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  • 1.
    Cruciani, Silvia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH Royal Inst Technol, Div Robot Percept & Learning, EECS, S-11428 Stockholm, Sweden..
    Sundaralingam, Balakumar
    Univ Utah, Robot Ctr, Salt Lake City, UT 84112 USA.;Univ Utah, Sch Comp, Salt Lake City, UT 84112 USA..
    Hang, Kaiyu
    Yale Univ, Dept Mech Engn & Mat Sci, New Haven, CT 06520 USA..
    Kumar, Vikash
    Google AI, San Francisco, CA 94110 USA..
    Hermans, Tucker
    Univ Utah, Robot Ctr, Salt Lake City, UT 84112 USA.;Univ Utah, Sch Comp, Salt Lake City, UT 84112 USA.;NVIDIA Res, Santa Clara, CA USA..
    Kragic, Danica
    KTH, Superseded Departments (pre-2005), Numerical Analysis and Computer Science, NADA. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH Royal Inst Technol, Div Robot Percept & Learning, EECS, S-11428 Stockholm, Sweden..
    Benchmarking In-Hand Manipulation2020In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 5, no 2, p. 588-595Article in journal (Refereed)
    Abstract [en]

    The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems. The goal is to assess the systems ability to change the pose of a hand-held object by either using the fingers, environment or a combination of both. Given an object surface mesh from the YCB data-set, we provide examples of initial and goal states (i.e. static object poses and fingertip locations) for various in-hand manipulation tasks. We further propose metrics that measure the error in reaching the goal state from a specific initial state, which, when aggregated across all tasks, also serves as a measure of the systems in-hand manipulation capability. We provide supporting software, task examples, and evaluation results associated with the benchmark.

  • 2.
    Tannure, Nayara C.
    et al.
    Univ Sao Paulo, Lab Biol Conservacao Mamiferos Aquat LABCMA, Inst Oceanog, Sao Paulo, Brazil..
    Barbosa, Fernando S.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Barcellos, Diogo D.
    Univ Sao Paulo, Lab Biol Conservacao Mamiferos Aquat LABCMA, Inst Oceanog, Sao Paulo, Brazil..
    Mattiuzzo, Beatriz
    Univ Sao Paulo, Lab Biol Conservacao Mamiferos Aquat LABCMA, Inst Oceanog, Sao Paulo, Brazil..
    Martinelli, Amanda
    Univ Sao Paulo, Lab Biol Conservacao Mamiferos Aquat LABCMA, Inst Oceanog, Sao Paulo, Brazil..
    Campos, Laura B.
    Univ Sao Paulo, Lab Biol Conservacao Mamiferos Aquat LABCMA, Inst Oceanog, Sao Paulo, Brazil..
    Conversani, Valeria R. M.
    Univ Sao Paulo, Lab Biol Conservacao Mamiferos Aquat LABCMA, Inst Oceanog, Sao Paulo, Brazil..
    Santos, Marcos C. de O.
    Univ Sao Paulo, Lab Biol Conservacao Mamiferos Aquat LABCMA, Inst Oceanog, Sao Paulo, Brazil..
    Acoustic Description of Beach-Hunting Guiana Dolphins (Sotalia guianensis) in the Cananeia Estuary, Southeastern Brazil2020In: Aquatic Mammals, ISSN 0167-5427, E-ISSN 1996-7292, Vol. 46, no 1, p. 11-20Article in journal (Refereed)
  • 3.
    Kontogiorgos, Dimosthenis
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Abelho Pereira, André Tiago
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH, Speech Communication and Technology.
    Sahindal, Boran
    KTH.
    van Waveren, Sanne
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Gustafson, Joakim
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Behavioural Responses to Robot Conversational Failures2020Conference paper (Refereed)
    Abstract [en]

    Humans and robots will increasingly collaborate in domestic environments which will cause users to encounter more failures in interactions. Robots should be able to infer conversational failures by detecting human users’ behavioural and social signals. In this paper, we study and analyse these behavioural cues in response to robot conversational failures. Using a guided task corpus, where robot embodiment and time pressure are manipulated, we ask human annotators to estimate whether user affective states differ during various types of robot failures. We also train a random forest classifier to detect whether a robot failure has occurred and compare results to human annotator benchmarks. Our findings show that human-like robots augment users’ reactions to failures, as shown in users’ visual attention, in comparison to non-humanlike smart-speaker embodiments. The results further suggest that speech behaviours are utilised more in responses to failures when non-human-like designs are present. This is particularly important to robot failure detection mechanisms that may need to consider the robot’s physical design in its failure detection model.

  • 4.
    Kontogiorgos, Dimosthenis
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    van Waveren, Sanne
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Wallberg, Olle
    KTH.
    Abelho Pereira, André Tiago
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Leite, Iolanda
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Gustafson, Joakim
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Embodiment Effects in Interactions with Failing Robots2020Conference paper (Refereed)
    Abstract [en]

    The increasing use of robots in real-world applications will inevitably cause users to encounter more failures in interactions. While there is a longstanding effort in bringing human-likeness to robots, how robot embodiment affects users’ perception of failures remains largely unexplored. In this paper, we extend prior work on robot failures by assessing the impact that embodiment and failure severity have on people’s behaviours and their perception of robots. Our findings show that when using a smart-speaker embodiment, failures negatively affect users’ intention to frequently interact with the device, however not when using a human-like robot embodiment. Additionally, users significantly rate the human-like robot higher in terms of perceived intelligence and social presence. Our results further suggest that in higher severity situations, human-likeness is distracting and detrimental to the interaction. Drawing on quantitative findings, we discuss benefits and drawbacks of embodiment in robot failures that occur in guided tasks.

  • 5.
    Haustein, Joshua Alexander
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Robot Manipulation Planning Among Obstacles: Grasping, Placing and Rearranging2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis presents planning algorithms for three different robot manipulation tasks: fingertip grasping, object placing and rearranging. Herein, we place special attention on addressing these tasks in the presence of obstacles. Obstacles are frequently encountered in human-centered environments and constrain a robot's motion and ability to manipulate objects. In narrow shelves, for example, even the common task of pick-and-place becomes challenging. A shelf is difficult to navigate and many potential grasps and placements are inaccessible. Hence, to solve such tasks, specialized manipulation planning algorithms are required that can cope with the presence of obstacles.

    For fingertip grasping, we first present a framework to learn models that encode which grasps a given dexterous robot hand can reach. These models are then used to facilitate planning and optimization of fingertip grasps. Next, we address the presence of obstacles and integrate fingertip grasp and motion planning to generate grasps that are reachable by a robot in complex scenes.

    For object placing, we first present an algorithm that plans the placement of a grasped object among obstacles so that a user-given placement objective is maximized. We then extend this algorithm, and incorporate planning in-hand manipulation to increase the set of placements a robot can reach.

    Lastly, we go beyond pure collision avoidance and study object rearrangement planning. Specifically, we consider the special case of non-prehensile rearrangement, where a robot rearranges multiple objects through pushing. First, we present how a kinodynamic motion planning algorithm can be augmented with learned models to rearrange a few target objects among movable and static obstacles. We then present how we can use Monte Carlo tree search to solve a large-scale rearrangement problem, where a robot is tasked to spatially sort many objects according to a user-assigned class membership.

