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  • Conference paper
    Cursi F, Chappell D, Kormushev P, 2021,

    Augmenting Loss Functions of Feedforward Neural Networks with Differential Relationships for Robot Kinematic Modelling

    , Ljubljana, Slovenia
  • 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

    , Proceedings of the 2021 Genetic and Evolutionary Computation Conference

    Neuroevolution is an alternative to gradient-based optimisation that has thepotential to avoid local minima and allows parallelisation. The main limitingfactor is that usually it does not scale well with parameter spacedimensionality. Inspired by recent work examining neural network intrinsicdimension and loss landscapes, we hypothesise that there exists alow-dimensional manifold, embedded in the policy network parameter space,around which a high-density of diverse and useful policies are located. Thispaper proposes a novel method for diversity-based policy search viaNeuroevolution, that leverages learned representations of the policy networkparameters, by performing policy search in this learned representation space.Our method relies on the Quality-Diversity (QD) framework which provides aprincipled approach to policy search, and maintains a collection of diversepolicies, used as a dataset for learning policy representations. Further, weuse the Jacobian of the inverse-mapping function to guide the search in therepresentation space. This ensures that the generated samples remain in thehigh-density regions, after mapping back to the original space. Finally, weevaluate our contributions on four continuous-control tasks in simulatedenvironments, 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

  • Conference paper
    Frazelle C, Walker I, AlAttar A, Kormushev Pet al., 2021,

    Kinematic-model-free control for space operations with continuum Manipulators

    , USA, IEEE Conference on Aerospace, Publisher: IEEE, Pages: 1-11, ISSN: 1095-323X

    Continuum robots have strong potential for application in Space environments. However, their modeling is challenging in comparison with traditional rigid-link robots. The Kinematic-Model-Free (KMF) robot control method has been shown to be extremely effective in permitting a rigid-link robot to learn approximations of local kinematics and dynamics (“kinodynamics”) at various points in the robot's task space. These approximations enable the robot to follow various trajectories and even adapt to changes in the robot's kinematic structure. In this paper, we present the adaptation of the KMF method to a three-section, nine degrees-of-freedom continuum manipulator for both planar and spatial task spaces. Using only an external 3D camera, we show that the KMF method allows the continuum robot to converge to various desired set points in the robot's task space, avoiding the complexities inherent in solving this problem using traditional inverse kinematics. The success of the method shows that a continuum robot can “learn” enough information from an external camera to reach and track desired points and trajectories, without needing knowledge of exact shape or position of the robot. We similarly apply the method in a simulated example of a continuum robot performing an inspection task on board the ISS.

  • Journal article
    AlAttar A, Cursi F, Kormushev P, 2021,

    Kinematic-model-free redundancy resolution using multi-point tracking and control for robot manipulation

    , Applied Sciences, Vol: 11, Pages: 1-15, ISSN: 2076-3417

    Abstract: Robots have been predominantly controlled using conventional control methods that require prior knowledge of the robots’ kinematic and dynamic models. These controllers can be challenging to tune and cannot directly adapt to changes in kinematic structure or dynamic properties. On the other hand, model-learning controllers can overcome such challenges.Our recently proposed model-learning orientation controller has shown promising ability to simul6 taneously control a three-degrees-of-freedom robot manipulator’s end-effector pose. However, this controller does not perform optimally with robots of higher degrees-of-freedom nor does it resolve redundancies. The research presented in this paper extends the state-of-the-art kinematic9 model-free controller to perform pose control of hyper-redundant robot manipulators and resolve redundancies by tracking and controlling multiple points along the robot’s serial chain. The results show that with more control points, the controller is able to reach desired poses in fewer steps, yielding an improvement of up to 66%, and capable of achieving complex configurations. The algorithm was validated by running the simulation 100 times and it was found that 82% of the times the robot successfully reached the desired target pose within 150 steps.

  • Conference paper
    Tavakoli A, Fatemi M, Kormushev P, 2021,

    Learning to represent action values as a hypergraph on the action vertices

    , Vienna, Austria, International Conference on Learning Representations

    Action-value estimation is a critical component of many reinforcement learning(RL) methods whereby sample complexity relies heavily on how fast a good estimator for action value can be learned. By viewing this problem through the lens ofrepresentation learning, good representations of both state and action can facilitateaction-value estimation. While advances in deep learning have seamlessly drivenprogress in learning state representations, given the specificity of the notion ofagency to RL, little attention has been paid to learning action representations. Weconjecture that leveraging the combinatorial structure of multi-dimensional actionspaces is a key ingredient for learning good representations of action. To test this,we set forth the action hypergraph networks framework—a class of functions forlearning action representations in multi-dimensional discrete action spaces with astructural inductive bias. Using this framework we realise an agent class basedon a combination with deep Q-networks, which we dub hypergraph Q-networks.We show the effectiveness of our approach on a myriad of domains: illustrativeprediction problems under minimal confounding effects, Atari 2600 games, anddiscretised physical control benchmarks.

