Publications

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  • Journal article
    Kormushev P, Ugurlu B, Caldwell DG, Tsagarakis NGet al., 2019,

    Learning to exploit passive compliance for energy-efficient gait generation on a compliant humanoid

    , Autonomous Robots, Vol: 43, Pages: 79-95, ISSN: 1573-7527

    Modern humanoid robots include not only active compliance but also passive compliance. Apart from improved safety and dependability, availability of passive elements, such as springs, opens up new possibilities for improving the energy efficiency. With this in mind, this paper addresses the challenging open problem of exploiting the passive compliance for the purpose of energy efficient humanoid walking. To this end, we develop a method comprising two parts: an optimization part that finds an optimal vertical center-of-mass trajectory, and a walking pattern generator part that uses this trajectory to produce a dynamically-balanced gait. For the optimization part, we propose a reinforcement learning approach that dynamically evolves the policy parametrization during the learning process. By gradually increasing the representational power of the policy parametrization, it manages to find better policies in a faster and computationally efficient way. For the walking generator part, we develop a variable-center-of-mass-height ZMP-based bipedal walking pattern generator. The method is tested in real-world experiments with the bipedal robot COMAN and achieves a significant 18% reduction in the electric energy consumption by learning to efficiently use the passive compliance of the robot.
  • Conference paper
    Wang K, Shah A, Kormushev P, 2018,

    SLIDER: A Bipedal Robot with Knee-less Legs and Vertical Hip Sliding Motion

    , 21st International Conference on Climbing and Walking Robots and Support Technologies for Mobile Machines (CLAWAR 2018)
  • Conference paper
    Saputra RP, Kormushev P, 2018,

    Casualty detection for mobile rescue robots via ground-projected point clouds

    , Towards Autonomous Robotic Systems (TAROS) 2018, Publisher: Springer, Cham, Pages: 473-475, ISSN: 0302-9743

    In order to operate autonomously, mobile rescue robots needto be able to detect human casualties in disaster situations. In this paper,we propose a novel method for autonomous detection of casualties lyingdown on the ground based on point-cloud data. This data can be obtainedfrom different sensors, such as an RGB-D camera or a 3D LIDAR sensor.The method is based on a ground-projected point-cloud (GPPC) imageto achieve human body shape detection. A preliminary experiment hasbeen conducted using the RANSAC method for floor detection and, theHOG feature and the SVM classifier to detect human body shape. Theresults show that the proposed method succeeds to identify a casualtyfrom point-cloud data in a wide range of viewing angles.
  • Conference paper
    Pardo F, Tavakoli A, Levdik V, Kormushev Pet al., 2018,

    Time limits in reinforcement learning

    , International Conference on Machine Learning, Pages: 4042-4051

    In reinforcement learning, it is common to let anagent interact for a fixed amount of time with itsenvironment before resetting it and repeating theprocess in a series of episodes. The task that theagent has to learn can either be to maximize itsperformance over (i) that fixed period, or (ii) anindefinite period where time limits are only usedduring training to diversify experience. In thispaper, we provide a formal account for how timelimits could effectively be handled in each of thetwo cases and explain why not doing so can causestate-aliasing and invalidation of experience re-play, leading to suboptimal policies and traininginstability. In case (i), we argue that the termi-nations due to time limits are in fact part of theenvironment, and thus a notion of the remainingtime should be included as part of the agent’s in-put to avoid violation of the Markov property. Incase (ii), the time limits are not part of the envi-ronment and are only used to facilitate learning.We argue that this insight should be incorporatedby bootstrapping from the value of the state atthe end of each partial episode. For both cases,we illustrate empirically the significance of ourconsiderations in improving the performance andstability of existing reinforcement learning algo-rithms, showing state-of-the-art results on severalcontrol tasks.
  • Conference paper
    Saputra RP, Kormushev P, 2018,

    ResQbot: a mobile rescue robot with immersive teleperception for casualty extraction

    , Towards Autonomous Robotic Systems (TAROS) 2018, Publisher: Springer International Publishing AG, part of Springer Nature, Pages: 209-220, ISSN: 0302-9743

