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  • Journal article
    Wang K, Fei H, Kormushev P, 2022,

    Fast online optimization for terrain-blind bipedal robot walking with a decoupled actuated SLIP model

    , Frontiers in Robotics and AI, Vol: 9, Pages: 1-11, ISSN: 2296-9144

    We present an online optimization algorithm which enables bipedal robots to blindly walk overvarious kinds of uneven terrains while resisting pushes. The proposed optimization algorithmperforms high level motion planning of footstep locations and center-of-mass height variationsusing the decoupled actuated Spring Loaded Inverted Pendulum (aSLIP) model. The decoupledaSLIP model simplifies the original aSLIP with Linear Inverted Pendulum (LIP) dynamics inhorizontal states and spring dynamics in the vertical state. The motion planning can beformulated as a discrete-time Model Predictive Control (MPC) problem and solved at a frequencyof 1 kHz. The output of the motion planner is fed into an inverse-dynamics based whole bodycontroller for execution on the robot. A key result of this controller is that the feet of the robot arecompliant, which further extends the robot’s ability to be robust to unobserved terrain variations.We evaluate our method in simulation with the bipedal robot SLIDER. Results show the robotcan blindly walk over various uneven terrains including slopes, wave fields and stairs. It can alsoresist pushes of up to 40 N for a duration of 0.1 s while walking on uneven terrain.

  • Journal article
    AlAttar A, Chappell D, Kormushev P, 2022,

    Kinematic-model-free predictive control for robotic manipulator target reaching with obstacle avoidance

    , Frontiers in Robotics and AI, Vol: 9, Pages: 1-9, ISSN: 2296-9144

    Model predictive control is a widely used optimal control method for robot path planning andobstacle avoidance. This control method, however, requires a system model to optimize controlover a finite time horizon and possible trajectories. Certain types of robots, such as softrobots, continuum robots, and transforming robots, can be challenging to model, especiallyin unstructured or unknown environments. Kinematic-model-free control can overcome thesechallenges by learning local linear models online. This paper presents a novel perception-basedrobot motion controller, the kinematic-model-free predictive controller, that is capable of controllingrobot manipulators without any prior knowledge of the robot’s kinematic structure and dynamicparameters and is able to perform end-effector obstacle avoidance. Simulations and physicalexperiments were conducted to demonstrate the ability and adaptability of the controller toperform simultaneous target reaching and obstacle avoidance.

  • Journal article
    Cursi F, Bai W, Yeatman EM, Kormushev Pet al., 2022,

    GlobDesOpt: a global optimization framework for optimal robot manipulator design

    , IEEE Access, Vol: 10, Pages: 5012-5023, ISSN: 2169-3536

    Robot design is a major component in robotics, as it allows building robots capable of performing properly in given tasks. However, designing a robot with multiple types of parameters and constraints and defining an optimization function analytically for the robot design problem may be intractable or even impossible. Therefore black-box optimization approaches are generally preferred. In this work we propose GlobDesOpt, a simple-to-use open-source optimization framework for robot design based on global optimization methods. The framework allows selecting various design parameters and optimizing for both single and dual-arm robots. The functionalities of the framework are shown here to optimally design a dual-arm surgical robot, comparing the different two optimization strategies.

  • Conference paper
    Cursi F, Chappell D, Kormushev P, 2022,

    Augmenting loss functions of feedforward neural networks with differential relationships for robot kinematic modelling

    , Ljubljana, Slovenia, 20th International Conference on Advanced Robotics (ICAR), Publisher: IEEE, Pages: 201-207

    Model learning is a crucial aspect of robotics as it enables the use of traditional and consolidated model-based controllers to perform desired motion tasks. However, due to the increasing complexity of robotic structures, modelling robots is becoming more and more challenging, and analytical models are very difficult to build, particularly for redundant robots. Machine learning approaches have shown great capabilities in learning complex mapping and have widely been used in robot model learning and control. Generally, inverse kinematics is learned, directly obtaining the desired control commands given a desired task. However, learning forward kinematics is simpler and allows the computation of the robot Jacobian and enables the exploitation of the optimality of controllers. Nevertheless, typical learning methods have no knowledge about the differential relationship between the position and velocity mappings. In this work, we present two novel loss functions to train feedforward Artificial Neural network (ANN) which incorporate this information in learning the forward kinematic model of robotic structures, and carry out a comparison with standard ANN training using position data only. Simulation results show that incorporating the knowledge of the velocity mapping improves the suitability of the learnt model for control tasks.