  • 6.
    Palmieri, Luigi
    et al.
    Robert Bosch GmbH, Corp Res, D-70049 Stuttgart, Germany..
    Bruns, Leonard
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. RWTH Aachen University, Germany.
    Meurer, Michael
    Rhein Westfal TH Aachen, German Aerosp Ctr DLR, D-82234 Wessling, Germany..
    Arras, Kai O.
    Robert Bosch GmbH, Corp Res, D-70049 Stuttgart, Germany..
    Dispertio: Optimal Sampling For Safe Deterministic Motion Planning2020In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 5, no 2, p. 362-368Article in journal (Refereed)
    Abstract [en]

    A key challenge in robotics is the efficient generation of optimal robot motion with safety guarantees in cluttered environments. Recently, deterministic optimal sampling-based motion planners have been shown to achieve good performance towards this end, in particular in terms of planning efficiency, final solution cost, quality guarantees as well as non-probabilistic completeness. Yet their application is still limited to relatively simple systems (i.e., linear, holonomic, Euclidean state spaces). In this work, we extend this technique to the class of symmetric and optimal driftless systems by presenting Dispertio, an offline dispersion optimization technique for computing sampling sets, aware of differential constraints, for sampling-based robot motion planning. We prove that the approach, when combined with PRM*, is deterministically complete and retains asymptotic optimality. Furthermore, in our experiments we show that the proposed deterministic sampling technique outperforms several baselines and alternative methods in terms of planning efficiency and solution cost.

  • 7.
    Pinto Basto de Carvalho, Joao Frederico
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Vejdemo-Johansson, Mikael
    CUNY College of Staten Island,Mathematics Department,New York,USA.
    Pokorny, Florian T.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Long-term Prediction of Motion Trajectories Using Path Homology Clusters2019In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2019Conference paper (Refereed)
    Abstract [en]

    In order for robots to share their workspace with people, they need to reason about human motion efficiently. In this work we leverage large datasets of paths in order to infer local models that are able to perform long-term predictions of human motion. Further, since our method is based on simple dynamics, it is conceptually simple to understand and allows one to interpret the predictions produced, as well as to extract a cost function that can be used for planning. The main difference between our method and similar systems, is that we employ a map of the space and translate the motion of groups of paths into vector fields on that map. We test our method on synthetic data and show its performance on the Edinburgh forum pedestrian long-term tracking dataset [1] where we were able to outperform a Gaussian Mixture Model tasked with extracting dynamics from the paths.

  • 8.
    Klamt, Tobias
    et al.
    Univ Bonn, Autonomous Intelligent Syst, Bonn, Germany..
    Chen, Xi
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Karaoǧuz, Hakan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Behnke, Sven
    Univ Bonn, Autonomous Intelligent Syst, Bonn, Germany..
    et al.,
    Flexible Disaster Response of Tomorrow: Final Presentation and Evaluation of the CENTAURO System2019In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 26, no 4, p. 59-72Article in journal (Refereed)
    Abstract [en]

    Mobile manipulation robots have great potential for roles in support of rescuers on disaster-response missions. Robots can operate in places too dangerous for humans and therefore can assist in accomplishing hazardous tasks while their human operators work at a safe distance. We developed a disaster-response system that consists of the highly flexible Centauro robot and suitable control interfaces, including an immersive telepresence suit and support-operator controls offering different levels of autonomy.

  • 9.
    Jonell, Patrik
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Lopes, J.
    Per, Fallgren
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Wennberg, Ulme
    KTH.
    Doğan, Fethiye Irmak
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Skantze, Gabriel
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
    Crowdsourcing a self-evolving dialog graph2019In: CUI '19: Proceedings of the 1st International Conference on Conversational User Interfaces, Association for Computing Machinery (ACM), 2019, article id 14Conference paper (Refereed)
    Abstract [en]

    In this paper we present a crowdsourcing-based approach for collecting dialog data for a social chat dialog system, which gradually builds a dialog graph from actual user responses and crowd-sourced system answers, conditioned by a given persona and other instructions. This approach was tested during the second instalment of the Amazon Alexa Prize 2018 (AP2018), both for the data collection and to feed a simple dialog system which would use the graph to provide answers. As users interacted with the system, a graph which maintained the structure of the dialogs was built, identifying parts where more coverage was needed. In an ofine evaluation, we have compared the corpus collected during the competition with other potential corpora for training chatbots, including movie subtitles, online chat forums and conversational data. The results show that the proposed methodology creates data that is more representative of actual user utterances, and leads to more coherent and engaging answers from the agent. An implementation of the proposed method is available as open-source code.

  • 10.
    Tajvar, Pouria
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Varava, Anastasiia
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Tumova, Jana
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Robust motion planning for non-holonomicrobots with planar geometric constraints2019In: Proceedings of the ISRR2019, 2019Conference paper (Refereed)
    Abstract [en]

    We present a motion planning algorithm for cases where geometry of the robot cannot be neglected and where its dynamics are governed by non-holonomic constraints. While the two problems are classically treated separately, orientation of the robot strongly affects its possible motions both from the obstacle avoidance and from kinodynamic constraints perspective. We adopt an abstraction based approach ensuring asymptotic completeness. To handle the complex dynamics, a data driven approach is presented to construct a library of feedback motion primitives that guarantee a bounded error in following arbitrarily long trajectories. The library is constructed along local abstractions of the dynamics that enables addition of new motion primitives through abstraction refinement. Both the robot and the obstacles are represented as a union of circles, which allows arbitrarily precise approximation of complex geometries. To handle the geometrical constraints, we represent over- and under-approximations of the three-dimensional collision space as a finite set of two-dimensional "slices" corresponding to different intervals of the robot's orientation space. Starting from a coarse slicing, we use the collision space over-approximation to find a valid path and the under-approximation to check for  potential path non-existence. If none of the attempts are conclusive, the abstraction is refined. The algorithm is applied for motion planning and control of a rover with slipping without its prior modelling.

  • 11.
    Polianskii, Vladislav
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Pokorny, Florian T.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration2019In: Proceedings of Machine Learning Research (PMLR) / [ed] Kamalika Chaudhuri, Ruslan Salakhutdinov, 2019, Vol. 97, p. 5162-5170Conference paper (Refereed)
    Abstract [en]

    Voronoi cell decompositions provide a classical avenue to classification. Typical approaches however only utilize point-wise cell-membership information by means of nearest neighbor queries and do not utilize further geometric information about Voronoi cells since the computation of Voronoi diagrams is prohibitively expensive in high dimensions. We propose a Monte-Carlo integration based approach that instead computes a weighted integral over the boundaries of Voronoi cells, thus incorporating additional information about the Voronoi cell structure. We demonstrate the scalability of our approach in up to 3072 dimensional spaces and analyze convergence based on the number of Monte Carlo samples and choice of weight functions. Experiments comparing our approach to Nearest Neighbors, SVM and Random Forests indicate that while our approach performs similarly to Random Forests for large data sizes, the algorithm exhibits non-trivial data-dependent performance characteristics for smaller datasets and can be analyzed in terms of a geometric confidence measure, thus adding to the repertoire of geometric approaches to classification while having the benefit of not requiring any model changes or retraining as new training samples or classes are added.

