80 results found
Kanajar P, Caldwell DG, Kormushev P, 2017, Climbing over large obstacles with a humanoid robot via multi-contact motion planning, Pages: 1202-1209
© 2017 IEEE. Incremental progress in humanoid robot locomotion over the years has achieved important capabilities such as navigation over flat or uneven terrain, stepping over small obstacles and climbing stairs. However, the locomotion research has mostly been limited to using only bipedal gait and only foot contacts with the environment, using the upper body for balancing without considering additional external contacts. As a result, challenging locomotion tasks like climbing over large obstacles relative to the size of the robot have remained unsolved. In this paper, we address this class of open problems with an approach based on multi-body contact motion planning guided through physical human demonstrations. Our goal is to make the humanoid locomotion problem more tractable by taking advantage of objects in the surrounding environment instead of avoiding them. We propose a multi-contact motion planning algorithm for humanoid robot locomotion which exploits the whole-body motion and multi-body contacts including both the upper and lower body limbs. The proposed motion planning algorithm is applied to a challenging task of climbing over a large obstacle. We demonstrate successful execution of the climbing task in simulation using our multi-contact motion planning algorithm initialized via a transfer from real-world human demonstrations of the task and further optimized.
Ahmadzadeh SR, Mastrogiovanni F, Kormushev P, 2016, Visuospatial Skill Learning for Robots
A novel skill learning approach is proposed that allows a robot to acquirehuman-like visuospatial skills for object manipulation tasks. Visuospatialskills are attained by observing spatial relationships among objects throughdemonstrations. The proposed Visuospatial Skill Learning (VSL) is a goal-basedapproach that focuses on achieving a desired goal configuration of objectsrelative to one another while maintaining the sequence of operations. VSL iscapable of learning and generalizing multi-operation skills from a singledemonstration, while requiring minimum prior knowledge about the objects andthe environment. In contrast to many existing approaches, VSL offerssimplicity, efficiency and user-friendly human-robot interaction. We also showthat VSL can be easily extended towards 3D object manipulation tasks, simply byemploying point cloud processing techniques. In addition, a robot learningframework, VSL-SP, is proposed by integrating VSL, Imitation Learning, and aconventional planning method. In VSL-SP, the sequence of performed actions arelearned using VSL, while the sensorimotor skills are learned using aconventional trajectory-based learning approach. such integration easilyextends robot capabilities to novel situations, even by users withoutprogramming ability. In VSL-SP the internal planner of VSL is integrated withan existing action-level symbolic planner. Using the underlying constraints ofthe task and extracted symbolic predicates, identified by VSL, symbolicrepresentation of the task is updated. Therefore the planner maintains ageneralized representation of each skill as a reusable action, which can beused in planning and performed independently during the learning phase. Theproposed approach is validated through several real-world experiments.
Jamisola RS, Kormushev PS, Roberts RG, et al., 2016, Task-Space Modular Dynamics for Dual-Arms Expressed through a Relative Jacobian, JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, Vol: 83, Pages: 205-218, ISSN: 0921-0296
Kormushev PS, 2016, Robot Learning for Persistent Autonomy, Handling Uncertainty and Networked Structure in Robot Control, Publisher: Springer, ISBN: 9783319263274
Chapter. 1. Robot. Learning. for. Persistent. Autonomy. Petar Kormushev and Seyed Reza Ahmadzadeh Abstract Autonomous robots are not very good at being autonomous. They work well in structured environments, but fail quickly in the real ...
