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Object Placement Planning and optimization for Robot Manipulators

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Object Placement Planning and optimization for Robot Manipul...

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Joshua A. Haustein Kaiyu Hang Johannes Stork Danica Kragic Division of Robotics Perception and Learning (RPL) CAS CSC KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical Engineering and Material Science Yale University New Haven USA Center for Applied Autonomous Sensor Systems (AASS) Örebro University Örebro Sweden

We address the problem of planning the placement of a rigid object with a dual-arm robot in a cluttered environment. In this task, we need to locate a collision-free pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot and c) optimizes a user-given placement objective. In addition, we need to select which robot arm to perform the placement with. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning with a novel hierarchical search for suitable placement poses. Our algorithm incrementally produces approach motions to stable placement poses, reaching placements with better objective as runtime progresses. We evaluate our approach for two different placement objectives, and observe its effectiveness even in challenging scenarios.

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Better faulty than sorry: Investigating social recovery strategies to minimize the impact of failure in human-robot interaction 1

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Better faulty than sorry: Investigating social recovery stra...

1st Workshop on Conversational Interruptions in Human-Agent Interactions, WCIHAI 2017

作者： Engelhardt, Sara Hansson, Emmeli Leite, Iolanda Department of Robotics Perception and Learning KTH Royal Institute of Technology Sweden

Failure happens in most social interactions, possibly even more so in interactions between a robot and a human. This paper investigates different failure recovery strategies that robots can employ to minimize the negative effect on people's perception of the robot. A between-subject Wizard-of-Oz experiment with 33 participants was conducted in a scenario where a robot and a human play a collaborative game. The interaction was mainly speech-based and controlled failures were introduced at specific moments. Three types of recovery strategies were investigated, one in each experimental condition: ignore (the robot ignores that a failure has occurred and moves on with the task), apology (the robot apologizes for failing and moves on) and problem-solving (the robot tries to solve the problem with the help of the human). Our results show that the apology-based strategy scored the lowest on measures such as likeability and perceived intelligence, and that the ignore strategy lead to better perceptions of perceived intelligence and animacy than the employed recovery strategies.

关键词： Recovery

Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement learning

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Rearrangement with Nonprehensile Manipulation Using Deep Rei...

IEEE International Conference on robotics and Automation (ICRA)

作者： Weihao Yuan Johannes A. Stork Danica Kragic Michael Y. Wang Kaiyu Hang Hong Kong University of Science and Technology HKUST Robotics Institute Robotics Perception and Learning Lab KTH Royal Institute of Technology Sweden Department of Electronic and Computer Engineering

Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on only visual feedback. For this, we model the task with rewards and train a deep Q-network. Our potential field-based heuristic exploration strategy reduces the amount of collisions which lead to suboptimal outcomes and we actively balance the training set to avoid bias towards poor examples. Our training process leads to quicker learning and better performance on the task as compared to uniform exploration and standard experience replay. We demonstrate empirical evidence from simulation that our method leads to a success rate of 85%, show that our system can cope with sudden changes of the environment, and compare our performance with human level performance.

关键词： Planning Task analysis Robots Tools Cameras Visualization Training

Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization

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arXiv 2018年

作者： Lundell, Jens Krug, Robert Schaffernicht, Erik Stoyanov, Todor Kyrki, Ville Intelligent Robotics Group Department of Electrical Engineering and Automation Aalto University Finland AASS Research Center Örebro University Sweden Robotics Learning and Perception lab KTH Royal Institute of Technology Sweden

Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

Universal, Open Source, Myoelectric Interface for Assistive Devices

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Universal, Open Source, Myoelectric Interface for Assistive ...

