Autonomous Robots最新文献_第7页

Towards neuromorphic FPGA-based infrastructures for a robotic arm 面向基于神经形态FPGA的机械臂基础设施

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-07-14 DOI: 10.1007/s10514-023-10111-x

Salvador Canas-Moreno, Enrique Piñero-Fuentes, Antonio Rios-Navarro, Daniel Cascado-Caballero, Fernando Perez-Peña, Alejandro Linares-Barranco

{"title":"Towards neuromorphic FPGA-based infrastructures for a robotic arm","authors":"Salvador Canas-Moreno, Enrique Piñero-Fuentes, Antonio Rios-Navarro, Daniel Cascado-Caballero, Fernando Perez-Peña, Alejandro Linares-Barranco","doi":"10.1007/s10514-023-10111-x","DOIUrl":"10.1007/s10514-023-10111-x","url":null,"abstract":"<div><p>Muscles are stretched with bursts of spikes that come from motor neurons connected to the cerebellum through the spinal cord. Then, alpha motor neurons directly innervate the muscles to complete the motor command coming from upper biological structures. Nevertheless, classical robotic systems usually require complex computational capabilities and relative high-power consumption to process their control algorithm, which requires information from the robot’s proprioceptive sensors. The way in which the information is encoded and transmitted is an important difference between biological systems and robotic machines. Neuromorphic engineering mimics these behaviors found in biology into engineering solutions to produce more efficient systems and for a better understanding of neural systems. This paper presents the application of a Spike-based Proportional-Integral-Derivative controller to a 6-DoF Scorbot ER-VII robotic arm, feeding the motors with Pulse-Frequency-Modulation instead of Pulse-Width-Modulation, mimicking the way in which motor neurons act over muscles. The presented frameworks allow the robot to be commanded and monitored locally or remotely from both a Python software running on a computer or from a spike-based neuromorphic hardware. Multi-FPGA and single-PSoC solutions are compared. These frameworks are intended for experimental use of the neuromorphic community as a testbed platform and for dataset recording for machine learning purposes.\u0000</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10111-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45151320","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

Learning rewards from exploratory demonstrations using probabilistic temporal ranking 使用概率时间排序从探索性演示中学习奖励

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-07-10 DOI: 10.1007/s10514-023-10120-w

Michael Burke, Katie Lu, Daniel Angelov, Artūras Straižys, Craig Innes, Kartic Subr, Subramanian Ramamoorthy

{"title":"Learning rewards from exploratory demonstrations using probabilistic temporal ranking","authors":"Michael Burke, Katie Lu, Daniel Angelov, Artūras Straižys, Craig Innes, Kartic Subr, Subramanian Ramamoorthy","doi":"10.1007/s10514-023-10120-w","DOIUrl":"10.1007/s10514-023-10120-w","url":null,"abstract":"<div><p>Informative path-planning is a well established approach to visual-servoing and active viewpoint selection in robotics, but typically assumes that a suitable cost function or goal state is known. This work considers the inverse problem, where the goal of the task is unknown, and a reward function needs to be inferred from exploratory example demonstrations provided by a demonstrator, for use in a downstream informative path-planning policy. Unfortunately, many existing reward inference strategies are unsuited to this class of problems, due to the exploratory nature of the demonstrations. In this paper, we propose an alternative approach to cope with the class of problems where these sub-optimal, exploratory demonstrations occur. We hypothesise that, in tasks which require discovery, successive states of any demonstration are progressively more likely to be associated with a higher reward, and use this hypothesis to generate time-based binary comparison outcomes and infer reward functions that support these ranks, under a probabilistic generative model. We formalise this <i>probabilistic temporal ranking</i> approach and show that it improves upon existing approaches to perform reward inference for autonomous ultrasound scanning, a novel application of learning from demonstration in medical imaging while also being of value across a broad range of goal-oriented learning from demonstration tasks.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10120-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48400024","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

Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning 基于可微语义映射和规划的自主导航逆强化学习

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-07-06 DOI: 10.1007/s10514-023-10118-4