  • 12.
    Mancini, Massimiliano
    et al.
    Sapienza Univ Rome, Rome, Italy.;Fdn Bruno Kessler, Trento, Italy..
    Karaoguz, Hakan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Ricci, Elisa
    Fdn Bruno Kessler, Trento, Italy.;Univ Trento, Trento, Italy..
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Caputo, Barbara
    Italian Inst Technol, Milan, Italy..
    Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition2019In: 2019 International Conference on Robotics And Automation (ICRA) / [ed] Howard, A Althoefer, K Arai, F Arrichiello, F Caputo, B Castellanos, J Hauser, K Isler, V Kim, J Liu, H Oh, P Santos, V Scaramuzza, D Ude, A Voyles, R Yamane, K Okamura, A, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 9537-9543, article id 8793803Conference paper (Refereed)
    Abstract [en]

    While today's robots are able to perform sophisticated tasks, they can only act on objects they have been trained to recognize. This is a severe limitation: any robot will inevitably see new objects in unconstrained settings, and thus will always have visual knowledge gaps. However, standard visual modules are usually built on a limited set of classes and are based on the strong prior that an object must belong to one of those classes. Identifying whether an instance does not belong to the set of known categories (i.e. open set recognition), only partially tackles this problem, as a truly autonomous agent should be able not only to detect what it does not know, but also to extend dynamically its knowledge about the world. We contribute to this challenge with a deep learning architecture that can dynamically update its known classes in an end-to-end fashion. The proposed deep network, based on a deep extension of a non-parametric model, detects whether a perceived object belongs to the set of categories known by the system and learns it without the need to retrain the whole system from scratch. Annotated images about the new category can be provided by an 'oracle' (i.e. human supervision), or by autonomous mining of the Web. Experiments on two different databases and on a robot platform demonstrate the promise of our approach.

  • 13.
    Yuan, Weihao
    et al.
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Peoples R China..
    Hang, Kaiyu
    Yale Univ, Dept Mech Engn & Mat Sci, New Haven, CT USA..
    Song, Haoran
    Hong Kong Univ Sci & Technol, Dept Mech & Aerosp Engn, Hong Kong, Peoples R China..
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Wang, Michael Y.
    Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Peoples R China.;Hong Kong Univ Sci & Technol, Dept Mech & Aerosp Engn, Hong Kong, Peoples R China..
    Stork, Johannes A.
    Örebro Univ, Ctr Appl Autonomous Sensor Syst, Örebro, Sweden.
    Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation2019In: 2019 International Conference on Robotics and Automation (ICRA) / [ed] Howard, A Althoefer, K Arai, F Arrichiello, F Caputo, B Castellanos, J Hauser, K Isler, V Kim, J Liu, H Oh, P Santos, V Scaramuzza, D Ude, A Voyles, R Yamane, K Okamura, A, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 2153-2160Conference paper (Refereed)
    Abstract [en]

    Moving a human body or a large and bulky object may require the strength of whole arm manipulation (WAM). This type of manipulation places the load on the robot's arms and relies on global properties of the interaction to succeed-rather than local contacts such as grasping or non-prehensile pushing. In this paper, we learn to generate motions that enable WAM for holding and transporting of humans in certain rescue or patient care scenarios. We model the task as a reinforcement learning problem in order to provide a robot behavior that can directly respond to external perturbation and human motion. For this, we represent global properties of the robot-human interaction with topology-based coordinates that are computed from arm and torso positions. These coordinates also allow transferring the learned policy to other body shapes and sizes. For training and evaluation, we simulate a dynamic sea rescue scenario and show in quantitative experiments that the policy can solve unseen scenarios with differently-shaped humans, floating humans, or with perception noise. Our qualitative experiments show the subsequent transporting after holding is achieved and we demonstrate that the policy can be directly transferred to a real world setting.

  • 14.
    Karaoguz, Hakan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. Royal Inst Technol KTH, Sch Elect Engn & Comp Sci, SE-10044 Stockholm, Sweden..
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Object Detection Approach for Robot Grasp Detection2019In: 2019 International Conference on Robotics And Automation (ICRA) / [ed] Howard, A Althoefer, K Arai, F Arrichiello, F Caputo, B Castellanos, J Hauser, K Isler, V Kim, J Liu, H Oh, P Santos, V Scaramuzza, D Ude, A Voyles, R Yamane, K Okamura, A, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 4953-4959, article id 8793751Conference paper (Refereed)
    Abstract [en]

    In this paper, we focus on the robot grasping problem with parallel grippers using image data. For this task, we propose and implement an end-to-end approach. In order to detect the good grasping poses for a parallel gripper from RGB images, we have employed transfer learning for a Convolutional Neural Network (CNN) based object detection architecture. Our obtained results show that, the adapted network either outperforms or is on-par with the state-of-the art methods on a benchmark dataset. We also performed grasping experiments on a real robot platform to evaluate our method's real world performance.

  • 15.
    Haustein, Joshua Alexander
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Hang, Kaiyu
    Department of Mechanical Engineering and Material Science, Yale University.
    Stork, Johannes A.
    Center for Applied Autonomous Sensor Systems (AASS), Örebro University.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Object Placement Planning and Optimization for Robot Manipulators2019In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), 2019Conference paper (Refereed)
    Abstract [en]

    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.

  • 16.
    van Waveren, Sanne
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Björklund, Linnéa
    KTH.
    Carter, Elizabeth
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Leite, Iolanda
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Knock on Wood: The Effects of Material Choice on the Perception of Social Robots2019In: Lecture Notes in Artificial Intelligence series (LNAI), 2019Conference paper (Refereed)
  • 17.
    Gällström, Andreas
    et al.
    Saab AB, SE-581 88 Linköping, Sweden ; Department of Electrical and Information Technology, Lund University.
    Rixon Fuchs, Louise
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligenta system, Robotics, Perception and Learning, RPL. Saab AB, SE-581 88 Linköping, Sweden.
    Larsson, Christer
    Saab AB, SE-581 88 Linköping, Sweden ; Department of Electrical and Information Technology, Lund University.
    Enhanced sonar image resolution using compressive sensing modelling2019In: Conference Proceedings 5th Underwater Acoustics Conference and Exhibition UACE2019 / [ed] John S. Papadakis, UACE , 2019, p. 995-999Conference paper (Other academic)
    Abstract [en]

    The sonar image resolution is classically limited by the sonar array dimensions. There are several techniques to enhance the resolution; most common is the synthetic aperture sonar (SAS) technique where several pings are added coherently to achieve a longer array and thereby higher cross range resolution. This leads to high requirements on navigation accuracy, but the different autofocus techniques in general also require collecting overlapping data. This limits the acquisition speed whencovering a specific area. We investigate the possibility to enhance the resolution in images processed from one ping measurementin this paperusing compressive sensing methods. A model consisting of isotropic point scatterers is used for the imaged target. The point scatterer amplitudes are frequency and angle independent. We assume only direct paths between the scatterers and the transmitter/receiver in theinverse problemformulation. The solution to this system of equations turns out to be naturally sparse, i.e., relatively few scatterers are required to describe the measured signal.The sparsity means that L1 optimization and methods from compressive sensing (CS) can be used to solve the inverse problem efficiently. We use the basis pursuit denoise algorithm (BPDN) as implemented in the SPGL1 package to solve the optimization problem.We present results based on CS on measurements collected at Saab. The measurements are collected using the experimental platform Sapphires in freshwater Lake Vättern. Images processed using classical back projection algorithms are compared tosonar images with enhanced resolution using CS, with a 10 times improvement in cross range resolution.

