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  • 1.
    Kragic, Danica
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Gustafson, Joakim
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Tal, musik och hörsel, TMH.
    Karaoǧuz, Hakan
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Jensfelt, Patric
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Krug, Robert
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Interactive, collaborative robots: Challenges and opportunities2018Ingår i: IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence , 2018, s. 18-25Konferensbidrag (Refereegranskat)
    Abstract [en]

    Robotic technology has transformed manufacturing industry ever since the first industrial robot was put in use in the beginning of the 60s. The challenge of developing flexible solutions where production lines can be quickly re-planned, adapted and structured for new or slightly changed products is still an important open problem. Industrial robots today are still largely preprogrammed for their tasks, not able to detect errors in their own performance or to robustly interact with a complex environment and a human worker. The challenges are even more serious when it comes to various types of service robots. Full robot autonomy, including natural interaction, learning from and with human, safe and flexible performance for challenging tasks in unstructured environments will remain out of reach for the foreseeable future. In the envisioned future factory setups, home and office environments, humans and robots will share the same workspace and perform different object manipulation tasks in a collaborative manner. We discuss some of the major challenges of developing such systems and provide examples of the current state of the art.

  • 2.
    Krug, Robert
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Bekiroglu, Yasemin
    Vicarious AI, San Francisco, CA USA..
    Kragic, Danica
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Roa, Maximo A.
    German Aerosp Ctr DLR, Inst Robot & Mechatron, D-82234 Wessling, Germany..
    Evaluating the Quality of Non-Prehensile Balancing Grasps2018Ingår i: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, s. 4215-4220Konferensbidrag (Refereegranskat)
    Abstract [en]

    Assessing grasp quality and, subsequently, predicting grasp success is useful for avoiding failures in many autonomous robotic applications. In addition, interest in non-prehensile grasping and manipulation has been growing as it offers the potential for a large increase in dexterity. However, while force-closure grasping has been the subject of intense study for many years, few existing works have considered quality metrics for non-prehensile grasps. Furthermore, no studies exist to validate them in practice. In this work we use a real-world data set of non-prehensile balancing grasps and use it to experimentally validate a wrench-based quality metric by means of its grasp success prediction capability. The overall accuracy of up to 84% is encouraging and in line with existing results for force-closure grasps.

  • 3.
    Krug, Robert
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.
    Bekiroglu, Yasemin
    Roa, Maximo
    Grasp Quality Evaluation Done Right: How Assumed Contact Force Bounds Affect Wrench-Based Quality Metrics2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    Wrench-based quality metrics play an important role in many applications such as grasp planning or grasp success prediction. In this work, we study the following discrepancy which is frequently overlooked in practice: the quality metrics are commonly computed under the assumption of sum-magnitude bounded contact forces, but the corresponding grasps are executed by a fully actuated device where the contact forces are limited independently. By means of experiments carried out in simulation and on real hardware, we show that in this setting the values of these metrics are severely underestimated. This can lead to erroneous conclusions regarding the actual capabilities of the grasps under consideration. Our findings highlight the importance of matching the physical properties of the task and the grasping device with the chosen quality metrics.

  • 4.
    Lundell, Jens
    et al.
    Aalto Univ, Dept Elect Engn & Automat, Intelligent Robot Grp, Helsinki, Finland..
    Krug, Robert
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Schaffernicht, Erik
    Orebro Univ, AASS Res Ctr, Orebro, Sweden..
    Stoyanov, Todor
    Orebro Univ, AASS Res Ctr, Orebro, Sweden..
    Kyrki, Ville
    Aalto Univ, Dept Elect Engn & Automat, Intelligent Robot Grp, Helsinki, Finland..
    Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization2018Ingår i: 2018 IEEE-RAS 18TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTS (HUMANOIDS) / [ed] Asfour, T, Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 132-138Konferensbidrag (Refereegranskat)
    Abstract [en]

    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.