Tianyu Wang, Vikas Dhiman, Nikolay Atanasov

{"title":"Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning","authors":"Tianyu Wang, Vikas Dhiman, Nikolay Atanasov","doi":"10.1007/s10514-023-10118-4","DOIUrl":"10.1007/s10514-023-10118-4","url":null,"abstract":"<div><p>This paper focuses on inverse reinforcement learning for autonomous navigation using distance and semantic category observations. The objective is to infer a cost function that explains demonstrated behavior while relying only on the expert’s observations and state-control trajectory. We develop a map encoder, that infers semantic category probabilities from the observation sequence, and a cost encoder, defined as a deep neural network over the semantic features. Since the expert cost is not directly observable, the model parameters can only be optimized by differentiating the error between demonstrated controls and a control policy computed from the cost estimate. We propose a new model of expert behavior that enables error minimization using a closed-form subgradient computed only over a subset of promising states via a motion planning algorithm. Our approach allows generalizing the learned behavior to new environments with new spatial configurations of the semantic categories. We analyze the different components of our model in a minigrid environment. We also demonstrate that our approach learns to follow traffic rules in the autonomous driving CARLA simulator by relying on semantic observations of buildings, sidewalks, and road lanes.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10118-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44835312","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: 3

AGRI-SLAM: a real-time stereo visual SLAM for agricultural environment 农业环境实时立体视觉SLAM

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-07-04 DOI: 10.1007/s10514-023-10110-y

Rafiqul Islam, Habibullah Habibullah, Tagor Hossain

{"title":"AGRI-SLAM: a real-time stereo visual SLAM for agricultural environment","authors":"Rafiqul Islam, Habibullah Habibullah, Tagor Hossain","doi":"10.1007/s10514-023-10110-y","DOIUrl":"10.1007/s10514-023-10110-y","url":null,"abstract":"<div><p>In this research, we proposed a stereo visual simultaneous localisation and mapping (SLAM) system that efficiently works in agricultural scenarios without compromising the performance and accuracy in contrast to the other state-of-the-art methods. The proposed system is equipped with an image enhancement technique for the ORB point and LSD line features recovery, which enables it to work in broader scenarios and gives extensive spatial information from the low-light and hazy agricultural environment. Firstly, the method has been tested on the standard dataset, i.e., KITTI and EuRoC, to validate the localisation accuracy by comparing it with the other state-of-the-art methods, namely VINS-SLAM, PL-SLAM, and ORB-SLAM2. The experimental results evidence that the proposed method obtains superior localisation and mapping accuracy than the other visual SLAM methods. Secondly, the proposed method is tested on the ROSARIO dataset, our low-light agricultural dataset, and O-HAZE dataset to validate the performance in agricultural environments. In such cases, while other methods fail to operate in such complex agricultural environments, our method successfully operates with high localisation and mapping accuracy.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10110-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45660238","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: 1

On robot grasp learning using equivariant models 基于等变模型的机器人抓取学习

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-07-04 DOI: 10.1007/s10514-023-10112-w

Xupeng Zhu, Dian Wang, Guanang Su, Ondrej Biza, Robin Walters, Robert Platt

引用次数: 0

TNES: terrain traversability mapping, navigation and excavation system for autonomous excavators on worksite TNES：用于现场自动挖掘机的地形可穿越性测绘、导航和挖掘系统

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-07-04 DOI: 10.1007/s10514-023-10113-9

Tianrui Guan, Zhenpeng He, Ruitao Song, Liangjun Zhang

{"title":"TNES: terrain traversability mapping, navigation and excavation system for autonomous excavators on worksite","authors":"Tianrui Guan, Zhenpeng He, Ruitao Song, Liangjun Zhang","doi":"10.1007/s10514-023-10113-9","DOIUrl":"10.1007/s10514-023-10113-9","url":null,"abstract":"<div><p>We present a terrain traversability mapping and navigation system (TNS) for autonomous excavator applications in an unstructured environment. We use an efficient approach to extract terrain features from RGB images and 3D point clouds and incorporate them into a global map for planning and navigation. Our system can adapt to changing environments and update the terrain information in real-time. Moreover, we present a novel dataset, the Complex Worksite Terrain dataset, which consists of RGB images from construction sites with seven categories based on navigability. Our novel algorithms improve the mapping accuracy over previous methods by 4.17–30.48<span>(%)</span> and reduce MSE on the traversability map by 13.8–71.4<span>(%)</span>. We have combined our mapping approach with planning and control modules in an autonomous excavator navigation system and observe <span>(49.3%)</span> improvement in the overall success rate. Based on TNS, we demonstrate the first autonomous excavator that can navigate through unstructured environments consisting of deep pits, steep hills, rock piles, and other complex terrain features. In addition, we combine the proposed TNS with the autonomous excavation system (AES), and deploy the new pipeline, TNES, on a more complex construction site. With minimum human intervention, we demonstrate autonomous navigation capability with excavation tasks.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41582747","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

