill Photogrammetry & Robotics Lab University of Bonn Germany Fraunhofer FKIE Wachtberg Germany Autonomous Intelligent Driving GmbH Munich Germany

ISBN: (纸本)9780992374761

Simultaneous localization and mapping (SLAM) is a fundamental capability required by most autonomous systems. In this paper, we address the problem of loop closing for SLAM based on 3D laser scans recorded by autonomous cars. Our approach utilizes a deep neural network exploiting different cues generated from LiDAR data for finding loop closures. It estimates an image overlap generalized to range images and provides a relative yaw angle estimate between pairs of scans. Based on such predictions, we tackle loop closure detection and integrate our approach into an existing SLAM system to improve its mapping results. We evaluate our approach on sequences of the KITTI odometry benchmark and the Ford campus dataset. We show that our method can effectively detect loop closures surpassing the detection performance of state-of-the-art methods. To highlight the generalization capabilities of our approach, we evaluate our model on the Ford campus dataset while using only KITTI for training. The experiments show that the learned representation is able to provide reliable loop closure candidates, also in unseen environments. © 2020, MIT Press Journals. All rights reserved.

关键词： Deep neural networks

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：