International Journal of Robotics Research最新文献_第8页

Sequential contact-based adaptive grasping for robotic hands 基于顺序接触的机器人手自适应抓取

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2022-04-01 DOI: 10.1177/02783649221081154

G. J. Pollayil, M. J. Pollayil, M. Catalano, A. Bicchi, G. Grioli

{"title":"Sequential contact-based adaptive grasping for robotic hands","authors":"G. J. Pollayil, M. J. Pollayil, M. Catalano, A. Bicchi, G. Grioli","doi":"10.1177/02783649221081154","DOIUrl":"https://doi.org/10.1177/02783649221081154","url":null,"abstract":"This paper proposes a novel type of grasping strategy that draws inspiration from the role of touch and the importance of wrist motions in human grasping. The proposed algorithm, which we call Sequential Contact-based Adaptive Grasping, can be used to reactively modify a given grasp plan according to contacts arising between the hand and the object. This technique, based on a systematic constraint categorization and an iterative task inversion procedure, is shown to lead to synchronized motions of the fingers and the wrist, as it can be observed in humans, and to increase grasp success rate by substantially mitigating the relevant problems of object slippage during hand closure and of uncertainties caused by the environment and by the perception system. After describing the grasping problem in its quasi-static aspects, the algorithm is derived and discussed with some simple simulations. The proposed method is general as it can be applied to different kinds of robotic hands. It refines a priori defined grasp plans and significantly reduces their accuracy requirements by relying only on a forward kinematic model and elementary contact information. The efficacy of our approach is confirmed by experimental results of tests performed on a collaborative robot manipulator equipped with a state-of-the-art underactuated soft hand.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46066998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 3

Boreas: A multi-season autonomous driving dataset Boreas:多季节自动驾驶数据集

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2022-03-18 DOI: 10.1177/02783649231160195

Keenan Burnett, David J. Yoon, Yuchen Wu, A. Z. Li, Haowei Zhang, Shichen Lu, Jingxing Qian, Wei-Kang Tseng, A. Lambert, K. Leung, Angela P. Schoellig, T. Barfoot

引用次数: 43

An efficient, modular controller for flapping flight composing model-based and model-free components 一种高效、模块化的扑翼飞行控制器，由基于模型和无模型的组件组成

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2022-03-15 DOI: 10.1177/02783649211063225

Avik De, Rebecca McGill, R. Wood

引用次数: 0

BenchBot environments for active robotics (BEAR): Simulated data for active scene understanding research 用于主动机器人的BenchBot环境（BEAR）：用于主动场景理解研究的模拟数据

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2022-03-01 DOI: 10.1177/02783649211069404

David Hall, Ben Talbot, S. Bista, Haoyang Zhang, Rohan Smith, Feras Dayoub, Niko Sünderhauf

{"title":"BenchBot environments for active robotics (BEAR): Simulated data for active scene understanding research","authors":"David Hall, Ben Talbot, S. Bista, Haoyang Zhang, Rohan Smith, Feras Dayoub, Niko Sünderhauf","doi":"10.1177/02783649211069404","DOIUrl":"https://doi.org/10.1177/02783649211069404","url":null,"abstract":"We present a platform to foster research in active scene understanding, consisting of high-fidelity simulated environments and a simple yet powerful API that controls a mobile robot in simulation and reality. In contrast to static, pre-recorded datasets that focus on the perception aspect of scene understanding, agency is a top priority in our work. We provide three levels of robot agency, allowing users to control a robot at varying levels of difficulty and realism. While the most basic level provides pre-defined trajectories and ground-truth localisation, the more realistic levels allow us to evaluate integrated behaviours comprising perception, navigation, exploration and SLAM. In contrast to existing simulation environments, we focus on robust scene understanding research using our environment interface (BenchBot) that provides a simple API for seamless transition between the simulated environments and real robotic platforms. We believe this scaffolded design is an effective approach to bridge the gap between classical static datasets without any agency and the unique challenges of robotic evaluation in reality. Our BenchBot Environments for Active Robotics (BEAR) consist of 25 indoor environments under day and night lighting conditions, a total of 1443 objects to be identified and mapped, and ground-truth 3D bounding boxes for use in evaluation. BEAR website: https://qcr.github.io/dataset/benchbot-bear-data/.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47712061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 3