  • 18.
    Rixon Fuchs, Louise
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. Saab, SE-581 88 Linköping, Sweden.
    Larsson, Christer
    Saab, SE-581 88 Linköping, Sweden;Department of Electrical and Information Technology, Lund University.
    Gällström, Andreas
    Saab, SE-581 88 Linköping, Sweden;Department of Electrical and Information Technology, Lund University.
    Deep learning based technique for enhanced sonar imaging2019Conference paper (Other academic)
    Abstract [en]

    Several beamforming techniques can be used to enhance the resolution of sonar images. Beamforming techniques can be divided into two types: data independent beamforming such as the delay-sum-beamformer, and data-dependent methods known as adaptive beamformers. Adaptive beamformers can often achieve higher resolution, but are more sensitive to errors. Several signals are processed from several consecutive pings. The signals are added coherently to achieve the same effect as having a longer array in synthetic aperture sonar (SAS). In general it can be said that a longer array gives a higher image resolution. SAS processing typically requires high navigation accuracy, and physical array-overlap between pings. This restriction on displacement between pings limits the area coverage rate for the vehicle carrying the SAS. We investigate the possibility to enhance sonar images from one ping measurements in this paper. This is done by using state-of-the art techniques from Image-to-Image translation, namely the conditional generative adversarial network (cGAN) Pix2Pix. The cGAN learns a mapping from an input to output image as well as a loss function to train the mapping. We test our concept by training a cGAN on simulated data, going from a short array (low resolution) to a longer array (high resolution). The method is evaluated using measured SAS-data collected by Saab with the experimental platform Sapphires in freshwater Lake Vättern.

  • 19.
    Cruciani, Silvia
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Vision-Based In-Hand Manipulation with Limited Dexterity2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In-hand manipulation is an action that allows for changing the grasp on an object without the need for releasing it. This action is an important component in the manipulation process and helps solving many tasks. Human hands are dexterous instruments suitable for moving an object inside the hand. However, it is not common for robots to be equipped with dexterous hands due to many challenges in control and mechanical design. In fact, robots are frequently equipped with simple parallel grippers, robust but lacking dexterity. This thesis focuses on achieving in-hand manipulation with limited dexterity. The proposed solutions are based only on visual input, without the need for additional sensing capabilities in the robot's hand.

    Extrinsic dexterity allows simple grippers to execute in-hand manipulation thanks to the exploitation of external supports. This thesis introduces new methods for solving in-hand manipulation using inertial forces, controlled friction and external pushes as additional supports to enhance the robot's manipulation capabilities. Pivoting is seen as a possible solution for simple grasp changes: two methods, which cope with inexact friction modeling, are reported, and pivoting is successfully integrated in an overall manipulation task. For large scale in-hand manipulation, the Dexterous Manipulation Graph is introduced as a novel representation of the object. This graph is a useful tool for planning how to change a certain grasp via in-hand manipulation. It can also be exploited to combine both in-hand manipulation and regrasping to augment the possibilities of adjusting the grasp. In addition, this method is extended to achieve in-hand manipulation even for objects with unknown shape. To execute the planned object motions within the gripper, dual-arm robots are exploited to enhance the poor dexterity of parallel grippers: the second arm is seen as an additional support that helps in pushing and holding the object to successfully adjust the grasp configuration.

    This thesis presents examples of successful executions of tasks where in-hand manipulation is a fundamental step in the manipulation process, showing how the proposed methods are a viable solution for achieving in-hand manipulation with limited dexterity.

  • 20.
    Almeida, Diogo
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Dual-Arm Robotic Manipulation under Uncertainties and Task-Based Redundancy2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Robotic manipulators are mostly employed in industrial environments, where their tasks can be prescribed with little to no uncertainty. This is possible in scenarios where the deployment time of robot workcells is not prohibitive, such as in the automotive industry. In other contexts, however, the time cost of setting up a classical robotic automation workcell is often prohibitive. This is the case with cellphone manufacturing, for example, which is currently mostly executed by human workers. Robotic automation is nevertheless desirable in these human-centric environments, as a robot can automate the most tedious parts of an assembly. To deploy robots in these environments, however, requires an ability to deal with uncertainties and to robustly execute any given task. In this thesis, we discuss two topics related to autonomous robotic manipulation. First, we address parametric uncertainties in manipulation tasks, such as the location of contacts during the execution of an assembly. We propose and experimentally evaluate two methods that rely on force and torque measurements to produce estimates of task related uncertainties: a method for dexterous manipulation under uncertainties which relies on a compliant rotational degree of freedom at the robot's gripper grasp point and exploits contact  with an external surface, and a cooperative manipulation system which is able to identify the kinematics of a two degrees of freedom mechanism. Then, we consider redundancies in dual-arm robotic manipulation. Dual-armed robots offer a large degree of redundancy which can be exploited to ensure a more robust task execution. When executing an assembly task, for instance, robots can freely change the location of the assembly in their workspace without affecting the task execution. We discuss methods that explore these types of redundancies in relative motion tasks in the form of asymmetries in their execution. Finally, we approach the converse problem by presenting a system which is able to balance measured forces and torques at its end-effectors by leveraging relative motion between them, while grasping a rigid tray. This is achieved through discrete sliding of the grasp points, which constitutes a novel application of bimanual dexterous manipulation.

  • 21.
    van Waveren, Sanne
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Carter, Elizabeth J.
    KTH.
    Leite, Iolanda
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Take one for the team: The effects of error severity in collaborative tasks with social robots2019In: IVA 2019 - Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents, Association for Computing Machinery (ACM), 2019, p. 151-158Conference paper (Refereed)
    Abstract [en]

    We explore the effects of robot failure severity (no failure vs. lowimpact vs. high-impact) on people's subjective ratings of the robot. We designed an escape room scenario in which one participant teams up with a remotely-controlled Pepper robot.We manipulated the robot's performance at the end of the game: The robot would either correctly follow the participant's instructions (control condition), the robot would fail but people could still complete the task of escaping the room (low-impact condition), or the robot's failure would cause the game to be lost (high-impact condition). Results showed no difference across conditions for people's ratings of the robot in terms of warmth, competence, and discomfort. However, people in the low-impact condition had significantly less faith in the robot's robustness in future escape room scenarios. Open-ended questions revealed interesting trends that are worth pursuing in the future: people may view task performance as a team effort and may blame their team or themselves more for the robot failure in case of a high-impact failure as compared to the low-impact failure.

  • 22.
    Cruciani, Silvia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Hang, Kaiyu
    Yale University.
    Smith, Christian
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Dual-Arm In-Hand Manipulation Using Visual Feedback2019Conference paper (Refereed)
    Abstract [en]

    In this work, we address the problem of executing in-hand manipulation based on visual input. Given an initial grasp, the robot has to change its grasp configuration without releasing the object. We propose a method for in-hand manipulation planning and execution based on information on the object’s shape using a dual-arm robot. From the available information on the object, which can be a complete point cloud but also partial data, our method plans a sequence of rotations and translations to reconfigure the object’s pose. This sequence is executed using non-prehensile pushes defined as relative motions between the two robot arms.