  • Journal article
    Russell F, Takeda Y, Kormushev P, Vaidyanathan R, Ellison Pet al., 2021,

    Stiffness modulation in a humanoid robotic leg and knee

    , IEEE Robotics and Automation Letters, Vol: 6, Pages: 2563-2570, ISSN: 2377-3766

    Stiffness modulation in walking is critical to maintain static/dynamic stability as well as minimize energy consumption and impact damage. However, optimal, or even functional, stiffness parameterization remains unresolved in legged robotics.We introduce an architecture for stiffness control utilizing a bioinspired robotic limb consisting of a condylar knee joint and leg with antagonistic actuation. The joint replicates elastic ligaments of the human knee providing tuneable compliance for walking. It further locks out at maximum extension, providing stability when standing. Compliance and friction losses between joint surfaces are derived as a function of ligament stiffness and length. Experimental studies validate utility through quantification of: 1) hip perturbation response; 2) payload capacity; and 3) static stiffness of the leg mechanism.Results prove initiation and compliance at lock out can be modulated independently of friction loss by changing ligament elasticity. Furthermore, increasing co-contraction or decreasing joint angle enables increased leg stiffness, which establishes co-contraction is counterbalanced by decreased payload.Findings have direct application in legged robots and transfemoral prosthetic knees, where biorobotic design could reduce energy expense while improving efficiency and stability. Future targeted impact involves increasing power/weight ratios in walking robots and artificial limbs for increased efficiency and precision in walking control.

  • Journal article
    Cursi F, Modugno V, Lanari L, Oriolo G, Kormushev Pet al., 2021,

    Bayesian neural network modeling and hierarchical MPC for a tendon-driven surgical robot with uncertainty minimization

    , IEEE Robotics and Automation Letters, Vol: 6, Pages: 2642-2649, ISSN: 2377-3766

    In order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertainties of the learned system. Model Predictive Control (MPC) is a reliable control strategy to ensure optimality and satisfaction of safety constraints. In this work we propose the use of BNN to build the highly nonlinear kinematic and dynamic models of a tendon-driven surgical robot, and exploit the information about the epistemic uncertainties by means of a Hierarchical MPC (Hi-MPC) control strategy. Simulation and real world experiments show that the method is capable of ensuring accurate tip positioning, while satisfying imposed safety bounds on the kinematics and dynamics of the robot.

  • Journal article
    Saputra RP, Rakicevic N, Chappell D, Wang K, Kormushev Pet al., 2021,

    Hierarchical decomposed-objective model predictive control for autonomous casualty extraction

    , IEEE Access, Vol: 9, Pages: 39656-39679, ISSN: 2169-3536

    In recent years, several robots have been developed and deployed to perform casualty extraction tasks. However, the majority of these robots are overly complex, and require teleoperation via either a skilled operator or a specialised device, and often the operator must be present at the scene to navigate safely around the casualty. Instead, improving the autonomy of such robots can reduce the reliance on expert operators and potentially unstable communication systems, while still extracting the casualty in a safe manner. There are several stages in the casualty extraction procedure, from navigating to the location of the emergency, safely approaching and loading the casualty, to finally navigating back to the medical assistance location. In this paper, we propose a Hierarchical Decomposed-Objective based Model Predictive Control (HiDO-MPC) method for safely approaching and manoeuvring around the casualty. We implement this controller on ResQbot — a proof-of-concept mobile rescue robot we previously developed — capable of safely rescuing an injured person lying on the ground, i.e. performing the casualty extraction procedure. HiDO-MPC achieves the desired casualty extraction behaviour by decomposing the main objective into multiple sub-objectives with a hierarchical structure. At every time step, the controller evaluates this hierarchical decomposed objective and generates the optimal control decision. We have conducted a number of experiments both in simulation and using the real robot to evaluate the proposed method’s performance, and compare it with baseline approaches. The results demonstrate that the proposed control strategy gives significantly better results than baseline approaches in terms of accuracy, robustness, and execution time, when applied to casualty extraction scenarios.

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