Maurelli F, Lane D, Kormushev P, et al., 2016, The PANDORA project: a success story in AUV autonomy, OCEANS Conference, Publisher: IEEE, ISSN: 0197-7385
Palomeras N, Carrera A, Hurtos N, et al., 2016, Toward persistent autonomous intervention in a subsea panel, AUTONOMOUS ROBOTS, Vol: 40, Pages: 1279-1306, ISSN: 0929-5593
Ahmadzadeh SR, Paikan A, Mastrogiovanni F, et al., 2015, Learning Symbolic Representations of Actions from Human Demonstrations, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE COMPUTER SOC, Pages: 3801-3808, ISSN: 1050-4729
Bimbo J, Kormushev P, Althoefer K, et al., 2015, Global estimation of an object's pose using tactile sensing, ADVANCED ROBOTICS, Vol: 29, Pages: 363-374, ISSN: 0169-1864
Carrera A, Palomeras N, Hurtos N, et al., 2015, Cognitive system for autonomous underwater intervention, PATTERN RECOGNITION LETTERS, Vol: 67, Pages: 91-99, ISSN: 0167-8655
Carrera A, Palomeras N, Hurtos N, et al., 2015, Learning multiple strategies to perform a valve turning with underwater currents using an I-AUV, Oceans 2015 Genova, Publisher: IEEE
Jamali N, Kormushev P, Vinas AC, et al., 2015, Underwater Robot-Object Contact Perception using Machine Learning on Force/Torque Sensor Feedback, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE COMPUTER SOC, Pages: 3915-3920, ISSN: 1050-4729
Jamisola RS, Kormushev P, Caldwell DG, et al., 2015, Modular Relative Jacobian for Dual-Arms and the Wrench Transformation Matrix, Proceedings of the 2015 7th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) And Robotics, Automation and Mechatronics (RAM), Publisher: IEEE
Kormushev P, Demiris Y, Caldwell DG, 2015, Encoderless Position Control of a Two-Link Robot Manipulator, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE COMPUTER SOC, Pages: 943-949, ISSN: 1050-4729
Kormushev P, Demiris Y, Caldwell DG, 2015, Kinematic-free Position Control of a 2-DOF Planar Robot Arm, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 5518-5525, ISSN: 2153-0858
Kryczka P, Kormushev P, Tsagarakis NG, et al., 2015, Online Regeneration of Bipedal Walking Gait Pattern Optimizing Footstep Placement and Timing, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 3352-3357, ISSN: 2153-0858
Lane DM, Maurelli F, Kormushev P, et al., 2015, PANDORA - Persistent autonomy through learning, adaptation, observation and replanning, Pages: 238-243
© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. PANDORA is a EU FP7 project that is developing new computational methods to make underwater robots Persistently Autonomous, significantly reducing the frequency of assistance requests. The aim of the project is to extend the range of tasks that can be carried on autonomously and increase their complexity while reducing the need for operator assistances. Dynamic adaptation to the change of conditions is very important while addressing autonomy in the real world and not just in well-known situation. The key of Pandora is the ability to recognise failure and respond to it, at all levels of abstraction. Under the guidance of major industrial players, validation tasks of inspection, cleaning and valve turning have been trialled with partners' AUVs in Scotland and Spain.
Takano W, Asfour T, Kormushev P, 2015, Special Issue on Humanoid Robotics PREFACE, ADVANCED ROBOTICS, Vol: 29, Pages: 301-301, ISSN: 0169-1864
Ahmadzadeh SR, Kormushev P, Caldwell DG, 2014, Multi-objective reinforcement learning for AUV thruster failure recovery
© 2014 IEEE. This paper investigates learning approaches for discovering fault-tolerant control policies to overcome thruster failures in Autonomous Underwater Vehicles (AUV). The proposed approach is a model-based direct policy search that learns on an on-board simulated model of the vehicle. When a fault is detected and isolated the model of the AUV is reconfigured according to the new condition. To discover a set of optimal solutions a multi-objective reinforcement learning approach is employed which can deal with multiple conflicting objectives. Each optimal solution can be used to generate a trajectory that is able to navigate the AUV towards a specified target while satisfying multiple objectives. The discovered policies are executed on the robot in a closed-loop using AUV's state feedback. Unlike most existing methods which disregard the faulty thruster, our approach can also deal with partially broken thrusters to increase the persistent autonomy of the AUV. In addition, the proposed approach is applicable when the AUV either becomes under-actuated or remains redundant in the presence of a fault. We validate the proposed approach on the model of the Girona500 AUV.
Ahmadzadeh SR, Kormushev P, Caldwell DG, 2014, Multi-Objective Reinforcement Learning for AUV Thruster Failure Recovery, IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), Publisher: IEEE, Pages: 103-110
Ahmadzadeh SR, Kormushev P, Jamisola RS, et al., 2014, Learning Reactive Robot Behavior for Autonomous Valve Turning, 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Publisher: IEEE, Pages: 366-373, ISSN: 2164-0572
Ahmadzadeh SR, Leonetti M, Carrera A, et al., 2014, Online Discovery of AUV Control Policies to Overcome Thruster Failures, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 6522-6528, ISSN: 1050-4729
Carrera A, Karras G, Bechlioulis C, et al., 2014, Improving a Learning by Demonstration framework for Intervention AUVs by means of an UVMS controller
Carrera A, Palomeras N, Hurtós N, et al., 2014, Learning by demonstration applied to underwater intervention, Pages: 95-104, ISSN: 0922-6389
Performing subsea intervention tasks is a challenge due to the complexities of the underwater domain. We propose to use a learning by demonstraition algorithm to intuitively teach an intervention autonomous underwater vehicle (I-AUV) how to perform a given task. Taking as an input few operator demonstrations, the algorithm generalizes the task into a model and simultaneously controls the vehicle and the manipulator (using 8 degrees of freedom) to reproduce the task. A complete framework has been implemented in order to integrate the LbD algorithm with the different onboard sensors and actuators. A valve turning intervention task is used to validate the full framework through real experiments conducted in a water tank.