  • 23.
    Haustein, Joshua A.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Cruciani, Silvia
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Asif, Rizwan
    KTH.
    Hang, Kaiyu
    KTH.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Placing Objects with prior In-Hand Manipulation using Dexterous Manipulation Graphs2019Conference paper (Refereed)
    Abstract [en]

    We address the problem of planning the placement of a grasped object with a robot manipulator. More specifically, the robot is tasked to place the grasped object such that a placement preference function is maximized. For this, we present an approach that uses in-hand manipulation to adjust the robot’s initial grasp to extend the set of reachable placements. Given an initial grasp, the algorithm computes a set of grasps that can be reached by pushing and rotating the object in-hand. With this set of reachable grasps, it then searches for a stable placement that maximizes the preference function. If successful it returns a sequence of in-hand pushes to adjust the initial grasp to a more advantageous grasp together with a transport motion that carries the object to the placement. We evaluate our algorithm’s performance on various placing scenarios, and observe its effectiveness also in challenging scenes containing many obstacles. Our experiments demonstrate that re-grasping with in-hand manipulation increases the quality of placements the robot can reach. In particular, it enables the algorithm to find solutions in situations where safe placing with the initial grasp wouldn’t be possible.

  • 24.
    Barbosa, Fernando S.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Lindemann, Lars
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Tumova, Jana
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Integrated motion planning and control under metric interval temporal logic specifications2019In: 2019 18th European Control Conference, ECC 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 2042-2049, article id 8795925Conference paper (Refereed)
    Abstract [en]

    This paper proposes an approach that combines motion planning and hybrid feedback control design in order to find and follow trajectories fulfilling a given complex mission involving time constraints. We use Metric Interval Temporal Logic (MITL) as a rich and rigorous formalism to specify such missions. The solution builds on three main steps: (i) using sampling-based motion planning methods and the untimed version of the mission specification in the form of Zone automaton, we find a sequence of waypoints in the workspace; (ii) based on the clock zones from the satisfying run on the Zone automaton, we compute time-stamps at which these waypoints should be reached; and (iii) to control the system to connect two waypoints in the desired time, we design a low-level feedback controller leveraging Time-varying Control Barrier Functions. Illustrative simulation results are included.

  • 25.
    Sibirtseva, Elena
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Ghadirzadeh, Ali
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. Intelligent Robotics Research Group, Aalto University, Espoo, Finland.
    Leite, Iolanda
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Björkman, Mårten
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Exploring Temporal Dependencies in Multimodal Referring Expressions with Mixed Reality2019In: Virtual, Augmented and Mixed Reality. Multimodal Interaction 11th International Conference, VAMR 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings, Springer Verlag , 2019, p. 108-123Conference paper (Refereed)
    Abstract [en]

    In collaborative tasks, people rely both on verbal and non-verbal cues simultaneously to communicate with each other. For human-robot interaction to run smoothly and naturally, a robot should be equipped with the ability to robustly disambiguate referring expressions. In this work, we propose a model that can disambiguate multimodal fetching requests using modalities such as head movements, hand gestures, and speech. We analysed the acquired data from mixed reality experiments and formulated a hypothesis that modelling temporal dependencies of events in these three modalities increases the model’s predictive power. We evaluated our model on a Bayesian framework to interpret referring expressions with and without exploiting the temporal prior.

  • 26.
    Jonell, Patrik
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Kucherenko, Taras
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Ekstedt, Erik
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Beskow, Jonas
    KTH, Superseded Departments (pre-2005), Speech, Music and Hearing.
    Learning Non-verbal Behavior for a Social Robot from YouTube Videos2019Conference paper (Refereed)
    Abstract [en]

    Non-verbal behavior is crucial for positive perception of humanoid robots. If modeled well it can improve the interaction and leave the user with a positive experience, on the other hand, if it is modelled poorly it may impede the interaction and become a source of distraction. Most of the existing work on modeling non-verbal behavior show limited variability due to the fact that the models employed are deterministic and the generated motion can be perceived as repetitive and predictable. In this paper, we present a novel method for generation of a limited set of facial expressions and head movements, based on a probabilistic generative deep learning architecture called Glow. We have implemented a workflow which takes videos directly from YouTube, extracts relevant features, and trains a model that generates gestures that can be realized in a robot without any post processing. A user study was conducted and illustrated the importance of having any kind of non-verbal behavior while most differences between the ground truth, the proposed method, and a random control were not significant (however, the differences that were significant were in favor of the proposed method).

  • 27.
    Englesson, Erik
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Azizpour, Hossein
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation2019Conference paper (Refereed)
  • 28.
    Baldassarre, Federico
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Azizpour, Hossein
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Explainability Techniques for Graph Convolutional Networks2019Conference paper (Refereed)
    Abstract [en]

    Graph Networks are used to make decisions in potentially complex scenarios but it is usually not obvious how or why they made them. In this work, we study the explainability of Graph Network decisions using two main classes of techniques, gradient-based and decomposition-based, on a toy dataset and a chemistry task. Our study sets the ground for future development as well as application to real-world problems.

  • 29.
    Yuan, Weihao
    et al.
    Hong Kong Univ Sci & Technol, ECE, Robot Inst, Hong Kong, Peoples R China..
    Hang, Kaiyu
    Yale Univ, Mech Engn & Mat Sci, New Haven, CT USA..
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Wang, Michael Y.
    Hong Kong Univ Sci & Technol, ECE, Robot Inst, Hong Kong, Peoples R China..
    Stork, Johannes A.
    Orebro Univ, Ctr Appl Autonomous Sensor Syst, Orebro, Sweden..
    End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer2019In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 119, p. 119-134Article in journal (Refereed)
    Abstract [en]

    Nonprehensile rearrangement is the problem of controlling a robot to interact with objects through pushing actions in order to reconfigure the objects into a predefined goal pose. In this work, we rearrange one object at a time in an environment with obstacles using an end-to-end policy that maps raw pixels as visual input to control actions without any form of engineered feature extraction. To reduce the amount of training data that needs to be collected using a real robot, we propose a simulation-to-reality transfer approach. In the first step, we model the nonprehensile rearrangement task in simulation and use deep reinforcement learning to learn a suitable rearrangement policy, which requires in the order of hundreds of thousands of example actions for training. Thereafter, we collect a small dataset of only 70 episodes of real-world actions as supervised examples for adapting the learned rearrangement policy to real-world input data. In this process, we make use of newly proposed strategies for improving the reinforcement learning process, such as heuristic exploration and the curation of a balanced set of experiences. We evaluate our method in both simulation and real setting using a Baxter robot to show that the proposed approach can effectively improve the training process in simulation, as well as efficiently adapt the learned policy to the real world application, even when the camera pose is different from simulation. Additionally, we show that the learned system not only can provide adaptive behavior to handle unforeseen events during executions, such as distraction objects, sudden changes in positions of the objects, and obstacles, but also can deal with obstacle shapes that were not present in the training process.

  • 30.
    Almeida, Diogo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Ataer-Cansizoglu, Esra
    Wayfair, Boston, MA 02116, USA.
    Corcodel, Radu
    Mitsubishi Electric Research Labs (MERL), Cambridge, MA 02139, USA.
    Detection, Tracking and 3D Modeling of Objects with Sparse RGB-D SLAM and Interactive Perception2019In: IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2019Conference paper (Refereed)
    Abstract [en]

    We present an interactive perception system that enables an autonomous agent to deliberately interact with its environment and produce 3D object models. Our system verifies object hypotheses through interaction and simultaneously maintains 3D SLAM maps for each rigidly moving object hypothesis in the scene. We rely on depth-based segmentation and a multigroup registration scheme to classify features into various object maps. Our main contribution lies in the employment of a novel segment classification scheme that allows the system to handle incorrect object hypotheses, common in cluttered environments due to touching objects or occlusion. We start with a single map and initiate further object maps based on the outcome of depth segment classification. For each existing map, we select a segment to interact with and execute a manipulation primitive with the goal of disturbing it. If the resulting set of depth segments has at least one segment that did not follow the dominant motion pattern of its respective map, we split the map, thus yielding updated object hypotheses. We show qualitative results with a Fetch manipulator and objects of various shapes, which showcase the viability of the method for identifying and modelling multiple objects through repeated interactions.

