International Journal of Robotics Research最新文献

{"title":"Selected papers from ISRR'2019","authors":"T. Asfour, Jaeheung Park, E. Yoshida","doi":"10.1177/02783649231180249","DOIUrl":"https://doi.org/10.1177/02783649231180249","url":null,"abstract":"This special issue is organized by selected papers presented at International Symposium on Robotics Research (ISRR 2019), held onOctober 6–10, 2019, at Hanoi, Vietnam. The conference enjoyed the distinguished talks by renowned invited speakers as well as active discussions at poster presentations of contributed papers on recent research. Following the strong relationship of the ISRR conferences with IJRR since its early years, the Program Chair and Co-Chairs proposed this special issue to the editorial board as guest editors and invited authors of contributed papers that received high review scores among many highquality papers to submit extended versions of the work. The guest editors would like to express their sincere gratitude to the authors for their patience by the review process that was slowed down due to the difficulties caused by the worldwide pandemic and are very delighted this special issue is finally complete. The special issue reflects the recent advancement of artificial intelligence and autonomy in different areas of robotics, but also includes the fundamental contributions that have been representing the tradition of ISRR, forming a good mixture covering broad areas of robotics research despite the small number of papers.While the conferencewas held 3 years back, we believe the special issue offers an excellent collection including updates with recent results. We have two papers that benefit from advanced research of artificial intelligence and autonomous agents especially useful for high-level planning. The first paper “Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations” by Adam Pacheck, Steven James, George Konidaris, and Hadas Kress-Gazit presents skill-based framework enabling execution of reactive high-level tasks, by encoding robot skills with learned abstract sensor data to find a feasible plan for the task to be achieved. The second paper entitled “Multilevel Monte Carlo for Solving POMDPs Online” by Marcus Hoerger, Hanna Kurniawati, and Alberto Elfes addresses the challenging planning problem of Partially Observable Markov Decision Process (POMDP) with complex nonlinear dynamics, through an original approach of multi-level POMDP planner to obtain near-optimal solutions efficiently. The robotics fundamental contributions are about algorithmic foundation for manipulation and innovative robotics design and two papers are included for each topic. “The Blindfolded Traveler’s Problem: A Search Framework for Motion Planning with Contact Estimates\" by Bradley Saund, Sanjiban Choudhury, Siddhartha Srinivasa, and Dmitry Berenson tackles planning on a graph containing unknown validity by novel policy-belief combination that expresses the collision probability and expert hypothesis about the collision at the same time. The secondmanipulation study is “The Certified Grasping” by Bernardo Aceituno-Cabezas, Jose Ballester, and Alberto Rodriguez. This paper proposes the idea of certificates","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"42 1","pages":"149 - 149"},"PeriodicalIF":9.2,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49048863","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}

{"title":"Modelify: An approach to incrementally build 3D object models for map completion","authors":"Fadri Furrer, Tonci Novkovic, M. Fehr, Margarita Grinvald, César Cadena, Juan I. Nieto, R. Siegwart","doi":"10.1177/02783649231166977","DOIUrl":"https://doi.org/10.1177/02783649231166977","url":null,"abstract":"The capabilities of discovering new knowledge and updating the previously acquired one are crucial for deploying autonomous robots in unknown and changing environments. Spatial and objectness concepts are at the basis of several robotic functionalities and are part of the intuitive understanding of the physical world for us humans. In this paper, we propose a method, which we call Modelify, to incrementally map the environment at the level of objects in a consistent manner. We follow an approach where no prior knowledge of the environment is required. The only assumption we make is that objects in the environment are separated by concave boundaries. The approach works on an RGB-D camera stream, where object-like segments are extracted and stored in an incremental database. Segment description and matching are performed by exploiting 2D and 3D information, allowing to build a graph of all segments. Finally, a matching score guides a Markov clustering algorithm to merge segments, thus completing object representations. Our approach allows creating single (merged) instances of repeating objects, objects that were observed from different viewpoints, and objects that were observed in previous mapping sessions. Thanks to our matching and merging strategies this also works with only partially overlapping segments. We perform evaluations on indoor and outdoor datasets recorded with different RGB-D sensors and show the benefit of using a clustering method to form merge candidates and keypoints detected in both 2D and 3D. Our new method shows better results than previous approaches while being significantly faster. A newly recorded dataset and the source code are released with this publication.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"42 1","pages":"45 - 65"},"PeriodicalIF":9.2,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41976633","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}