Complex environment localization system using complementary ceiling and ground map information 利用互补的天花板和地面地图信息的复杂环境定位系统

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-06-28 DOI: 10.1007/s10514-023-10116-6

Chee-An Yu, Hao-Yun Chen, Chun-Chieh Wang, Li-Chen Fu

{"title":"Complex environment localization system using complementary ceiling and ground map information","authors":"Chee-An Yu, Hao-Yun Chen, Chun-Chieh Wang, Li-Chen Fu","doi":"10.1007/s10514-023-10116-6","DOIUrl":"10.1007/s10514-023-10116-6","url":null,"abstract":"<div><p>This paper proposes a robust localization system using complementary information extracted from ceiling and ground plans, particularly applicable to dynamic and complex environments. The ceiling perception provides the robot with stable and time-invariant environmental features independent of the dynamic changes on the ground, whereas the ground perception allows the robot to navigate in the ground plane while avoiding stationary obstacles. We propose an architecture to fuse ground 2D LiDAR scan and ceiling 3D LiDAR scan with our enhanced mapping algorithm associating perception from both sources efficiently. The localization ability and the navigation performance can be promisingly secured even in a harsh environment with our complementary sensed information from the ground and ceiling. The salient feature of our work is that our system can simultaneously map both the ceiling and ground plane efficiently without extra efforts of deploying articulated landmarks and apply such hybrid information effectively, which facilitates the robot to travel through any indoor environment with human crowds without getting lost.</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10116-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41898236","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

Event-based neural learning for quadrotor control 基于事件的四旋翼控制神经学习

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-06-23 DOI: 10.1007/s10514-023-10115-7

Estéban Carvalho, Pierre Susbielle, Nicolas Marchand, Ahmad Hably, Jilles S. Dibangoye

引用次数: 0

Learning latent representations to co-adapt to humans 学习潜在表征以共同适应人类

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-06-17 DOI: 10.1007/s10514-023-10109-5

Sagar Parekh, Dylan P. Losey

{"title":"Learning latent representations to co-adapt to humans","authors":"Sagar Parekh, Dylan P. Losey","doi":"10.1007/s10514-023-10109-5","DOIUrl":"10.1007/s10514-023-10109-5","url":null,"abstract":"<div><p>When robots interact with humans in homes, roads, or factories the human’s behavior often changes in response to the robot. Non-stationary humans are challenging for robot learners: actions the robot has learned to coordinate with the original human may fail after the human adapts to the robot. In this paper we introduce an algorithmic formalism that enables robots (i.e., ego agents) to <i>co-adapt</i> alongside dynamic humans (i.e., other agents) using only the robot’s low-level states, actions, and rewards. A core challenge is that humans not only react to the robot’s behavior, but the way in which humans react inevitably changes both over time and between users. To deal with this challenge, our insight is that—instead of building an exact model of the human–robots can learn and reason over <i>high-level representations</i> of the human’s policy and policy dynamics. Applying this insight we develop RILI: Robustly Influencing Latent Intent. RILI first embeds low-level robot observations into predictions of the human’s latent strategy and strategy dynamics. Next, RILI harnesses these predictions to select actions that influence the adaptive human towards advantageous, high reward behaviors over repeated interactions. We demonstrate that—given RILI’s measured performance with users sampled from an underlying distribution—we can probabilistically bound RILI’s expected performance across new humans sampled from the same distribution. Our simulated experiments compare RILI to state-of-the-art representation and reinforcement learning baselines, and show that RILI better learns to coordinate with imperfect, noisy, and time-varying agents. Finally, we conduct two user studies where RILI co-adapts alongside actual humans in a game of tag and a tower-building task. See videos of our user studies here: https://youtu.be/WYGO5amDXbQ</p></div>","PeriodicalId":55409,"journal":{"name":"Autonomous Robots","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10514-023-10109-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45856995","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: 4

A learning-based approach to surface vehicle dynamics modeling for robust multistep prediction 基于学习的地面车辆动力学建模鲁棒多步预测方法

IF 3.5 3区计算机科学

Autonomous Robots Pub Date : 2023-06-14 DOI: 10.1007/s10514-023-10114-8

Junwoo Jang, Changyu Lee, Jinwhan Kim

引用次数: 0