  • 31.
    Almeida, Diogo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Karayiannidis, Yiannis
    A Lyapunov-Based Approach to Exploit Asymmetries in Robotic Dual-Arm Task Resolution2019In: 58th IEEE Conference on Decision and Control (CDC), 2019Conference paper (Refereed)
    Abstract [en]

    Dual-arm manipulation tasks can be prescribed to a robotic system in terms of desired absolute and relative motion of the robot’s end-effectors. These can represent, e.g., jointly carrying a rigid object or performing an assembly task. When both types of motion are to be executed concurrently, the symmetric distribution of the relative motion between arms prevents task conflicts. Conversely, an asymmetric solution to the relative motion task will result in conflicts with the absolute task. In this work, we address the problem of designing a control law for the absolute motion task together with updating the distribution of the relative task among arms. Through a set of numerical results, we contrast our approach with the classical symmetric distribution of the relative motion task to illustrate the advantages of our method.

  • 32.
    Almeida, Diogo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Karayiannidis, Yiannis
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Asymmetric Dual-Arm Task Execution using an Extended Relative Jacobian2019In: The International Symposium on Robotics Research, 2019Conference paper (Refereed)
    Abstract [en]

    Coordinated dual-arm manipulation tasks can be broadly characterized as possessing absolute and relative motion components. Relative motion tasks, in particular, are inherently redundant in the way they can be distributed between end-effectors. In this work, we analyse cooperative manipulation in terms of the asymmetric resolution of relative motion tasks. We discuss how existing approaches enable the asymmetric execution of a relative motion task, and show how an asymmetric relative motion space can be defined. We leverage this result to propose an extended relative Jacobian to model the cooperative system, which allows a user to set a concrete degree of asymmetry in the task execution. This is achieved without the need for prescribing an absolute motion target. Instead, the absolute motion remains available as a functional redundancy to the system. We illustrate the properties of our proposed Jacobian through numerical simulations of a novel differential Inverse Kinematics algorithm.

  • 33.
    Mitsioni, Ioanna
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Karayiannidis, Yiannis
    Division of Systems and Control, Dept. of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Stork, Johannes A.
    Center for Applied Autonomous Sensor Systems (AASS), Örebro University, Örebro, Sweden.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Data-Driven Model Predictive Control for the Contact-Rich Task of Food Cutting2019Conference paper (Refereed)
    Abstract [en]

    Modelling of contact-rich tasks is challenging and cannot be entirely solved using classical control approaches due to the difficulty of constructing an analytic description of the contact dynamics. Additionally, in a manipulation task like food-cutting, purely learning-based methods such as Reinforcement Learning, require either a vast amount of data that is expensive to collect on a real robot, or a highly realistic simulation environment, which is currently not available. This paper presents a data-driven control approach that employs a recurrent neural network to model the dynamics for a Model Predictive Controller. We build upon earlier work limited to torque-controlled robots and redefine it for velocity controlled ones. We incorporate force/torque sensor measurements, reformulate and further extend the control problem formulation. We evaluate the performance on objects used for training, as well as on unknown objects, by means of the cutting rates achieved and demonstrate that the method can efficiently treat different cases with only one dynamic model. Finally we investigate the behavior of the system during force-critical instances of cutting and illustrate its adaptive behavior in difficult cases.

  • 34.
    Tu, Ruibo
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Zhange, Cheng
    Ackermann, Paul
    Mohan, Karthika
    Kjellström, Hedvig
    Zhang, Kun
    Causal Discovery in the Presence of Missing Data2019Conference paper (Refereed)
  • 35.
    Hamesse, Charles
    et al.
    KTH. Royal Military Academy, Brussels, Belgium.
    Tu, Ruibo
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Ackermann, Paul
    Karolinska University Hospital, Stockholm, Sweden.
    Kjellström, Hedvig
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Zhang, Cheng
    Microsoft Research, Cambridge, UK.
    Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation2019In: Proceedings of Machine Learning Research 106, 2019Conference paper (Refereed)
    Abstract [en]

    Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such a musculoskeletal injury remains a prolonged process with a very variable outcome. Accurately predicting rehabilitation outcome is crucial for treatment decision support. However, it is challenging to train an automatic method for predicting the AT Rrehabilitation outcome from treatment data, due to a massive amount of missing entries in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes. In this work, we design an end-to-end probabilistic framework to impute missing data entries and predict rehabilitation outcomes simultaneously. We evaluate our model on a real-life ATR clinical cohort, comparing with various baselines. The proposed method demonstrates its clear superiority over traditional methods which typically perform imputation and prediction in two separate stages.

  • 36.
    Arnekvist, Isac
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Stork, Johannes A.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. Center for Applied Autonomous Sensor Systems, Örebro University, Sweden.
    Vpe: Variational policy embedding for transfer reinforcement learning2019In: 2019 International Conference on Robotics And Automation (ICRA), Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 36-42Conference paper (Refereed)
    Abstract [en]

    Reinforcement Learning methods are capable of solving complex problems, but resulting policies might perform poorly in environments that are even slightly different. In robotics especially, training and deployment conditions often vary and data collection is expensive, making retraining undesirable. Simulation training allows for feasible training times, but on the other hand suffer from a reality-gap when applied in real-world settings. This raises the need of efficient adaptation of policies acting in new environments. We consider the problem of transferring knowledge within a family of similar Markov decision processes. We assume that Q-functions are generated by some low-dimensional latent variable. Given such a Q-function, we can find a master policy that can adapt given different values of this latent variable. Our method learns both the generative mapping and an approximate posterior of the latent variables, enabling identification of policies for new tasks by searching only in the latent space, rather than the space of all policies. The low-dimensional space, and master policy found by our method enables policies to quickly adapt to new environments. We demonstrate the method on both a pendulum swing-up task in simulation, and for simulation-to-real transfer on a pushing task.

  • 37.
    Mänttäri, Joonatan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Folkesson, John
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Incorporating Uncertainty in Predicting Vehicle Maneuvers at Intersections With Complex Interactions2019In: 2019 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    Highly automated driving systems are required to make robust decisions in many complex driving environments, such as urban intersections with high traffic. In order to make as informed and safe decisions as possible, it is necessary for the system to be able to predict the future maneuvers and positions of other traffic agents, as well as to provide information about the uncertainty in the prediction to the decision making module. While Bayesian approaches are a natural way of modeling uncertainty, recently deep learning-based methods have emerged to address this need as well. However, balancing the computational and system complexity, while also taking into account agent interactions and uncertainties, remains a difficult task. The work presented in this paper proposes a method of producing predictions of other traffic agents' trajectories in intersections with a singular Deep Learning module, while incorporating uncertainty and the interactions between traffic participants. The accuracy of the generated predictions is tested on a simulated intersection with a high level of interaction between agents, and different methods of incorporating uncertainty are compared. Preliminary results show that the CVAE-based method produces qualitatively and quantitatively better measurements of uncertainty and manage to more accurately assign probability to the future occupied space of traffic agents.