Carrera A, Palomeras N, Ribas D, et al., 2014, An Intervention-AUV learns how to perform an underwater valve turning
© 2014 IEEE. Intervention autonomous underwater vehicles (I-AUVs) are a promising platform to perform intervention task in underwater environments, replacing current methods like remotely operate underwater vehicles (ROVs) and manned sub-mersibles that are more expensive. This article proposes a complete system including all the necessary elements to perform a valve turning task using an I-AUV. The knowledge of an operator to perform the task is transmitted to an I-AUV by a learning by demonstration (LbD) algorithm. The algorithm learns the trajectory of the vehicle and the end-effector to accomplish the valve turning. The method has shown its feasibility in a controlled environment repeating the learned task with different valves and configurations.
Dallali H, Kormushev P, Tsagarakis NG, et al., 2014, Can Active Impedance Protect Robots from Landing Impact?, 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Publisher: IEEE, Pages: 1022-1027, ISSN: 2164-0572
Jamali N, Kormushev P, Ahmadzadeh SR, et al., 2014, Covariance Analysis as a Measure of Policy Robustness in Reinforcement Learning, OCEANS'14 MTS/IEEE
—In this paper we propose covariance analysis as ametric for reinforcement learning to improve the robustness ofa learned policy. The local optima found during the explorationare analyzed in terms of the total cumulative reward and thelocal behavior of the system in the neighborhood of the optima.The analysis is performed in the solution space to select a policythat exhibits robustness in uncertain and noisy environments.We demonstrate the utility of the method using our previouslydeveloped system where an autonomous underwater vehicle(AUV) has to recover from a thruster failure. When a failure isdetected the recovery system is invoked, which uses simulationsto learn a new controller that utilizes the remaining functioningthrusters to achieve the goal of the AUV, that is, to reach a targetposition. In this paper, we use covariance analysis to examinethe performance of the top, n, policies output by the previousalgorithm. We propose a scoring metric that uses the output ofthe covariance analysis, the time it takes the AUV to reach thetarget position and the distance between the target position andthe AUV’s final position. The top polices are simulated in a noisyenvironment and evaluated using the proposed scoring metric toanalyze the effect of noise on their performance. The policy thatexhibits more tolerance to noise is selected. We show experimentalresults where covariance analysis successfully selects a morerobust policy that was ranked lower by the original algorithm.
Jamali N, Kormushev P, Ahmadzadeh SR, et al., 2014, Covariance analysis as a measure of policy robustness
© 2014 IEEE. In this paper we propose covariance analysis as a metric for reinforcement learning to improve the robustness of a learned policy. The local optima found during the exploration are analyzed in terms of the total cumulative reward and the local behavior of the system in the neighborhood of the optima. The analysis is performed in the solution space to select a policy that exhibits robustness in uncertain and noisy environments. We demonstrate the utility of the method using our previously developed system where an autonomous underwater vehicle (AUV) has to recover from a thruster failure . When a failure is detected the recovery system is invoked, which uses simulations to learn a new controller that utilizes the remaining functioning thrusters to achieve the goal of the AUV, that is, to reach a target position. In this paper, we use covariance analysis to examine the performance of the top, n, policies output by the previous algorithm. We propose a scoring metric that uses the output of the covariance analysis, the time it takes the AUV to reach the target position and the distance between the target position and the AUV's final position. The top polices are simulated in a noisy environment and evaluated using the proposed scoring metric to analyze the effect of noise on their performance. The policy that exhibits more tolerance to noise is selected. We show experimental results where covariance analysis successfully selects a more robust policy that was ranked lower by the original algorithm.
Jamali N, Kormushev P, Caldwell DG, 2014, Robot-Object Contact Perception using Symbolic Temporal Pattern Learning, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 6542-6548, ISSN: 1050-4729
Jamisola RS, Kormushev P, Bicchi A, et 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
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