  • 5.
    Salvado, Joao
    et al.
    Orebro Univ, AASS Res Ctr, Orebro, Sweden..
    Krug, Robert
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Mansouri, Masoumeh
    Orebro Univ, AASS Res Ctr, Orebro, Sweden..
    Pecora, Federico
    Orebro Univ, AASS Res Ctr, Orebro, Sweden..
    Motion Planning and Goal Assignment for Robot Fleets Using Trajectory Optimization2018Ingår i: 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) / [ed] Maciejewski, AA Okamura, A Bicchi, A Stachniss, C Song, DZ Lee, DH Chaumette, F Ding, H Li, JS Wen, J Roberts, J Masamune, K Chong, NY Amato, N Tsagwarakis, N Rocco, P Asfour, T Chung, WK Yasuyoshi, Y Sun, Y Maciekeski, T Althoefer, K AndradeCetto, J Chung, WK Demircan, E Dias, J Fraisse, P Gross, R Harada, H Hasegawa, Y Hayashibe, M Kiguchi, K Kim, K Kroeger, T Li, Y Ma, S Mochiyama, H Monje, CA Rekleitis, I Roberts, R Stulp, F Tsai, CHD Zollo, L, IEEE , 2018, s. 7939-7946Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper is concerned with automating fleets of autonomous robots. This involves solving a multitude of problems, including goal assignment, motion planning, and coordination, while maximizing some performance criterion. While methods for solving these sub-problems have been studied, they address only a facet of the overall problem, and make strong assumptions on the use-case, on the environment, or on the robots in the fleet. In this paper, we formulate the overall fleet management problem in terms of Optimal Control. We describe a scheme for solving this problem in the particular case of fleets of non-holonomic robots navigating in an environment with obstacles. The method is based on a two-phase approach, whereby the first phase solves for fleet-wide boolean decision variables via Mixed Integer Quadratic Programming, and the second phase solves for real-valued variables to obtain an optimized set of trajectories for the fleet. Examples showcasing the features of the method are illustrated, and the method is validated experimentally.

  • 6.
    Stoyanov, Todor
    et al.
    Orebro Univ, Ctr Appl Autonomous Sensor Syst AASS, Fakultetsgatan 1, S-70182 Orebro, Sweden..
    Krug, Robert
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Kiselev, Andrey
    Orebro Univ, Ctr Appl Autonomous Sensor Syst AASS, Fakultetsgatan 1, S-70182 Orebro, Sweden..
    Sun, Da
    Orebro Univ, Ctr Appl Autonomous Sensor Syst AASS, Fakultetsgatan 1, S-70182 Orebro, Sweden..
    Loutfi, Amy
    Orebro Univ, Ctr Appl Autonomous Sensor Syst AASS, Fakultetsgatan 1, S-70182 Orebro, Sweden..
    Assisted Telemanipulation: A Stack-of-Tasks Approach to Remote Manipulator Control2018Ingår i: 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) / [ed] Maciejewski, AA Okamura, A Bicchi, A Stachniss, C Song, DZ Lee, DH Chaumette, F Ding, H Li, JS Wen, J Roberts, J Masamune, K Chong, NY Amato, N Tsagwarakis, N Rocco, P Asfour, T Chung, WK Yasuyoshi, Y Sun, Y Maciekeski, T Althoefer, K AndradeCetto, J Chung, WK Demircan, E Dias, J Fraisse, P Gross, R Harada, H Hasegawa, Y Hayashibe, M Kiguchi, K Kim, K Kroeger, T Li, Y Ma, S Mochiyama, H Monje, CA Rekleitis, I Roberts, R Stulp, F Tsai, CHD Zollo, L, IEEE , 2018, s. 6640-6645Konferensbidrag (Refereegranskat)
    Abstract [en]

    This article presents an approach for assisted teleoperation of a robot arm, formulated within a real-time stack-of-tasks (SoT) whole-body motion control framework. The approach leverages the hierarchical nature of the SoT framework to integrate operator commands with assistive tasks, such as joint limit and obstacle avoidance or automatic gripper alignment. Thereby some aspects of the teleoperation problem are delegated to the controller and carried out autonomously. The key contributions of this work are two-fold: the first is a method for unobtrusive integration of autonomy in a telemanipulation system; and the second is a user study evaluation of the proposed system in the context of teleoperated pick-and-place tasks. The proposed approach of assistive control was found to result in higher grasp success rates and shorter trajectories than achieved through manual control, without incurring additional cognitive load to the operator.

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