AURORA, a multi-sensor dataset for robotic ocean exploration AURORA，一个用于机器人海洋探测的多传感器数据集

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2022-02-07 DOI: 10.1177/02783649221078612

Marco Bernardi, Brett Hosking, C. Petrioli, B. Bett, Daniel Jones, V. Huvenne, Rachel Marlow, M. Furlong, S. McPhail, A. Munafò

引用次数: 4

Inducing structure in reward learning by learning features 基于学习特征的奖励学习诱导结构

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2022-01-18 DOI: 10.1177/02783649221078031

Andreea Bobu, Marius Wiggert, C. Tomlin, A. Dragan

{"title":"Inducing structure in reward learning by learning features","authors":"Andreea Bobu, Marius Wiggert, C. Tomlin, A. Dragan","doi":"10.1177/02783649221078031","DOIUrl":"https://doi.org/10.1177/02783649221078031","url":null,"abstract":"Reward learning enables robots to learn adaptable behaviors from human input. Traditional methods model the reward as a linear function of hand-crafted features, but that requires specifying all the relevant features a priori, which is impossible for real-world tasks. To get around this issue, recent deep Inverse Reinforcement Learning (IRL) methods learn rewards directly from the raw state but this is challenging because the robot has to implicitly learn the features that are important and how to combine them, simultaneously. Instead, we propose a divide-and-conquer approach: focus human input specifically on learning the features separately, and only then learn how to combine them into a reward. We introduce a novel type of human input for teaching features and an algorithm that utilizes it to learn complex features from the raw state space. The robot can then learn how to combine them into a reward using demonstrations, corrections, or other reward learning frameworks. We demonstrate our method in settings where all features have to be learned from scratch, as well as where some of the features are known. By first focusing human input specifically on the feature(s), our method decreases sample complexity and improves generalization of the learned reward over a deep IRL baseline. We show this in experiments with a physical 7-DoF robot manipulator, and in a user study conducted in a simulated environment.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45495193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 16

Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization. 在室外范围传感器定位中使用高空图像作为地图的自监督学习。

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-12-01 Epub Date: 2021-09-28 DOI: 10.1177/02783649211045736

Tim Y Tang, Daniele De Martini, Shangzhe Wu, Paul Newman

{"title":"Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization.","authors":"Tim Y Tang, Daniele De Martini, Shangzhe Wu, Paul Newman","doi":"10.1177/02783649211045736","DOIUrl":"10.1177/02783649211045736","url":null,"abstract":"<p><p>Traditional approaches to outdoor vehicle localization assume a reliable, prior map is available, typically built using the same sensor suite as the on-board sensors used during localization. This work makes a different assumption. It assumes that an overhead image of the workspace is available and utilizes that as a map for use for range-based sensor localization by a vehicle. Here, range-based sensors are radars and lidars. Our motivation is simple, off-the-shelf, publicly available overhead imagery such as Google satellite images can be a ubiquitous, cheap, and powerful tool for vehicle localization when a usable prior sensor map is unavailable, inconvenient, or expensive. The challenge to be addressed is that overhead images are clearly not directly comparable to data from ground range sensors because of their starkly different modalities. We present a learned metric localization method that not only handles the modality difference, but is also cheap to train, learning in a self-supervised fashion without requiring metrically accurate ground truth. By evaluating across multiple real-world datasets, we demonstrate the robustness and versatility of our method for various sensor configurations in cross-modality localization, achieving localization errors on-par with a prior supervised approach while requiring no pixel-wise aligned ground truth for supervision at training. We pay particular attention to the use of millimeter-wave radar, which, owing to its complex interaction with the scene and its immunity to weather and lighting conditions, makes for a compelling and valuable use case.</p>","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8721700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39904384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 to solve sequential physical reasoning problems from a scene image 学习从场景图像中解决顺序物理推理问题

