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  • JOURNAL ARTICLE
    Kormushev P, Ugurlu B, Caldwell DG, Tsagarakis NGet al., 2018,

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

    , Autonomous Robots, Pages: 1-17, ISSN: 0929-5593

    © 2018 Springer Science+Business Media, LLC, part of Springer Nature 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
    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 for Casualty Extraction

    , Pages: 239-240

    © 2018 Authors. 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
    Saputra RP, Kormushev P, 2018,

    Casualty Detection from 3D Point Cloud Data for Autonomous Ground Mobile Rescue Robots

    © 2018 IEEE. One of the most important features of mobile rescue robots is the ability to autonomously detect casualties, i.e. human bodies, which are usually lying on the ground. This paper proposes a novel method for autonomously detecting casualties lying on the ground using obtained 3D point-cloud data from an on-board sensor, such as an RGB-D camera or a 3D LIDAR, on a mobile rescue robot. In this method, the obtained 3D point-cloud data is projected onto the detected ground plane, i.e. floor, within the point cloud. Then, this projected point cloud is converted into a grid-map that is used afterwards as an input for the algorithm to detect human body shapes. The proposed method is evaluated by performing detections of a human dummy, placed in different random positions and orientations, using an on-board RGB-D camera on a mobile rescue robot called ResQbot. To evaluate the robustness of the casualty detection method to different camera angles, the orientation of the camera is set to different angles. The experimental results show that using the point-cloud data from the on-board RGB-D camera, the proposed method successfully detects the casualty in all tested body positions and orientations relative to the on-board camera, as well as in all tested camera angles.

  • CONFERENCE PAPER
    Saputra RP, Kormushev P, 2018,

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

    , Pages: 473-475, ISSN: 0302-9743

    © Springer International Publishing AG, part of Springer Nature 2018. In order to operate autonomously, mobile rescue robots need to be able to detect human casualties in disaster situations. In this paper, we propose a novel method for autonomous detection of casualties lying down on the ground based on point-cloud data. This data can be obtained from different sensors, such as an RGB-D camera or a 3D LIDAR sensor. The method is based on a ground-projected point-cloud (GPPC) image to achieve human body shape detection. A preliminary experiment has been conducted using the RANSAC method for floor detection and, the HOG feature and the SVM classifier to detect human body shape. The results show that the proposed method succeeds to identify a casualty from point-cloud data in a wide range of viewing angles.

  • CONFERENCE PAPER
    Saputra RP, Kormushev P, 2018,

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

    , Pages: 209-220, ISSN: 0302-9743

    © Springer International Publishing AG, part of Springer Nature 2018. 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
    Tavakoli A, Pardo F, Kormushev P, 2018,

    Action Branching Architectures for Deep Reinforcement Learning

    Discrete-action algorithms have been central to numerous recent successes ofdeep reinforcement learning. However, applying these algorithms tohigh-dimensional action tasks requires tackling the combinatorial increase ofthe number of possible actions with the number of action dimensions. Thisproblem is further exacerbated for continuous-action tasks that require finecontrol of actions via discretization. In this paper, we propose a novel neuralarchitecture featuring a shared decision module followed by several networkbranches, one for each action dimension. This approach achieves a linearincrease of the number of network outputs with the number of degrees of freedomby allowing a level of independence for each individual action dimension. Toillustrate the approach, we present a novel agent, called Branching DuelingQ-Network (BDQ), as a branching variant of the Dueling Double Deep Q-Network(Dueling DDQN). We evaluate the performance of our agent on a set ofchallenging continuous control tasks. The empirical results show that theproposed agent scales gracefully to environments with increasing actiondimensionality and indicate the significance of the shared decision module incoordination of the distributed action branches. Furthermore, we show that theproposed agent performs competitively against a state-of-the-art continuouscontrol algorithm, Deep Deterministic Policy Gradient (DDPG).