  • 38.
    Tang, Jiexiong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Ericson, Ludvig
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Folkesson, John
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS.
    GCNv2: Efficient Correspondence Prediction for Real-Time SLAM2019In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, no 4, p. 3505-3512Article in journal (Refereed)
    Abstract [en]

    In this letter, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors. GCNv2 is built on our previous method, GCN, a network trained for 3D projective geometry. GCNv2 is designed with a binary descriptor vector as the ORB feature so that it can easily replace ORB in systems such as ORB-SLAM2. GCNv2 significantly improves the computational efficiency over GCN that was only able to run on desktop hardware. We show how a modified version of ORBSLAM2 using GCNv2 features runs on a Jetson TX2, an embedded low-power platform. Experimental results show that GCNv2 retains comparable accuracy as GCN and that it is robust enough to use for control of a flying drone. Source code is available at: https://github.com/jiexiong2016/GCNv2_SLAM.

  • 39.
    Nordström, Marcus
    et al.
    KTH. RaySearch Labs, Stockholm, Sweden..
    Soderberg, J.
    RaySearch Labs, Stockholm, Sweden..
    Shusharina, N.
    Massachusetts Gen Hosp, Boston, MA 02114 USA..
    Edmunds, D.
    Massachusetts Gen Hosp, Boston, MA 02114 USA..
    Lofman, F.
    RaySearch Labs, Stockholm, Sweden..
    Hult, Henrik
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Maki, Atsuto
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Bortfeld, T.
    Massachusetts Gen Hosp, Boston, MA 02114 USA..
    Interactive Deep Learning-Based Delineation of Gross Tumor Volume for Postoperative Glioma Patients2019In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 46, no 6, p. E426-E427Article in journal (Other academic)
  • 40.
    Stefanov, Kalin
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Salvi, Giampiero
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Kontogiorgos, Dimosthenis
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Kjellström, Hedvig
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Beskow, Jonas
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Modeling of Human Visual Attention in Multiparty Open-World Dialogues2019In: ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION, ISSN 2573-9522, Vol. 8, no 2, article id UNSP 8Article in journal (Refereed)
    Abstract [en]

    This study proposes, develops, and evaluates methods for modeling the eye-gaze direction and head orientation of a person in multiparty open-world dialogues, as a function of low-level communicative signals generated by his/hers interlocutors. These signals include speech activity, eye-gaze direction, and head orientation, all of which can be estimated in real time during the interaction. By utilizing these signals and novel data representations suitable for the task and context, the developed methods can generate plausible candidate gaze targets in real time. The methods are based on Feedforward Neural Networks and Long Short-Term Memory Networks. The proposed methods are developed using several hours of unrestricted interaction data and their performance is compared with a heuristic baseline method. The study offers an extensive evaluation of the proposed methods that investigates the contribution of different predictors to the accurate generation of candidate gaze targets. The results show that the methods can accurately generate candidate gaze targets when the person being modeled is in a listening state. However, when the person being modeled is in a speaking state, the proposed methods yield significantly lower performance.

  • 41.
    Eriksson, Sara
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Media Technology and Interaction Design, MID.
    Unander-Scharin, Åsa
    Luleå University of Technology.
    Trichon, Vincent
    KTH, School of Electrical Engineering and Computer Science (EECS), Media Technology and Interaction Design, MID.
    Unander-Scharin, Carl
    Karlstad University.
    Kjellström, Hedvig
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Höök, Kristina
    KTH, School of Electrical Engineering and Computer Science (EECS), Media Technology and Interaction Design, MID.
    Dancing with Drones: Crafting Novel Artistic Expressions through Intercorporeality2019In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, New York, NY USA, 2019, p. 617:1-617:12Conference paper (Refereed)
  • 42.
    Shi, Jiajun
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Yin, Wenjie
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Du, Yipai
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Folkesson, John
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Automated Underwater Pipeline Damage Detection using Neural Nets2019Conference paper (Refereed)
    Abstract [en]

    Pipeline inspection is a very human intensive taskand automation could improve efficiencies significantly. We propose a system that could allow an autonomous underwater vehicle (AUV), to detect pipeline damage in a stream of images.Our classifiers were based on transfer learning from pre-trained convolutional neural networks (CNN). This allows us to achieve good results despite relatively few training examples of damage. We test the approach using data from an actual pipeline inspection.

  • 43. Peng, Dongdong
    et al.
    Folkesson, John
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Xu, Chao
    Robust Particle Filter Based on Huber Function for Underwater Terrain Aided Navigation2019In: IET radar, sonar & navigation, ISSN 1751-8784, E-ISSN 1751-8792Article in journal (Refereed)
    Abstract [en]

    Terrain aided navigation is a promising technique to determine the location of underwater vehicle by matching terrain measurement against a known map. The particle filter is a natural choice for terrain aided navigation because of its ability to handle nonlinear, multimodal problems. However, the terrain measurements are vulnerable to outliers, which will cause the particle filter to degrade or even diverge. Modification of the Gaussian likelihood function by using robust cost functions is a way to reduce the effect of outliers on an estimate. We propose to use the Huber function to modify the measurement model used to set importance weights in a particle filter. We verify our method in simulations of multi-beam sonar in a real underwater digital map. The results demonstrate that the proposed method is more robust to outliers than the standard particle filter.

  • 44.
    Bütepage, Judith
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Generative models for action generation and action understanding2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The question of how to build intelligent machines raises the question of how to rep-resent the world to enable intelligent behavior. In nature, this representation relies onthe interplay between an organism’s sensory input and motor input. Action-perceptionloops allow many complex behaviors to arise naturally. In this work, we take these sen-sorimotor contingencies as an inspiration to build robot systems that can autonomouslyinteract with their environment and with humans. The goal is to pave the way for robotsystems that can learn motor control in an unsupervised fashion and relate their ownsensorimotor experience to observed human actions. By combining action generationand action understanding we hope to facilitate smooth and intuitive interaction betweenrobots and humans in shared work spaces.To model robot sensorimotor contingencies and human behavior we employ gen-erative models. Since generative models represent a joint distribution over relevantvariables, they are flexible enough to cover the range of tasks that we are tacklinghere. Generative models can represent variables that originate from multiple modali-ties, model temporal dynamics, incorporate latent variables and represent uncertaintyover any variable - all of which are features required to model sensorimotor contin-gencies. By using generative models, we can predict the temporal development of thevariables in the future, which is important for intelligent action selection.We present two lines of work. Firstly, we will focus on unsupervised learning ofmotor control with help of sensorimotor contingencies. Based on Gaussian Processforward models we demonstrate how the robot can execute goal-directed actions withthe help of planning techniques or reinforcement learning. Secondly, we present anumber of approaches to model human activity, ranging from pure unsupervised mo-tion prediction to including semantic action and affordance labels. Here we employdeep generative models, namely Variational Autoencoders, to model the 3D skeletalpose of humans over time and, if required, include semantic information. These twolines of work are then combined to implement physical human-robot interaction tasks.Our experiments focus on real-time applications, both when it comes to robot ex-periments and human activity modeling. Since many real-world scenarios do not haveaccess to high-end sensors, we require our models to cope with uncertainty. Additionalrequirements are data-efficient learning, because of the wear and tear of the robot andhuman involvement, online employability and operation under safety and complianceconstraints. We demonstrate how generative models of sensorimotor contingencies canhandle these requirements in our experiments satisfyingly.