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-12-01 DOI: 10.1177/02783649211056967

Danny Driess, Jung-Su Ha, Marc Toussaint

{"title":"Learning to solve sequential physical reasoning problems from a scene image","authors":"Danny Driess, Jung-Su Ha, Marc Toussaint","doi":"10.1177/02783649211056967","DOIUrl":"https://doi.org/10.1177/02783649211056967","url":null,"abstract":"In this article, we propose deep visual reasoning, which is a convolutional recurrent neural network that predicts discrete action sequences from an initial scene image for sequential manipulation problems that arise, for example, in task and motion planning (TAMP). Typical TAMP problems are formalized by combining reasoning on a symbolic, discrete level (e.g., first-order logic) with continuous motion planning such as nonlinear trajectory optimization. The action sequences represent the discrete decisions on a symbolic level, which, in turn, parameterize a nonlinear trajectory optimization problem. Owing to the great combinatorial complexity of possible discrete action sequences, a large number of optimization/motion planning problems have to be solved to find a solution, which limits the scalability of these approaches. To circumvent this combinatorial complexity, we introduce deep visual reasoning: based on a segmented initial image of the scene, a neural network directly predicts promising discrete action sequences such that ideally only one motion planning problem has to be solved to find a solution to the overall TAMP problem. Our method generalizes to scenes with many and varying numbers of objects, although being trained on only two objects at a time. This is possible by encoding the objects of the scene and the goal in (segmented) images as input to the neural network, instead of a fixed feature vector. We show that the framework can not only handle kinematic problems such as pick-and-place (as typical in TAMP), but also tool-use scenarios for planar pushing under quasi-static dynamic models. Here, the image-based representation enables generalization to other shapes than during training. Results show runtime improvements of several orders of magnitudes by, in many cases, removing the need to search over the discrete action sequences.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49360154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 16

Robotics: Science and Systems (RSS) 2020 机器人:科学与系统(RSS) 2020

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-12-01 DOI: 10.1177/02783649211052346

T. Nanayakkara, T. Barfoot, T. Howard

引用次数: 2

Enabling impedance-based physical human–multi–robot collaboration: Experiments with four torque-controlled manipulators 实现基于阻抗的物理人-多机器人协作：四个扭矩控制机械手的实验

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-11-24 DOI: 10.1177/02783649211053650

Niels Dehio, Joshua Smith, D. L. Wigand, Pouya Mohammadi, M. Mistry, Jochen J. Steil

{"title":"Enabling impedance-based physical human–multi–robot collaboration: Experiments with four torque-controlled manipulators","authors":"Niels Dehio, Joshua Smith, D. L. Wigand, Pouya Mohammadi, M. Mistry, Jochen J. Steil","doi":"10.1177/02783649211053650","DOIUrl":"https://doi.org/10.1177/02783649211053650","url":null,"abstract":"Robotics research into multi-robot systems so far has concentrated on implementing intelligent swarm behavior and contact-less human interaction. Studies of haptic or physical human-robot interaction, by contrast, have primarily focused on the assistance offered by a single robot. Consequently, our understanding of the physical interaction and the implicit communication through contact forces between a human and a team of multiple collaborative robots is limited. We here introduce the term Physical Human Multi-Robot Collaboration (PHMRC) to describe this more complex situation, which we consider highly relevant in future service robotics. The scenario discussed in this article covers multiple manipulators in close proximity and coupled through physical contacts. We represent this set of robots as fingers of an up-scaled agile robot hand. This perspective enables us to employ model-based grasping theory to deal with multi-contact situations. Our torque-control approach integrates dexterous multi-manipulator grasping skills, optimization of contact forces, compensation of object dynamics, and advanced impedance regulation into a coherent compliant control scheme. For this to achieve, we contribute fundamental theoretical improvements. Finally, experiments with up to four collaborative KUKA LWR IV+ manipulators performed both in simulation and real world validate the model-based control approach. As a side effect, we notice that our multi-manipulator control framework applies identically to multi-legged systems, and we execute it also on the quadruped ANYmal subject to non-coplanar contacts and human interaction.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49246810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 10