{"title":"Online and offline learning of player objectives from partial observations in dynamic games","authors":"Lasse Peters, Vicencc Rubies-Royo, C. Tomlin, L. Ferranti, Javier Alonso-Mora, C. Stachniss, David Fridovich-Keil","doi":"10.1177/02783649231182453","DOIUrl":"https://doi.org/10.1177/02783649231182453","url":null,"abstract":"Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scenarios in which agents have individual objectives and interactions evolve over time. However, a key limitation of such techniques is that they require a priori knowledge of all players’ objectives. In this work, we address this issue by proposing a novel method for learning players’ objectives in continuous dynamic games from noise-corrupted, partial state observations. Our approach learns objectives by coupling the estimation of unknown cost parameters of each player with inference of unobserved states and inputs through Nash equilibrium constraints. By coupling past state estimates with future state predictions, our approach is amenable to simultaneous online learning and prediction in receding horizon fashion. We demonstrate our method in several simulated traffic scenarios in which we recover players’ preferences, for, e.g. desired travel speed and collision-avoidance behavior. Results show that our method reliably estimates game-theoretic models from noise-corrupted data that closely matches ground-truth objectives, consistently outperforming state-of-the-art approaches.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":" ","pages":""},"PeriodicalIF":9.2,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44849379","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}

{"title":"A sampling and learning framework to prove motion planning infeasibility","authors":"Sihui Li, Neil T. Dantam","doi":"10.1177/02783649231154674","DOIUrl":"https://doi.org/10.1177/02783649231154674","url":null,"abstract":"We present a learning-based approach to prove infeasibility of kinematic motion planning problems. Sampling-based motion planners are effective in high-dimensional spaces but are only probabilistically complete. Consequently, these planners cannot provide a definite answer if no plan exists, which is important for high-level scenarios, such as task-motion planning. We apply data generated during multi-directional sampling-based planning (such as PRM) to a machine learning approach to construct an infeasibility proof. An infeasibility proof is a closed manifold in the obstacle region of the configuration space that separates the start and goal into disconnected components of the free configuration space. We train the manifold using common machine learning techniques and then triangulate the manifold into a polytope to prove containment in the obstacle region. Under assumptions about the hyper-parameters and robustness of configuration space optimization, the output is either an infeasibility proof or a motion plan in the limit. We demonstrate proof construction for up to 4-DOF configuration spaces. A large part of the algorithm is parallelizable, which offers potential to address higher dimensional configuration spaces.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":" ","pages":""},"PeriodicalIF":9.2,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48377278","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}

{"title":"Alternating direction method of multipliers-based distributed control for distributed manipulation by shaping physical force fields","authors":"Martin Gurtner, J. Zemánek, Z. Hurák","doi":"10.1177/02783649231153958","DOIUrl":"https://doi.org/10.1177/02783649231153958","url":null,"abstract":"This paper proposes an algorithm for decomposing and possibly distributing an optimization problem that naturally emerges in distributed manipulation by shaping physical force fields through actuators distributed in space (arrays of actuators). One or several manipulated objects located in this field can “feel the force” and move simultaneously and independently. The control system has to produce commands for all actuators so that desired forces are developed at several prescribed places. This can be formulated as an optimization problem that has to be solved in every sampling period. Exploiting the structure of the optimization problem is crucial for platforms with many actuators and many manipulated objects, hence the goal of decomposing the huge optimization problem into several subproblems. Furthermore, if the platform is composed of interconnected actuator modules with computational capabilities, the decomposition can give guidance for the distribution of the computation to the modules. We propose an algorithm for decomposing/distributing the optimization problem using Alternating Direction Method of Multipliers (ADMM). The proposed algorithm is shown to converge to modest accuracy for various distributed platforms in a few iterations. We demonstrate our algorithm through numerical experiments corresponding to three physical experimental platforms for distributed manipulation using electric, magnetic, and pressure fields. Furthermore, we deploy and test it on real experimental platforms for distributed manipulation using an array of solenoids and ultrasonic transducers.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"42 1","pages":"3 - 20"},"PeriodicalIF":9.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43243936","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}

