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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jin Xu De'Aira G. Bryant Ayanna Howard Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA USA School of Interactive Computing. Georgia Institute of Technology Atlanta GA USA

With the recent advances in computing, artificial intelligence (AI) is quickly becoming a key component in the future of advanced applications. In one application in particular, AI has played a major role - that of revolutionizing traditional healthcare assistance. Using embodied interactive agents, or interactive robots, in healthcare scenarios has emerged as an innovative way to interact with patients. As an essential factor for interpersonal interaction, trust plays a crucial role in establishing and maintaining a patient-agent relationship. In this paper, we discuss a study related to healthcare in which we examine aspects of trust between humans and interactive robots during a therapy intervention in which the agent provides corrective feedback. A total of twenty participants were randomly assigned to receive corrective feedback from either a robotic agent or a human agent. Survey results indicate trust in a therapy intervention coupled with a robotic agent is comparable to that of trust in an intervention coupled with a human agent. Results also show a trend that the agent condition has a medium-sized effect on trust. In addition, we found that participants in the robot therapist condition are 3.5 times likely to have trust involved in their decision than the participants in the human therapist condition. These results indicate that the deployment of interactive robot agents in healthcare scenarios has the potential to maintain quality of health for future generations.

关键词： Robots Games Medical treatment Atmospheric measurements Particle measurements Artificial intelligence

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Investigating the Relationship between Believability and Presence during a Collaborative Cognitive Task with a Socially interactive Robot

Investigating the Relationship between Believability and Pre...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jin Xu Ayanna Howard Institute for Robotics and Intelligent Machines Georgia Institute ofd Technology Atlanta GA USA School of Interactive Computing Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781538679814

The use of socially interactive robots in people's homes and workplaces will continue to grow. To test such systems before deployment, most human-robot interaction experiments are usually conducted in a research laboratory or require the physical presence of the robot. These experiments are usually time-consuming, costly, and, in most cases, require an experimenter to be present. To address this problem, we have developed a web-based research platform in which participants can remotely interact with a socially interactive robot shown via video based on a behavior profile programmed via a Wizard-of-Oz framework. To help us validate the believability when using this platform, we compared participant's responses to a physical presence robot with that to a remote presence robot during a problem-solving scenario. Participants were asked to complete a series of cognitive tasks with the help of the robot. We analyzed participant's tendency to take the robot's advice, their feelings before and after the experiment, and a quantitative assessment about believability. The experimental results indicate that there was a high degree of agreement between participants' behaviors in the two conditions and only minor changes in feelings before and after the experiment. The survey responses found no significant difference in their overall rating for believability, suggesting the equivalency of believability between the two conditions.

关键词： Task analysis Games Robot sensing systems Human-robot interaction Feeds Collaboration

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Robust learning of tactile force estimation through robot interaction

arXiv

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arXiv 2018年

作者： Sundaralingam, Balakumar Lambert, Alexander Handa, Ankur Boots, Byron Hermans, Tucker Birchfield, Stan Ratliff, Nathan Fox, Dieter NVIDIA United States University of Utah Robotics Center School of Computing University of Utah Salt Lake CityUT United States Institute for Robotics and Intelligent Machines Georgia Institute of Technology GA United States University of Washington Paul G. Allen School for Comupter Science & Engineering SeattleWA United States

Current methods for estimating force from tactile sensor signals are either inaccurate analytic models or task-specific learned models. In this paper, we explore learning a robust model that maps tactile sensor signals to force. We specifically explore learning a mapping for the SynTouch BioTac sensor via neural networks. We propose a voxelized input feature layer for spatial signals and leverage information about the sensor surface to regularize the loss function. To learn a robust tactile force model that transfers across tasks, we generate ground truth data from three different sources: (1) the BioTac rigidly mounted to a force torque (FT) sensor, (2) a robot interacting with a ball rigidly attached to the same FT sensor, and (3) through force inference on a planar pushing task by formalizing the mechanics as a system of particles and optimizing over the object motion. A total of 140k samples were collected from the three sources. We achieve a median angular accuracy of 3.5 degrees in predicting force direction (66% improvement over the current state of the art) and a median magnitude accuracy of 0.06 N (93% improvement) on a test dataset. Additionally, we evaluate the learned force model in a force feedback grasp controller performing object lifting and gentle placement. Our results can be found on https://***/view/tactile-force. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistical tests

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Work those Arms: Toward Dynamic and Stable Humanoid Walking that Optimizes Full-Body Motion

Work those Arms: Toward Dynamic and Stable Humanoid Walking ...

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IEEE International Conference on robotics and Automation

作者： Christian M. Hubicki Ayonga Hereid Michael X. Grey Andrea L. Thomaz Aaron D. Ames the Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332 USA the Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA 30332 USA the School of Interactive Computing Georgia Institute of Technology Atlanta GA 30332 USA the Woodruff School of Mechanical Engineering and the School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 USA

ISBN: (纸本)9781467380270

Humanoid robots are designed with dozens of actuated joints to suit a variety of tasks, but walking controllers rarely make the best use of all of this freedom. We present a framework for maximizing the use of the full humanoid body for the purpose of stable dynamic locomotion, which requires no restriction to a planning template (e.g. LIPM). Using a hybrid zero dynamics (HZD) framework, this approach optimizes a set of outputs which provides requirements for the motion for all actuated links, including arms. These output equations are then rapidly solved by a whole-body inverse-kinematic (IK) solver, providing a set of joint trajectories to the robot. We apply this procedure to a simulation of the humanoid robot, DRC-HUBO, which has over 27 actuators. As a consequence, the resulting gaits swing their arms, not by a user defining swinging motions a priori or superimposing them on gaits post hoc, but as an emergent behavior from optimizing the dynamic gait. We also present preliminary dynamic walking experiments with DRC-HUBO in hardware, thereby building a case that hybrid zero dynamics as augmented by inverse kinematics (HZD+IK) is becoming a viable approach for controlling the full complexity of humanoid locomotion.

