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    Jamisola RS, Kormushev P, Bicchi A, Caldwell DGet al., 2014,

    Haptic Exploration of Unknown Surfaces with Discontinuities

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 1255-1260, ISSN: 2153-0858
    Ahmadzadeh SR, Kormushev P, Caldwell DG, 2013,

    Visuospatial Skill Learning for Object Reconfiguration Tasks

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 685-691, ISSN: 2153-0858
    Ahmadzadeh SR, Kormushev P, Caldwell DG, 2013,

    Autonomous robotic valve turning: A hierarchical learning approach

    , 2013 IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 4629-4634, ISSN: 1050-4729

    Autonomous valve turning is an extremely challenging task for an Autonomous Underwater Vehicle (AUV). To resolve this challenge, this paper proposes a set of different computational techniques integrated in a three-layer hierarchical scheme. Each layer realizes specific subtasks to improve the persistent autonomy of the system. In the first layer, the robot acquires the motor skills of approaching and grasping the valve by kinesthetic teaching. A Reactive Fuzzy Decision Maker (RFDM) is devised in the second layer which reacts to the relative movement between the valve and the AUV, and alters the robot's movement accordingly. Apprenticeship learning method, implemented in the third layer, performs tuning of the RFDM based on expert knowledge. Although the long-term goal is to perform the valve turning task on a real AUV, as a first step the proposed approach is tested in a laboratory environment. © 2013 IEEE.

    Calinon S, Kormushev P, Caldwell DG, 2013,

    Compliant skills acquisition and multi-optima policy search with EM-based reinforcement learning

    , ROBOTICS AND AUTONOMOUS SYSTEMS, Vol: 61, Pages: 369-379, ISSN: 0921-8890
    Carrera A, Carreras M, Kormushev P, Palomeras N, Nagappa Set al., 2013,

    Towards valve turning with an AUV using Learning by Demonstration

    , MTS/IEEE OCEANS Conference, Publisher: IEEE, ISSN: 0197-7385
    Colasanto L, Kormushev P, Tsagarakis N, Caldwell DGet al., 2013,

    Optimization of a compact model for the compliant humanoid robot COMAN using reinforcement learning

    , Cybernetics and Information Technologies, Vol: 12, Pages: 76-85, ISSN: 1311-9702

    Coman is a compliant humanoid robot. The introduction of passive compliance in some of its joints affects the dynamics of the whole system. Unlike traditional stiff robots, there is a deflection of the joint angle with respect to the desired one whenever an external torque is applied. Following a bottom up approach, the dynamic equations of the joints are defined first. Then, a new model which combines the inverted pendulum approach with a three-dimensional (Cartesian) compliant model at the level of the center of mass is proposed. This compact model is based on some assumptions that reduce the complexity but at the same time affect the precision. To address this problem, additional parameters are inserted in the model equation and an optimization procedure is performed using reinforcement learning. The optimized model is experimentally validated on the COMAN robot using several ZMP-based walking gaits.

    Dallali H, Mosadeghzad M, Medrano-Cerda GA, Docquier N, Kormushev P, Tsagarakis N, Li Z, Caldwell Det al., 2013,

    Development of a Dynamic Simulator for a Compliant Humanoid Robot Based on a Symbolic Multibody Approach

    , IEEE International Conference on Mechatronics (ICM), Publisher: IEEE
    Karras GC, Bechlioulis CP, Leonetti M, Palomeras N, Kormushev P, Kyriakopoulos KJ, Caldwell DGet al., 2013,

    On-line Identification of Autonomous Underwater Vehicles Through Global Derivative-free Optimization

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 3859-3864, ISSN: 2153-0858
    Kormushev P, Caldwell DG, 2013,

    Comparative Evaluation of Reinforcement Learning with Scalar Rewards and Linear Regression with Multidimensional Feedback


    This paper presents a comparative evaluation of two learningapproaches. The first approach is a conventional reinforcement learningalgorithm for direct policy search which uses scalar rewards by definition.The second approach is a custom linear regression based algorithm thatuses multidimensional feedback instead of a scalar reward. The two approachesare evaluated in simulation on a common benchmark problem:an aiming task where the goal is to learn the optimal parameters for aimingthat result in hitting as close as possible to a given target. The comparativeevaluation shows that the multidimensional feedback providesa significant advantage over the scalar reward, resulting in an order-ofmagnitudespeed-up of the convergence. A real-world experiment with ahumanoid robot confirms the results from the simulation and highlightsthe importance of multidimensional feedback for fast learning

    Kryczka P, Kormushev P, Hashimoto K, Lim H-O, Tsagarakis NG, Caldwell DG, Takanishi Aet al., 2013,

    Hybrid gait pattern generator capable of rapid and dynamically consistent pattern regeneration