  • Conference paper
    La Barbera V, Pardo F, Tassa Y, Daley M, Richards C, Kormushev P, Hutchinson Jet al., 2021,

    OstrichRL: a musculoskeletal ostrich simulation to study bio-mechanical locomotion

    , NeurIPS 2021

    Muscle-actuated control is a research topic of interest spanning different fields, inparticular biomechanics, robotics and graphics. This type of control is particularlychallenging because models are often overactuated, and dynamics are delayed andnon-linear. It is however a very well tested and tuned actuation model that hasundergone millions of years of evolution and that involves interesting propertiesexploiting passive forces of muscle-tendon units and efficient energy storage andrelease. To facilitate research on muscle-actuated simulation, we release a 3Dmusculoskeletal simulation of an ostrich based on the MuJoCo simulator. Ostrichesare one of the fastest bipeds on earth and are therefore an excellent model forstudying muscle-actuated bipedal locomotion. The model is based on CT scans anddissections used to gather actual muscle data such as insertion sites, lengths andpennation angles. Along with this model, we also provide a set of reinforcementlearning tasks, including reference motion tracking and a reaching task with theneck. The reference motion data are based on motion capture clips of variousbehaviors which we pre-processed and adapted to our model. This paper describeshow the model was built and iteratively improved using the tasks. We evaluate theaccuracy of the muscle actuation patterns by comparing them to experimentallycollected electromyographic data from locomoting birds. We believe that this workcan be a useful bridge between the biomechanics, reinforcement learning, graphicsand robotics communities, by providing a fast and easy to use simulation.

  • Conference paper
    Cursi F, Kormushev P, 2021,

    Pre-operative offline optimization of insertion point location for safe and accurate surgical task execution

    , Prague, Czech Republic, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)

    In robotically assisted surgical procedures thesurgical tool is usually inserted in the patient’s body througha small incision, which acts as a constraint for the motionof the robot, known as remote center of Motion (RCM). Thelocation of the insertion point on the patient’s body has hugeeffects on the performances of the surgical robot. In this workwe present an offline pre-operative framework to identify theoptimal insertion point location in order to guarantee accurateand safe surgical task execution. The approach is validatedusing a serial-link manipulator in conjunction with a surgicalrobotic tool to perform a tumor resection task, while avoidingnearby organs. Results show that the framework is capable ofidentifying the best insertion point ensuring high dexterity, hightracking accuracy, and safety in avoiding nearby organs.

  • Conference paper
    Cursi F, Bai W, Kormushev P, 2021,

    Kalibrot: a simple-to-use Matlab package for robot kinematic calibration

    , Prague, Czech Republic, International Conference on Intelligent Robots and Systems (IROS) 2021

    Robot modelling is an essential part to properlyunderstand how a robotic system moves and how to controlit. The kinematic model of a robot is usually obtained byusing Denavit-Hartenberg convention, which relies on a set ofparameters to describe the end-effector pose in a Cartesianspace. These parameters are assigned based on geometricalconsiderations of the robotic structure, however, the assignedvalues may be inaccurate. The purpose of robot kinematiccalibration is therefore to find optimal parameters whichimprove the accuracy of the robot model. In this work wepresent Kalibrot, an open source Matlab package for robotkinematic calibration. Kalibrot has been designed to simplifyrobot calibration and easily assess the calibration results. Besidecomputing the optimal parameters, Kalibrot provides a visualization layer showing the values of the calibrated parameters,what parameters can be identified, and the calibrated roboticstructure. The capabilities of the package are here shownthrough simulated and real world experiments.