关键词： controlling full complexity humanoid locomotion

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Decoupling behavior, perception, and control for autonomous learning of affordances

Decoupling behavior, perception, and control for autonomous ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Tucker Hermans James M. Rehg Aaron F. Bobick Center for Robotics and Intelligent Machines and The School of Interactive Computing Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781467356404

A novel behavior representation is introduced that permits a robot to systematically explore the best methods by which to successfully execute an affordance-based behavior for a particular object. The approach decomposes affordance-based behaviors into three components. We first define controllers that specify how to achieve a desired change in object state through changes in the agent's state. For each controller we develop at least one behavior primitive that determines how the controller outputs translate to specific movements of the agent. Additionally we provide multiple perceptual proxies that define the representation of the object that is to be computed as input to the controller during execution. A variety of proxies may be selected for a given controller and a given proxy may provide input for more than one controller. When developing an appropriate affordance-based behavior strategy for a given object, the robot can systematically vary these elements as well as note the impact of additional task variables such as location in the workspace. We demonstrate the approach using a PR2 robot that explores different combinations of controller, behavior primitive, and proxy to perform a push or pull positioning behavior on a selection of household objects, learning which methods best work for each object.

关键词： Grippers End effectors Planning Adaptive control Vectors Feedback control

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Learning contact locations for pushing and orienting unknown objects

Learning contact locations for pushing and orienting unknown...

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IEEE-RAS International Conference on Humanoid Robots

作者： Tucker Hermans Fuxin Li James M. Rehg Aaron F. Bobick Center for Robotics and Intelligent Machines and The School of Interactive Computing Georgia Institute of Technology Atlanta GA USA Center for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA USA

We present a method by which a robot learns to predict effective contact locations for pushing as a function of object shape. The robot performs push experiments at many contact locations on multiple objects and records local and global shape features at each point of contact. Each trial attempts to either push the object in a straight line or to rotate the object to a new orientation. The robot observes the outcome trajectories of the manipulations and computes either a push-stability or rotate-push score for each trial. The robot then learns a regression function for each score in order to predict push effectiveness as a function of object shape. With this mapping, the robot can infer effective push locations for subsequent objects from their shapes, regardless of whether they belong to a previously encountered object class. These results are demonstrated on a mobile manipulator robot pushing a variety of household objects on a tabletop surface.

关键词： Robots Shape Trajectory Feature extraction Histograms Kernel Training

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The motion grammar: Linguistic perception, planning, and control

The motion grammar: Linguistic perception, planning, and con...

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International Conference on robotics Science and Systems, RSS 2011

作者： Dantam, Neil Stilman, Mike School of Interactive Computing Center for Robotics and Intelligent Machines Georgia Institute of Technology 801 Atlantic Dr. NW AtlantaGA30332 United States

ISBN: (纸本)9780262517799

We present and analyze the Motion Grammar: a novel unified representation for task decomposition, perception, planning, and control that provides both fast online control of robots in uncertain environments and the ability to guarantee completeness and correctness. The grammar represents a policy for the task which is parsed in real-time based on perceptual input. Branches of the syntax tree form the levels of a hierarchical decomposition, and the individual robot sensor readings are given by tokens. We implement this approach in the interactive game of Yamakuzushi on a physical robot resulting in a system that repeatably competes with a human opponent in sustained gameplay for the roughly six minute duration of each match.

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Finding graph topologies for feasible multirobot motion planning

Finding graph topologies for feasible multirobot motion plan...

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2012 IEEE/RSJ International Conference on intelligent Robots and Systems

作者： Pushkar Kolhe Henrik I. Christensen Center for Robotics and Intelligent Machines School of Interactive Computing at the Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)9781467317375

In this paper we present a design methodology for mapping the configuration space of multirobot systems in a warehouse. The method generates a tree topology with robot and terminal nodes. In the most general case multirobot motion planning problems are NP-complete. But, we show that pebble motion problems in the resultant tree topology are always feasible. Our method uses an integer programming formulation to find such a solution.

关键词： Robots Topology Junctions Planning Linear programming Steiner trees Network topology

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Guided Pushing for Object Singulation

Guided Pushing for Object Singulation

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IEEE/RSJ International Conference on intelligent robotics and Systems

作者： Tucker Hermans James M. Rehg Aaron Bobick Center for Robotics and Intelligent Machines and The School of Interactive Computing Georgia Institute of Technology Atlanta GA.

ISBN: (纸本)9781467317375

We propose a novel method for a robot to separate and segment objects in a cluttered tabletop environment. The method leverages the fact that external object boundaries produce visible edges within an object cluster. We achieve this singulation of objects by using the robot arm to perform pushing actions specifically selected to test whether particular visible edges correspond to object boundaries. We verify the separation of objects after a push by examining the clusters formed by geometric segmentation of regions residing on the table surface. To avoid explicitly representing and tracking edges across push behaviors we aggregate over all edges in a given orientation by representing the push-history as an orientation histogram. By tracking the history of directions pushed for each object cluster we can build evidence that a cluster cannot be further separated. We present quantitative and qualitative experimental results performed in a real home environment by a mobile manipulator using input from an RGB-D camera mounted on the robot's head. We show that our pushing strategy can more reliably obtain singulation in fewer pushes than an approach, that does not explicitly reason about boundary information.

关键词： reason pushes environment

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