    , 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Publisher: IEEE, Pages: 475-480, ISSN: 2325-033X
    Kryczka P, Shiguematsu YM, Kormushev P, Hashimoto K, Lim H-O, Takanishi Aet al., 2013,

    Towards Dynamically Consistent Real-time Gait Pattern Generation for Full-size Humanoid Robots

    , IEEE International Conference on Robotics and Biomimetics (ROBIO), Publisher: IEEE, Pages: 1408-1413
    Carrera A, Ahmadzadeh SR, Ajoudani A, Kormushev P, Carreras M, Caldwell DGet al., 2012,

    Towards autonomous robotic valve turning

    , Cybernetics and Information Technologies, Vol: 12, Pages: 17-26, ISSN: 1311-9702

    In this paper an autonomous intervention robotic task to learn the skill of grasping and turning a valve is described. To resolve this challenge a set of different techniques are proposed, each one realizing a specific task and sending information to the others in a Hardware-In-Loop (HIL) simulation. To improve the estimation of the valve position, an Extended Kalman Filter is designed. Also to learn the trajectory to follow with the robotic arm, Imitation Learning approach is used. In addition, to perform safely the task a fuzzy system is developed which generates appropriate decisions. Although the achievement of this task will be used in an Autonomous Underwater Vehicle, for the first step this idea has been tested in a laboratory environment with an available robot and a sensor.

    Dallali H, Kormushev P, Li Z, Caldwell Det al., 2012,

    On global optimization of walking gaits for the compliant humanoid robot, COMAN using reinforcement learning

    , Cybernetics and Information Technologies, Vol: 12, Pages: 39-52, ISSN: 1311-9702

    In ZMP trajectory generation using simple models, often a considerable amount of trials and errors are involved to obtain locally stable gaits by manually tuning the gait parameters. In this paper a 15 degrees of Freedom dynamic model of a compliant humanoid robot is used, combined with reinforcement learning to perform global search in the parameter space to produce stable gaits. It is shown that for a given speed, multiple sets of parameters, namely step sizes and lateral sways, are obtained by the learning algorithm which can lead to stable walking. The resulting set of gaits can be further studied in terms of parameter sensitivity and also to include additional optimization criteria to narrow down the chosen walking trajectories for the humanoid robot.

    Kormushev P, Calinon S, Caldwell DG, Ugurlu Bet al., 2012,

    Challenges for the Policy Representation when Applying Reinforcement Learning in Robotics

    , International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, ISSN: 2161-4393
    Leonetti M, Kormushev P, Sagratella S, 2012,

    Combining local and global direct derivative-free optimization for reinforcement learning

    , Cybernetics and Information Technologies, Vol: 12, Pages: 53-65, ISSN: 1311-9702

    We consider the problem of optimization in policy space for reinforcement learning. While a plethora of methods have been applied to this problem, only a narrow category of them proved feasible in robotics. We consider the peculiar characteristics of reinforcement learning in robotics, and devise a combination of two algorithms from the literature of derivative-free optimization. The proposed combination is well suited for robotics, as it involves both off-line learning in simulation and on-line learning in the real environment. We demonstrate our approach on a real-world task, where an Autonomous Underwater Vehicle has to survey a target area under potentially unknown environment conditions. We start from a given controller, which can perform the task under foreseeable conditions, and make it adaptive to the actual environment.

    Kormushev P, Calinon S, Caldwell DG, 2011,

    Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input

    , ADVANCED ROBOTICS, Vol: 25, Pages: 581-603, ISSN: 0169-1864
    Kormushev P, Nenchev DN, Calinon S, Caldwell DGet al., 2011,

    Upper-body Kinesthetic Teaching of a Free-standing Humanoid Robot

    , IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, ISSN: 1050-4729
    Kormushev P, Nomoto K, Dong F, Hirota Ket al., 2011,

    Time hopping technique for faster reinforcement learning in simulations

    , Cybernetics and Information Technologies, Vol: 11, Pages: 42-59, ISSN: 1311-9702

    A technique called Time Hopping is proposed for speeding up reinforcement learning algorithms. It is applicable to continuous optimization problems running in computer simulations. Making shortcuts in time by hopping between distant states combined with off-policy reinforcement learning allows the technique to maintain higher learning rate. Experiments on a simulated biped crawling robot confirm that Time Hopping can accelerate the learning process more than seven times.

    Kormushev P, Ugurlu B, Calinon S, Tsagarakis NG, Caldwell DGet al., 2011,

    Bipedal Walking Energy Minimization by Reinforcement Learning with Evolving Policy Parameterization

    , IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEE, Pages: 318-324, ISSN: 2153-0858
    Kormushev P, Calinon S, Caldwell DG, 2010,

    Robot Motor Skill Coordination with EM-based Reinforcement Learning

    , IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEE, Pages: 3232-3237, ISSN: 2153-0858

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