Autonomous Robots最新文献_第3页

Editorial - Robotics: Science and Systems 2022 社论 - 机器人：科学与系统 2022

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-05-03 DOI: 10.1007/s10514-024-10161-9

引用次数: 0

Adaptive hybrid local–global sampling for fast informed sampling-based optimal path planning 通过自适应知情采样加速基于采样的最优路径规划

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-04-20 DOI: 10.1007/s10514-024-10157-5

Marco Faroni, Nicola Pedrocchi, Manuel Beschi

引用次数: 0

Reinforcement learning with imitative behaviors for humanoid robots navigation: synchronous planning and control 仿人机器人导航的模仿行为强化学习：同步规划与控制

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-04-17 DOI: 10.1007/s10514-024-10160-w

Xiaoying Wang, Tong Zhang

{"title":"Reinforcement learning with imitative behaviors for humanoid robots navigation: synchronous planning and control","authors":"Xiaoying Wang, Tong Zhang","doi":"10.1007/s10514-024-10160-w","DOIUrl":"10.1007/s10514-024-10160-w","url":null,"abstract":"<div><p>Humanoid robots have strong adaptability to complex environments and possess human-like flexibility, enabling them to perform precise farming and harvesting tasks in varying depths of terrains. They serve as essential tools for agricultural intelligence. In this article, a novel method was proposed to improve the robustness of autonomous navigation for humanoid robots, which intercommunicates the data fusion of the footprint planning and control levels. In particular, a deep reinforcement learning model - Proximal Policy Optimization (PPO) that has been fine-tuned is introduced into this layer, before which heuristic trajectory was generated based on imitation learning. In the RL period, the KL divergence between the agent’s policy and imitative expert policy as a value penalty is added to the advantage function. As a proof of concept, our navigation policy is trained in a robotic simulator and then successfully applied to the physical robot <i>GTX</i> for indoor multi-mode navigation. The experimental results conclude that incorporating imitation learning imparts anthropomorphic attributes to robots and facilitates the generation of seamless footstep patterns. There is a significant improvement in ZMP trajectory in y-direction from the center by 21.56% is noticed. Additionally, this method improves dynamic locomotion stability, the body attitude angle falling between less than ± 5.5<span>(^circ )</span> compared to ± 48.4<span>(^circ )</span> with traditional algorithm. In general, navigation error is below 5 cm, which we verified in the experiments. It is thought that the outcome of the proposed framework presented in this article can provide a reference for researchers studying autonomous navigation applications of humanoid robots on uneven ground.\u0000</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140608698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Terrain traversability prediction through self-supervised learning and unsupervised domain adaptation on synthetic data 通过合成数据上的自监督学习和无监督域适应进行地形可穿越性预测

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-03-30 DOI: 10.1007/s10514-024-10158-4

Giuseppe Vecchio, Simone Palazzo, Dario C. Guastella, Daniela Giordano, Giovanni Muscato, Concetto Spampinato

{"title":"Terrain traversability prediction through self-supervised learning and unsupervised domain adaptation on synthetic data","authors":"Giuseppe Vecchio, Simone Palazzo, Dario C. Guastella, Daniela Giordano, Giovanni Muscato, Concetto Spampinato","doi":"10.1007/s10514-024-10158-4","DOIUrl":"10.1007/s10514-024-10158-4","url":null,"abstract":"<div><p>Terrain traversability estimation is a fundamental task for supporting robot navigation on uneven surfaces. Recent learning-based approaches for predicting traversability from RGB images have shown promising results, but require manual annotation of a large number of images for training. To address this limitation, we present a method for traversability estimation on unlabeled videos that combines dataset synthesis, self-supervision and unsupervised domain adaptation. We pose the traversability estimation as a vector regression task over vertical bands of the observed frame. The model is pre-trained through self-supervision to reduce the distribution shift between synthetic and real data and encourage shared feature learning. Then, supervised training on synthetic videos is carried out, while employing an unsupervised domain adaptation loss to improve its generalization capabilities on real scenes. Experimental results show that our approach is on par with standard supervised training, and effectively supports robot navigation without the need of manual annotations. Training code and synthetic dataset will be publicly released at: https://github.com/perceivelab/traversability-synth.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-024-10158-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140364755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Maximal coverage problems with routing constraints using cross-entropy Monte Carlo tree search 利用交叉熵蒙特卡洛树搜索解决具有路由限制的最大覆盖问题

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-01-30 DOI: 10.1007/s10514-024-10156-6

Pao-Te Lin, Kuo-Shih Tseng

引用次数: 0

Collocation methods for second and higher order systems 二阶和高阶系统的搭配方法

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-01-28 DOI: 10.1007/s10514-023-10155-z