  • Conference paper
    Wang K, Saputra RP, Foster JP, Kormushev Pet al., 2021,

    Improved energy efficiency via parallel elastic elements for the straight-legged vertically-compliant robot SLIDER

    , Japan, 24th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines

    Most state-of-the-art bipedal robots are designed to be anthropomorphic, and therefore possess articulated legs with knees. Whilstthis facilitates smoother, human-like locomotion, there are implementation issues that make walking with straight legs difficult. Many robotshave to move with a constant bend in the legs to avoid a singularityoccurring at the knee joints. The actuators must constantly work tomaintain this stance, which can result in the negation of energy-savingtechniques employed. Furthermore, vertical compliance disappears whenthe leg is straight and the robot undergoes high-energy loss events such asimpacts from running and jumping, as the impact force travels throughthe fully extended joints to the hips. In this paper, we attempt to improve energy efficiency in a simple yet effective way: attaching bungeecords as elastic elements in parallel to the legs of a novel, knee-less bipedrobot SLIDER, and show that the robot’s prismatic hip joints preservevertical compliance despite the legs being constantly straight. Due tothe nonlinear dynamics of the bungee cords and various sources of friction, Bayesian Optimization is utilized to find the optimals configurationof bungee cords that achieves the largest reduction in energy consumption. The optimal solution found saves 15% of the energy consumptioncompared to the robot configuration without parallel elastic elements.Additional Video: https://youtu.be/ZTaG9−Dz8A

  • Conference paper
    Rakicevic N, Cully A, Kormushev P, 2021,

    Policy manifold search: exploring the manifold hypothesis for diversity-based neuroevolution

    , Genetic and Evolutionary Computation Conference (GECCO '21), Pages: 901-909

    Neuroevolution is an alternative to gradient-based optimisation that has the potential to avoid local minima and allows parallelisation. The main limiting factor is that usually it does not scale well with parameter space dimensionality. Inspired by recent work examining neural network intrinsic dimension and loss landscapes, we hypothesise that there exists a low-dimensional manifold, embedded in the policy network parameter space, around which a high-density of diverse and useful policies are located. This paper proposes a novel method for diversity-based policy search via Neuroevolution, that leverages learned representations of the policy network parameters, by performing policy search in this learned representation space. Our method relies on the Quality-Diversity (QD) framework which provides a principled approach to policy search, and maintains a collection of diverse policies, used as a dataset for learning policy representations. Further, we use the Jacobian of the inverse-mapping function to guide the search in the representation space. This ensures that the generated samples remain in the high-density regions, after mapping back to the original space. Finally, we evaluate our contributions on four continuous-control tasks in simulated environments, and compare to diversity-based baselines.

  • Journal article
    Saputra RP, Rakicevic N, Kuder I, Bilsdorfer J, Gough A, Dakin A, Cocker ED, Rock S, Harpin R, Kormushev Pet al., 2021,

    ResQbot 2.0: an improved design of a mobile rescue robot with an inflatable neck securing device for safe casualty extraction

    , Applied Sciences, Vol: 11, Pages: 1-18, ISSN: 2076-3417

    Despite the fact that a large number of research studies have been conducted in the field of searchand rescue robotics, significantly little attention has been given to the development of rescue robotscapable of performing physical rescue interventions, including loading and transporting victims toa safe zone—i.e. casualty extraction tasks. The aim of this study is to develop a mobile rescue robotthat could assist first responders when saving casualties from a danger area by performing a casualty extraction procedure, whilst ensuring that no additional injury is caused by the operation andno additional lives are put at risk. In this paper, we present a novel design of ResQbot 2.0—a mobilerescue robot designed for performing the casualty extraction task. This robot is a stretcher-type casualty extraction robot, which is a significantly improved version of the initial proof-of-concept prototype, ResQbot (retrospectively referred to as ResQbot 1.0), that has been developed in our previous work. The proposed designs and development of the mechanical system of ResQbot 2.0, as wellas the method for safely loading a full body casualty onto the robot’s ‘stretcher bed’, are describedin detail based on the conducted literature review, evaluation of our previous work and feedbackprovided by medical professionals. To verify the proposed design and the casualty extraction procedure, we perform simulation experiments in Gazebo physics engine simulator. The simulationresults demonstrate the capability of ResQbot 2.0 to successfully carry out safe casualty extractions

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