    In this work, we propose a novel mobile rescue robot equipped with an immersive stereoscopic teleperception and a teleoperation control. This robot is designed with the capability to perform safely a casualty-extraction procedure. We have built a proof-of-concept mobile rescue robot called ResQbot for the experimental platform. An approach called “loco-manipulation” is used to perform the casualty-extraction procedure using the platform. The performance of this robot is evaluated in terms of task accomplishment and safety by conducting a mock rescue experiment. We use a custom-made human-sized dummy that has been sensorised to be used as the casualty. In terms of safety, we observe several parameters during the experiment including impact force, acceleration, speed and displacement of the dummy’s head. We also compare the performance of the proposed immersive stereoscopic teleperception to conventional monocular teleperception. The results of the experiments show that the observed safety parameters are below key safety thresholds which could possibly lead to head or neck injuries. Moreover, the teleperception comparison results demonstrate an improvement in task-accomplishment performance when the operator is using the immersive teleperception.
  • Conference paper
    Wang K, Shah A, Kormushev P, 2018,

    SLIDER: a novel bipedal walking robot without knees

    , Towards Autonomous Robotic Systems (TAROS) 2018, Publisher: Springer International Publishing AG, part of Springer Nature, Pages: 471-472, ISSN: 0302-9743

    In this work, we propose a novel mobile rescue robot equipped with an immersive stereoscopic teleperception and a teleoperation control. This robot is designed with the capability to perform safely a casualty-extraction procedure. We have built a proof-of-concept mobile rescue robot called ResQbot for the experimental platform. An approach called “loco-manipulation” is used to perform the casualty-extraction procedure using the platform. The performance of this robot is evaluated in terms of task accomplishment and safety by conducting a mock rescue experiment. We use a custom-made human-sized dummy that has been sensorised to be used as the casualty. In terms of safety, we observe several parameters during the experiment including impact force, acceleration, speed and displacement of the dummy’s head. We also compare the performance of the proposed immersive stereoscopic teleperception to conventional monocular teleperception. The results of the experiments show that the observed safety parameters are below key safety thresholds which could possibly lead to head or neck injuries. Moreover, the teleperception comparison results demonstrate an improvement in task-accomplishment performance when the operator is using the immersive teleperception.
  • Conference paper
    Saputra RP, Kormushev P, 2018,

    Casualty detection from 3D point cloud data for autonomous ground mobile rescue robots

    , SSRR 2018, Publisher: IEEE

    One of the most important features of mobilerescue robots is the ability to autonomously detect casualties,i.e. human bodies, which are usually lying on the ground. Thispaper proposes a novel method for autonomously detectingcasualties lying on the ground using obtained 3D point-clouddata from an on-board sensor, such as an RGB-D camera ora 3D LIDAR, on a mobile rescue robot. In this method, theobtained 3D point-cloud data is projected onto the detectedground plane, i.e. floor, within the point cloud. Then, thisprojected point cloud is converted into a grid-map that isused afterwards as an input for the algorithm to detecthuman body shapes. The proposed method is evaluated byperforming detections of a human dummy, placed in differentrandom positions and orientations, using an on-board RGB-Dcamera on a mobile rescue robot called ResQbot. To evaluatethe robustness of the casualty detection method to differentcamera angles, the orientation of the camera is set to differentangles. The experimental results show that using the point-clouddata from the on-board RGB-D camera, the proposed methodsuccessfully detects the casualty in all tested body positions andorientations relative to the on-board camera, as well as in alltested camera angles.
  • Conference paper
    Saputra RP, Kormushev P, 2018,

    ResQbot: A mobile rescue robot for casualty extraction

    , 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI 2018), Publisher: Association for Computing Machinery, Pages: 239-240

    Performing search and rescue missions in disaster-struck environments is challenging. Despite the advances in the robotic search phase of the rescue missions, few works have been focused on the physical casualty extraction phase. In this work, we propose a mobile rescue robot that is capable of performing a safe casualty extraction routine. To perform this routine, this robot adopts a loco-manipulation approach. We have designed and built a mobile rescue robot platform called ResQbot as a proof of concept of the proposed system. We have conducted preliminary experiments using a sensorised human-sized dummy as a victim, to confirm that the platform is capable of performing a safe casualty extraction procedure.
  • Conference paper
    Tavakoli A, Pardo F, Kormushev P, 2018,