Siro Moreno-Martín, Lluís Ros, Enric Celaya

{"title":"Collocation methods for second and higher order systems","authors":"Siro Moreno-Martín, Lluís Ros, Enric Celaya","doi":"10.1007/s10514-023-10155-z","DOIUrl":"10.1007/s10514-023-10155-z","url":null,"abstract":"<div><p>It is often unnoticed that the predominant way to use collocation methods is fundamentally flawed when applied to optimal control in robotics. Such methods assume that the system dynamics is given by a first order ODE, whereas robots are often governed by a second or higher order ODE involving configuration variables and their time derivatives. To apply a collocation method, therefore, the usual practice is to resort to the well known procedure of casting an <i>M</i>th order ODE into <i>M</i> first order ones. This manipulation, which in the continuous domain is perfectly valid, leads to inconsistencies when the problem is discretized. Since the configuration variables and their time derivatives are approximated with polynomials of the same degree, their differential dependencies cannot be fulfilled, and the actual dynamics is not satisfied, not even at the collocation points. This paper draws attention to this problem, and develops improved versions of the trapezoidal and Hermite–Simpson collocation methods that do not present these inconsistencies. In many cases, the new methods reduce the dynamics transcription error in one order of magnitude, or even more, without noticeably increasing the cost of computing the solutions.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10155-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139579306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boosting the hospital by integrating mobile robotic assistance systems: a comprehensive classification of the risks to be addressed 通过整合移动机器人辅助系统促进医院发展：应对风险的全面分类

IF 3.7 3区计算机科学

Autonomous Robots Pub Date : 2024-01-24 DOI: 10.1007/s10514-023-10154-0

Lukas Bernhard, Patrik Schwingenschlögl, Jörg Hofmann, Dirk Wilhelm, Alois Knoll

{"title":"Boosting the hospital by integrating mobile robotic assistance systems: a comprehensive classification of the risks to be addressed","authors":"Lukas Bernhard, Patrik Schwingenschlögl, Jörg Hofmann, Dirk Wilhelm, Alois Knoll","doi":"10.1007/s10514-023-10154-0","DOIUrl":"10.1007/s10514-023-10154-0","url":null,"abstract":"<div><p>Mobile service robots are a promising technology for supporting workflows throughout the hospital. Combined with an understanding of the environment and the current situation, such systems have the potential to become invaluable tools for overcoming personal shortages and streamlining healthcare workflows. However, few robotic systems have actually been translated to practical application so far, which is due to many challenges centered around the strict and unique requirements imposed by the different hospital environments, which have not yet been collected and analyzed in a structured manner. To address this need, we now present a comprehensive classification of different dimensions of risk to be considered when designing mobile service robots for the hospital. Our classification consists of six risk categories – environmental complexity, hygienic requirements, interaction with persons and objects, workflow flexibility and autonomy – for each of which a scale with distinct risk levels is provided. This concept, for the first time allows for a precise classification of mobile service robots for the hospital, which can prove useful for certification and admission procedures as well as for defining architectural and safety requirements throughout the design process of such robots.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10154-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139558975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic task allocation approaches for coordinated exploration of Subterranean environments 地下环境协同勘探的动态任务分配方法

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-11-23 DOI: 10.1007/s10514-023-10142-4

Matthew O’Brien, Jason Williams, Shengkang Chen, Alex Pitt, Ronald Arkin, Navinda Kottege

引用次数: 1

AuRo special issue on large language models in robotics guest editorial AuRo关于机器人中的大型语言模型的特刊客座编辑

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-11-17 DOI: 10.1007/s10514-023-10153-1

引用次数: 0

TidyBot: personalized robot assistance with large language models TidyBot:具有大型语言模型的个性化机器人辅助

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-11-16 DOI: 10.1007/s10514-023-10139-z

Jimmy Wu, Rika Antonova, Adam Kan, Marion Lepert, Andy Zeng, Shuran Song, Jeannette Bohg, Szymon Rusinkiewicz, Thomas Funkhouser

{"title":"TidyBot: personalized robot assistance with large language models","authors":"Jimmy Wu, Rika Antonova, Adam Kan, Marion Lepert, Andy Zeng, Shuran Song, Jeannette Bohg, Szymon Rusinkiewicz, Thomas Funkhouser","doi":"10.1007/s10514-023-10139-z","DOIUrl":"10.1007/s10514-023-10139-z","url":null,"abstract":"<div><p>For a robot to personalize physical assistance effectively, it must learn user preferences that can be generally reapplied to future scenarios. In this work, we investigate personalization of household cleanup with robots that can tidy up rooms by picking up objects and putting them away. A key challenge is determining the proper place to put each object, as people’s preferences can vary greatly depending on personal taste or cultural background. For instance, one person may prefer storing shirts in the drawer, while another may prefer them on the shelf. We aim to build systems that can learn such preferences from just a handful of examples via prior interactions with a particular person. We show that robots can combine language-based planning and perception with the few-shot summarization capabilities of large language models to infer generalized user preferences that are broadly applicable to future interactions. This approach enables fast adaptation and achieves 91.2% accuracy on unseen objects in our benchmark dataset. We also demonstrate our approach on a real-world mobile manipulator called TidyBot, which successfully puts away 85.0% of objects in real-world test scenarios.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138473086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 68