{"title":"Robotics Research","authors":"","doi":"10.1007/978-3-031-25555-7","DOIUrl":"https://doi.org/10.1007/978-3-031-25555-7","url":null,"abstract":"","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"45 1","pages":""},"PeriodicalIF":9.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87174781","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}

{"title":"Impact of colchicine on mortality and morbidity in COVID-19: a systematic review.","authors":"Devang Sanghavi, Pankaj Bansal, Ikwinder Preet Kaur, Mohsin Sheraz Mughal, Chandana Keshavamurthy, Austin Cusick, Jennifer Schram, Siva Naga S Yarrarapu, Abhishek R Giri, Nirmaljot Kaur, Pablo Moreno Franco, Andy Abril, Fawad Aslam","doi":"10.1080/07853890.2021.1993327","DOIUrl":"10.1080/07853890.2021.1993327","url":null,"abstract":"<p><strong>Introduction: </strong>Colchicine, because of its anti-inflammatory and possible anti-viral properties, has been proposed as potential therapeutic option for COVID-19. The role of colchicine to mitigate \"cytokine storm\" and to decrease the severity and mortality associated with COVID-19 has been evaluated in many studies.</p><p><strong>Objective: </strong>To evaluate the role of colchicine on morbidity and mortality in COVID-19 patients.</p><p><strong>Methods: </strong>This systematic review was conducted in accordance with the PRISMA recommendations. The literature search was conducted in 6 medical databases from inception to February 17, 2021 to identify studies evaluating colchicine as a therapeutic agent in COVID-19. All included studies were evaluated for risk of bias (ROB) using the Revised Cochrane ROB tool for randomised controlled trials (RCTs) and Newcastle-Ottawa Scale (NOS) for case-control and cohort studies.</p><p><strong>Results: </strong>Four RCTs and four observational studies were included in the final analysis. One study evaluated colchicine in outpatients, while all others evaluated inpatient use of colchicine. There was significant variability in treatment protocols for colchicine and standard of care in all studies. A statistically significant decrease in all-cause mortality was observed in three observational studies. The risk of mechanical ventilation was significantly reduced only in one observational study. Length of hospitalisation was significantly reduced in two RCTs. Risk for hospitalisation was not significantly decreased in the study evaluating colchicine in outpatients. Very few studies had low risk of bias.</p><p><strong>Conclusion: </strong>Based on the available data, colchicine shall not be recommended to treat COVID-19. Further high-quality and multi-center RCTs are required to assess the meaningful impact of this drug in COVID-19.KEY MESSAGESColchicine, an anti-inflammatory agent has demonstrated anti-viral properties in in-vitro studies by degrading the microtubules, as well as by inhibiting the production of pro-inflammatory cytokines.Colchicine has been studied as a potential therapeutic option for COVID-19, with variable results.Until further research can establish the efficacy of colchicine in COVID-19, the use of colchicine in COVID-19 shall be restricted to clinical trials.</p>","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"42 1","pages":"775-789"},"PeriodicalIF":4.9,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8920395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90414311","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}

{"title":"Continuum robot state estimation using Gaussian process regression on S E ( 3 )","authors":"S. Lilge, T. Barfoot, J. Burgner-Kahrs","doi":"10.1177/02783649221128843","DOIUrl":"https://doi.org/10.1177/02783649221128843","url":null,"abstract":"Continuum robots have the potential to enable new applications in medicine, inspection, and countless other areas due to their unique shape, compliance, and size. Excellent progress has been made in the mechanical design and dynamic modeling of continuum robots, to the point that there are some canonical designs, although new concepts continue to be explored. In this paper, we turn to the problem of state estimation for continuum robots that can been modeled with the common Cosserat rod model. Sensing for continuum robots might comprise external camera observations, embedded tracking coils, or strain gauges. We repurpose a Gaussian process (GP) regression approach to state estimation, initially developed for continuous-time trajectory estimation in SE(3). In our case, the continuous variable is not time but arclength and we show how to estimate the continuous shape (and strain) of the robot (along with associated uncertainties) given discrete, noisy measurements of both pose and strain along the length. We demonstrate our approach quantitatively through simulations as well as through experiments. Our evaluations show that accurate and continuous estimates of a continuum robot’s shape can be achieved, resulting in average end-effector errors between the estimated and ground truth shape as low as 3.5 mm and 0.016° in simulation or 3.3 mm and 0.035° for unloaded configurations and 6.2 mm and 0.041° for loaded ones during experiments, when using discrete pose measurements.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"41 1","pages":"1099 - 1120"},"PeriodicalIF":9.2,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49387090","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}