    Action branching architectures for deep reinforcement learning

    , AAAI 2018, Publisher: AAAI

    Discrete-action algorithms have been central to numerousrecent successes of deep reinforcement learning. However,applying these algorithms to high-dimensional action tasksrequires tackling the combinatorial increase of the numberof possible actions with the number of action dimensions.This problem is further exacerbated for continuous-actiontasks that require fine control of actions via discretization.In this paper, we propose a novel neural architecture fea-turing a shared decision module followed by several net-workbranches, one for each action dimension. This approachachieves a linear increase of the number of network outputswith the number of degrees of freedom by allowing a level ofindependence for each individual action dimension. To illus-trate the approach, we present a novel agent, called Branch-ing Dueling Q-Network (BDQ), as a branching variant ofthe Dueling Double Deep Q-Network (Dueling DDQN). Weevaluate the performance of our agent on a set of challeng-ing continuous control tasks. The empirical results show thatthe proposed agent scales gracefully to environments with in-creasing action dimensionality and indicate the significanceof the shared decision module in coordination of the dis-tributed action branches. Furthermore, we show that the pro-posed agent performs competitively against a state-of-the-art continuous control algorithm, Deep Deterministic PolicyGradient (DDPG).
  • Conference paper
    Kanajar P, Caldwell DG, Kormushev P, 2017,

    Climbing over large obstacles with a humanoid robot via multi-contact motion planning

    , IEEE RO-MAN 2017: 26th IEEE International Symposium on Robot and Human Interactive Communication, Publisher: IEEE, Pages: 1202-1209

    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.
  • Conference paper
    Tavakoli A, Pardo F, Kormushev P, 2017,

    Action Branching Architectures for Deep Reinforcement Learning

    , Deep Reinforcement Learning Symposium, 31st Conference on Neural Information Processing Systems (NIPS 2017)
  • Conference paper
    Rakicevic N, Kormushev P, 2017,

    Efficient Robot Task Learning and Transfer via Informed Search in Movement Parameter Space

    , Workshop on Acting and Interacting in the Real World: Challenges in Robot Learning, 31st Conference on Neural Information Processing Systems (NIPS 2017)
  • Journal article
    Palomeras N, Carrera A, Hurtós N, Karras GC, Bechlioulis CP, Cashmore M, Magazzeni D, Long D, Fox M, Kyriakopoulos KJ, Kormushev P, Salvi J, Carreras Met al., 2016,

    Toward persistent autonomous intervention in a subsea panel

    , Autonomous Robots, Vol: 40, Pages: 1279-1306
  • Journal article
    Jamisola RS, Kormushev P, Roberts RG, Caldwell DGet al., 2016,

    Task-Space Modular Dynamics for Dual-Arms Expressed through a Relative Jacobian

    , Journal of Intelligent & Robotic Systems, Pages: 1-14, ISSN: 1573-0409
  • Book chapter
    Kormushev P, Ahmadzadeh SR, 2016,

    Robot Learning for Persistent Autonomy

    , Handling Uncertainty and Networked Structure in Robot Control, Editors: Busoniu, Tamás, Publisher: Springer International Publishing, Pages: 3-28, ISBN: 978-3-319-26327-4
  • Book chapter
    Ahmadzadeh SR, Kormushev P, 2016,

    Visuospatial Skill Learning

    , Handling Uncertainty and Networked Structure in Robot Control, Editors: Busoniu, Tamás, Publisher: Springer International Publishing, Pages: 75-99, ISBN: 978-3-319-26327-4
  • Conference paper
    Maurelli F, Lane D, Kormushev P, Caldwell D, Carreras M, Salvi J, Fox M, Long D, Kyriakopoulos K, Karras Get al., 2016,

    The PANDORA project: a success story in AUV autonomy

    , OCEANS Conference 2016, Publisher: IEEE, ISSN: 0197-7385

    This paper presents some of the results of the EU-funded project PANDORA - Persistent Autonomy Through Learning Adaptation Observation and Re-planning. The project was three and a half years long and involved several organisations across Europe. The application domain is underwater inspection and intervention, a topic particularly interesting for the oil and gas sector, whose representatives constituted the Industrial Advisory Board. Field trials were performed at The Underwater Centre, in Loch Linnhe, Scotland, and in harbour conditions close to Girona, Spain.
  • Conference paper
    Kryczka P, Kormushev P, Tsagarakis N, Caldwell DGet al., 2015,

    Online Regeneration of Bipedal Walking Gait Optimizing Footstep Placement and Timing
  • Conference paper
    Kormushev P, Demiris Y, Caldwell DG, 2015,

    Kinematic-free Position Control of a 2-DOF Planar Robot Arm
  • Journal article
    Carrera A, Palomeras N, Hurtós N, Kormushev P, Carreras Met al., 2015,

    Cognitive System for Autonomous Underwater Intervention

    , Pattern Recognition Letters, ISSN: 0167-8655

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