  • 45.
    Zhang, Cheng
    et al.
    Microsoft Res, Cambridge CB1 2FB, England..
    Butepage, Judith
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Kjellström, Hedvig
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Mandt, Stephan
    Advances in Variational Inference2019In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 41, no 8, p. 2008-2026Article in journal (Refereed)
    Abstract [en]

    Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully applied to various models and large-scale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects: (a) scalable VI, which includes stochastic approximations, (b) generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as non-conjugate models, mean field approximation or with atypical divergences, and (d) amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.

  • 46. Wolfert, Pieter
    et al.
    Kucherenko, Taras
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Kjellström, Hedvig
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Belpaeme, Tony
    Should Beat Gestures Be Learned Or Designed?: A Benchmarking User Study2019In: ICDL-EPIROB 2019: Workshop on Naturalistic Non-Verbal and Affective Human-Robot Interactions, IEEE conference proceedings, 2019Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a user study on gener-ated beat gestures for humanoid agents. It has been shownthat Human-Robot Interaction can be improved by includingcommunicative non-verbal behavior, such as arm gestures. Beatgestures are one of the four types of arm gestures, and are knownto be used for emphasizing parts of speech. In our user study,we compare beat gestures learned from training data with hand-crafted beat gestures. The first kind of gestures are generatedby a machine learning model trained on speech audio andhuman upper body poses. We compared this approach with threehand-coded beat gestures methods: designed beat gestures, timedbeat gestures, and noisy gestures. Forty-one subjects participatedin our user study, and a ranking was derived from pairedcomparisons using the Bradley Terry Luce model. We found thatfor beat gestures, the gestures from the machine learning modelare preferred, followed by algorithmically generated gestures.This emphasizes the promise of machine learning for generating communicative actions.

  • 47.
    Barbosa, Fernando S.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Duberg, Daniel
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Jensfelt, Patric
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Tumova, Jana
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Guiding Autonomous Exploration with Signal Temporal Logic2019In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, no 4, p. 3332-3339Article in journal (Refereed)
    Abstract [en]

    Algorithms for autonomous robotic exploration usually focus on optimizing time and coverage, often in a greedy fashion. However, obstacle inflation is conservative and might limit mapping capabilities and even prevent the robot from moving through narrow, important places. This letter proposes a method to influence the manner the robot moves in the environment by taking into consideration a user-defined spatial preference formulated in a fragment of signal temporal logic (STL). We propose to guide the motion planning toward minimizing the violation of such preference through a cost function that integrates the quantitative semantics, i.e., robustness of STL. To demonstrate the effectiveness of the proposed approach, we integrate it into the autonomous exploration planner (AEP). Results from simulations and real-world experiments are presented, highlighting the benefits of our approach.

  • 48.
    Billard, Aude
    et al.
    Ecole Polytech Fed Lausanne, Learning Algorithms & Syst Lab, Lausanne, Switzerland..
    Kragic, Danica
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Trends and challenges in robot manipulation2019In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 364, no 6446, p. 1149-+Article, review/survey (Refereed)
    Abstract [en]

    Dexterous manipulation is one of the primary goals in robotics. Robots with this capability could sort and package objects, chop vegetables, and fold clothes. As robots come to work side by side with humans, they must also become human-aware. Over the past decade, research has made strides toward these goals. Progress has come from advances in visual and haptic perception and in mechanics in the form of soft actuators that offer a natural compliance. Most notably, immense progress in machine learning has been leveraged to encapsulate models of uncertainty and to support improvements in adaptive and robust control. Open questions remain in terms of how to enable robots to deal with the most unpredictable agent of all, the human.

  • 49.
    Correia, Filipa
    et al.
    Univ Lisbon, INESC ID, Inst Super Tecn, Lisbon, Portugal..
    Mascarenhas, Samuel F.
    Univ Lisbon, INESC ID, Inst Super Tecn, Lisbon, Portugal..
    Gomes, Samuel
    Univ Lisbon, INESC ID, Inst Super Tecn, Lisbon, Portugal..
    Arriaga, Patricia
    CIS IUL, Inst Univ Lisboa ISCTE IUL, Lisbon, Portugal..
    Leite, Iolanda
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Prada, Rui
    Univ Lisbon, INESC ID, Inst Super Tecn, Lisbon, Portugal..
    Melo, Francisco S.
    Univ Lisbon, INESC ID, Inst Super Tecn, Lisbon, Portugal..
    Paiva, Ana
    Univ Lisbon, INESC ID, Inst Super Tecn, Lisbon, Portugal..
    Exploring Prosociality in Human-Robot Teams2019In: HRI '19: 2019 14TH ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION, IEEE , 2019, p. 143-151Conference paper (Refereed)
    Abstract [en]

    This paper explores the role of prosocial behaviour when people team up with robots in a collaborative game that presents a social dilemma similar to a public goods game. An experiment was conducted with the proposed game in which each participant joined a team with a prosocial robot and a selfish robot. During 5 rounds of the game, each player chooses between contributing to the team goal (cooperate) or contributing to his individual goal (defect). The prosociality level of the robots only affects their strategies to play the game, as one always cooperates and the other always defects. We conducted a user study at the office of a large corporation with 70 participants where we manipulated the game result (winning or losing) in a between-subjects design. Results revealed two important considerations: (1) the prosocial robot was rated more positively in terms of its social attributes than the selfish robot, regardless of the game result; (2) the perception of competence, the responsibility attribution (blame/credit), and the preference for a future partner revealed significant differences only in the losing condition. These results yield important concerns for the creation of robotic partners, the understanding of group dynamics and, from a more general perspective, the promotion of a prosocial society.

  • 50.
    Kucherenko, Taras
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Hasegawa, Dai
    Henter, Gustav Eje
    KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
    Kaneko, Naoshi
    Kjellström, Hedvig
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Analyzing Input and Output Representations for Speech-Driven Gesture Generation2019In: 19th ACM International Conference on Intelligent Virtual Agents, New York, NY, USA: ACM Publications, 2019Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel framework for automatic speech-driven gesture generation, applicable to human-agent interaction including both virtual agents and robots. Specifically, we extend recent deep-learning-based, data-driven methods for speech-driven gesture generation by incorporating representation learning. Our model takes speech as input and produces gestures as output, in the form of a sequence of 3D coordinates.

    Our approach consists of two steps. First, we learn a lower-dimensional representation of human motion using a denoising autoencoder neural network, consisting of a motion encoder MotionE and a motion decoder MotionD. The learned representation preserves the most important aspects of the human pose variation while removing less relevant variation. Second, we train a novel encoder network SpeechE to map from speech to a corresponding motion representation with reduced dimensionality. At test time, the speech encoder and the motion decoder networks are combined: SpeechE predicts motion representations based on a given speech signal and MotionD then decodes these representations to produce motion sequences.

    We evaluate different representation sizes in order to find the most effective dimensionality for the representation. We also evaluate the effects of using different speech features as input to the model. We find that mel-frequency cepstral coefficients (MFCCs), alone or combined with prosodic features, perform the best. The results of a subsequent user study confirm the benefits of the representation learning.

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