  • CONFERENCE PAPER
    Wang K, Shah A, Kormushev P, 2018,

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

  • CONFERENCE PAPER
    Wang K, Shah A, Kormushev P, 2018,

    SLIDER: A novel bipedal walking robot without knees

    , Pages: 471-472, ISSN: 0302-9743

    © Springer International Publishing AG, part of Springer Nature 2018. This extended abstract describes our work on SLIDER: a novel bipedal robot with knee-less legs and hip sliding motion. Compared with conventional anthropomorphic leg design, SLIDER’s leg design enables the robot to have very lightweight legs and is suitable to perform agile locomotion. To validate this design, we created a dynamics model and implemented a walk pattern generator capable of walking with a speed of 0.18 m/s in Gazebo. Currently, a physical prototype is under construction for real-world testing. The initial mechanical design and the control strategy for SLIDER are introduced.

  • 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
    Pardo F, Tavakoli A, Levdik V, Kormushev Pet al., 2017,

    Time Limits in Reinforcement Learning

    , Deep Reinforcement Learning Symposium (DRLS), 31st Conference on Neural Information Processing Systems (NIPS 2017)

    In reinforcement learning, it is common to let an agent interact with its environment for a fixed amount of time before resetting the environment and repeating the process in a series of episodes. The task that the agent has to learn can either be to maximize its performance over (i) that fixed period, or (ii) an indefinite period where time limits are only used during training to diversify experience. In this paper, we investigate theoretically how time limits could effectively be handled in each of the two cases. In the first one, we argue that the terminations due to time limits are in fact part of the environment, and propose to include a notion of the remaining time as part of the agent’s input. In the second case, the time limits are not part of the environment and are only used to facilitate learning. We argue that such terminations should not be treated as environmental ones and propose a method, specific to value-based algorithms, that incorporates this insight by continuing to bootstrap at the end of each partial episode. To illustrate the significance of our proposals, we perform several experiments on a range of environments from simple few-state transition graphs to complex control tasks, including novel and standard benchmark domains. Our results show that the proposed methods improve the performance and stability of existing reinforcement learning algorithms.

  • CONFERENCE PAPER
    Rakicevic N, Kormushev P, 2017,

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

  • CONFERENCE PAPER
    Tavakoli A, Pardo F, Kormushev P, 2017,

    Action Branching Architectures for Deep Reinforcement Learning

  • JOURNAL ARTICLE
    Jamisola RS, Kormushev PS, Roberts RG, Caldwell DGet 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
  • 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, Publisher: IEEE, ISSN: 0197-7385
  • JOURNAL ARTICLE
    Palomeras N, Carrera A, Hurtos 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, ISSN: 0929-5593
  • BOOK CHAPTER
    Ahmadzadeh SR, Kormushev P, 2015,

    Visuospatial skill learning

    , Studies in Systems, Decision and Control, Pages: 75-99

    © Springer International Publishing Switzerland 2015. This chapter introduces Visuospatial Skill Learning (VSL), which is a novel interactive robot learning approach. VSL is based on visual perception that allows a robot to acquire new skills by observing a single demonstration while interacting with a tutor. The focus of VSL is placed on achieving a desired goal configuration of objects relative to another. VSL captures the object’s context for each demonstrated action. This context is the basis of the visuospatial representation and encodes implicitly the relative positioning of the object with respect to multiple other objects simultaneously. VSL is capable of learning and generalizing multi-operation skills from a single demonstration, while requiring minimum a priori knowledge about the environment. Different capabilities of VSL such as learning and generalization of object reconfiguration, classification, and turn-taking interaction are illustrated through both simulation and real-world experiments.

  • CONFERENCE PAPER
    Ahmadzadeh SR, Paikan A, Mastrogiovanni F, Natale L, Kormushev P, Caldwell DGet 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
  • JOURNAL ARTICLE
    Bimbo J, Kormushev P, Althoefer K, Liu Het al., 2015,

    Global estimation of an object's pose using tactile sensing

    , ADVANCED ROBOTICS, Vol: 29, Pages: 363-374, ISSN: 0169-1864
  • JOURNAL ARTICLE
    Carrera A, Palomeras N, Hurtos N, Kormushev P, Carreras Met al., 2015,

    Cognitive system for autonomous underwater intervention

    , PATTERN RECOGNITION LETTERS, Vol: 67, Pages: 91-99, ISSN: 0167-8655

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