International Conference on Control, Automation, robotics and Vision

作者： Adam Naber Yiannis Karayiannidis Max Ortiz-Catalan Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Department of Robotics and Perception and Learning Royal Institute of Technology Stockholm Sweden Integrum AB Mölndal Sweden

We present an integrated, open-source platform for the control of assistive vehicles. The system is vehicle-agnostic and can be controlled using a myoelectric interface to translate muscle contractions into vehicular commands. A modular shared-control system was used to enhance safety and ease of use, and three collision avoidance systems were included and verified in both an included test platform and on a quadcopter operating in a simulated environment. Seven subjects performed the experiments and rated the user experience of the system under each of the provided collision avoidance systems with positive results. Qualitative tests with the quadcopter validated the proposed system and shared-control techniques. This open-source platform for shared control between humans and machines integrates decoding of motor volition with control engineering to expedite further investigation into the operation of mobile robots.

关键词： Collision avoidance Sensors Electromyography Robots Open source software Pattern recognition

Anticipating many futures: Online human motion prediction and synthesis for human-robot collaboration

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arXiv 2017年

作者： Bütepage, Judith Kjellström, Hedvig Kragic, Danica Department of Robotics Perception and Learning KTH Royal Institute of Technology Sweden

Fluent and safe interactions of humans and robots require both partners to anticipate the others’ actions. A common approach to human intention inference is to model specific trajectories towards known goals with supervised classifiers. However, these approaches do not take possible future movements into account nor do they make use of kinematic cues, such as legible and predictable motion. The bottleneck of these methods is the lack of an accurate model of general human motion. In this work, we present a conditional variational autoencoder that is trained to predict a window of future human motion given a window of past frames. Using skeletal data obtained from RGB depth images, we show how this unsupervised approach can be used for online motion prediction for up to 1660 ms. Additionally, we demonstrate online target prediction within the first 300-500 ms after motion onset without the use of target specific training data. The advantage of our probabilistic approach is the possibility to draw samples of possible future motions. Finally, we investigate how movements and kinematic cues are represented on the learned low dimensional manifold. Copyright © 2017, The Authors. All rights reserved.

关键词： Kinematics

Understanding metric-related pitfalls in image analysis validation

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

Flexible disaster response of tomorrow final presentation and evaluation of the CENTAURO system

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arXiv 2019年

作者： Klamt, Tobias Rodriguez, Diego Baccelliere, Lorenzo Chen, Xi Chiaradia, Domenico Cichon, Torben Gabardi, Maßimiliano Guria, Paolo Holmquist, Karl Kamedula, Malgorzata Karaoguz, Hakan Kashiri, Navvab Laurenzi, Arturo Lenz, Christian Leonardis, Daniele Hoffman, Enrico Mingo Muratore, Luca Pavlichenko, Dmytro Porcini, Francesco Ren, Zeyu Schilling, Fabian Schwarz, Max Solazzi, Maßimiliano Felsberg, Michael Frisoli, Antonio Gustmann, Michael Jensfelt, Patric Nordberg, Klas Roßmann, Jürgen Süß, Uwe Tsagarakis, Nikos G. Behnke, Sven Autonomous Intelligent Systems University of Bonn Germany Humanoids and Human-Centred Mechatronics Istituto Italiano di Tecnologia Genoa Italy Department of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden PERCRO Laboratory TeCIP Institute Sant'Anna School of Advanced Studies Pisa Italy Institute for Man-Machine Interaction RWTH Aachen University Germany Computer Vision Laboratory Linköping University Sweden Lausanne Switzerland Kerntechnische Hilfsdienst GmbH Karlsruhe Germany School of Electrical and Electronic Engineering University of Manchester United Kingdom

Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement learning

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arXiv 2018年

作者： Yuan, Weihao Stork, Johannes A. Kragic, Danica Wang, Michael Y. Hang, Kaiyu Department of Electronic and Computer Engineering Hong Kong University of Science and Technology and Hkust Robotics Institute Dept. of Mech. and Aerospace Engineering and the Department of Electronic and Computer Engineering Department of ComputerScience and Engineering and Hkust Institute for Advanced Study Perception and Learning Lab Centre for Autonomous Systems Kth Royal Institute of Technology Sweden

关键词： Deep learning