{"title":"Constraint-consistent task-oriented whole-body robot formulation: Task, posture, constraints, multiple contacts, and balance","authors":"O. Khatib, Mikael Jorda, Jaeheung Park, L. Sentis, S. Chung","doi":"10.1177/02783649221120029","DOIUrl":"https://doi.org/10.1177/02783649221120029","url":null,"abstract":"We present a comprehensive formulation to the problem of controlling a high-dimensional robotic system involving complex tasks subject to a variety of constraints, obstacles, balance, and contact challenges. Using intuitive and natural representations, the approach is initiated by establishing individual objectives for a task and its constraints. Simple independent controllers using artificial potential fields are then designed for each objective to reach goals while enforcing the constraints. Dynamically consistent projections in nullspaces associated with task and constraint representations are employed to deliver a coherent whole-body robot control. In multi-link multi-contact tasks, contact forces produce both resulting and internal forces. Internal forces play a critical role in robot balance and stability, achieved in this framework through modeling and controlling virtual linkages that explicitly describe the relationship between active/passive contact force, resultant force, controlled/uncontrolled internal force for multi-link multi-contact underactuated robots. Control of contacts with the environment involves material considerations such as friction and geometric constraints. Potential barriers direct the selection of contact forces ensuring stability and balance. This approach of dynamic projection and the Virtual Linkage Model addresses robot underactuation. In addition, the framework introduces a coordinate completion mechanism to establish a generalized coordinates representation of the task, removing redundancy and maintaining the full operational space dynamics description. This enables task-space dynamic control based on the relevant inertial properties. We present the experimental validation on a physical humanoid platform.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"41 1","pages":"1079 - 1098"},"PeriodicalIF":9.2,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42654951","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}

{"title":"MassMIND: Massachusetts Maritime INfrared Dataset","authors":"Shailesh Nirgudkar, M. Defilippo, Michael Sacarny, M. Benjamin, P. Robinette","doi":"10.1177/02783649231153020","DOIUrl":"https://doi.org/10.1177/02783649231153020","url":null,"abstract":"Recent advances in deep learning technology have triggered radical progress in the autonomy of ground vehicles. Marine coastal Autonomous Surface Vehicles (ASVs) that are regularly used for surveillance, monitoring, and other routine tasks can benefit from this autonomy. Long haul deep sea transportation activities are additional opportunities. These two use cases present very different terrains—the first being coastal waters—with many obstacles, structures, and human presence while the latter is mostly devoid of such obstacles. Variations in environmental conditions are common to both terrains. Robust labeled datasets mapping such terrains are crucial in improving the situational awareness that can drive autonomy. However, there are only limited such maritime datasets available and these primarily consist of optical images. Although, long wave infrared (LWIR) is a strong complement to the optical spectrum that helps in extreme light conditions, a labeled public dataset with LWIR images does not currently exist. In this paper, we fill this gap by presenting a labeled dataset of over 2900 LWIR segmented images captured in coastal maritime environment over a period of 2 years. The images are labeled using instance segmentation and classified into seven categories—sky, water, obstacle, living obstacle, bridge, self, and background. We also evaluate this dataset across three deep learning architectures (UNet, PSPNet, DeepLabv3) and provide detailed analysis of its efficacy. While the dataset focuses on the coastal terrain, it can equally help deep sea use cases. Such terrain would have less traffic, and the classifier trained on cluttered environment would be able to handle sparse scenes effectively. We share this dataset with the research community with the hope that it spurs new scene understanding capabilities in the maritime environment.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":"42 1","pages":"21 - 32"},"PeriodicalIF":9.2,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41850155","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}