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  • Conference paper
    Cully A, 2021,

    Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emitters

    , Genetic and Evolutionary Computation Conference (GECCO)

    Quality-Diversity (QD) optimisation is a new family of learning algorithmsthat aims at generating collections of diverse and high-performing solutions.Among those algorithms, MAP-Elites is a simple yet powerful approach that hasshown promising results in numerous applications. In this paper, we introduce anovel algorithm named Multi-Emitter MAP-Elites (ME-MAP-Elites) that improvesthe quality, diversity and convergence speed of MAP-Elites. It is based on therecently introduced concept of emitters, which are used to drive thealgorithm's exploration according to predefined heuristics. ME-MAP-Elitesleverages the diversity of a heterogeneous set of emitters, in which eachemitter type is designed to improve differently the optimisation process.Moreover, a bandit algorithm is used to dynamically find the best emitter setdepending on the current situation. We evaluate the performance ofME-MAP-Elites on six tasks, ranging from standard optimisation problems (in 100dimensions) to complex locomotion tasks in robotics. Our comparisons againstMAP-Elites and existing approaches using emitters show that ME-MAP-Elites isfaster at providing collections of solutions that are significantly morediverse and higher performing. Moreover, in the rare cases where no fruitfulsynergy can be found between the different emitters, ME-MAP-Elites isequivalent to the best of the compared algorithms.

  • Conference paper
    Shen M, Clark A, Rojas N, 2020,

    A scalable variable stiffness revolute joint based on layer jamming for robotic exoskeletons

    , Towards Autonomous Robotic Systems Conference ( TAROS ) 2020, Publisher: Springer Verlag, ISSN: 0302-9743

    Robotic exoskeletons have been a focal point of research dueto an ever-increasing ageing population, longer life expectancy, and adesire to further improve the existing capabilities of humans. However,their effectiveness is often limited, with strong rigid structures poorlyinterfacing with humans and soft flexible mechanisms providing limitedforces. In this paper, a scalable variable stiffness revolute jointis pro-posed to overcome this problem. By using layer jamming, the jointhasthe ability to stiffen or soften for different use cases. A theoretical and ex-perimental study of maximum stiffness with size was conductedto deter-mine the suitability and scalablity of this technology. Three sizes (25 mm,18.75 mm, 12.5 mm diameter) of the joint were developed and evaluated.Results indicate that this technology is most suitable for use in humanfingers, as the prototypes demonstrate a sufficient torque (0.054 Nm) tosupport finger movement.

  • Conference paper
    Wang J, Lu Q, Clark A, Rojas Net al., 2020,

    A passively complaint idler mechanism for underactuated dexterous grippers with dynamic tendon routing

    , Towards Autonomous Robotic Systems Conference (TAROS ) 2020, Publisher: Springer Verlag, ISSN: 0302-9743

    n the field of robotic hands, tendon actuation is one of themost common ways to control self-adaptive underactuated fingers thanksto its compact size. Either differential or direct drive mechanisms areusually used in these systems to perform synchronised grasping using asingle actuator. However, synchronisation problems arise in underactu-ated grippers whose position of proximal joints varies with time to per-form manipulation operations, as this results in a tendon-driven systemwith dynamic anchor pulleys. This paper introduces a novel passivelycomplaint idler mechanism to avoid unsynchronisation in grippers witha dynamic multi-tendon routing system, such that adequate graspingcontact forces are kept under changes in the proximal joints’ positions.A re-configurable palm underactuated dexterous gripper is used as acase study, with the performance of the proposed compliant idler systembeing evaluated and compared through a contact force analysis duringrotation and translation in-hand manipulation tasks. Experiment resultsclearly demonstrate the ability of the mechanism to synchronise a dy-namic tendon routing gripper. A video summarising experiments andfindings can be found athttps://imperialcollegelondon.box.com/s/hk58688q2hjnu8dhw7uskr7vi9tqr9r5.

  • Journal article
    Saracino A, Oude-Vrielink TJC, Menciassi A, Sinibaldi E, Mylonas GPet al., 2020,

    Haptic Intracorporeal Palpation Using a Cable-Driven Parallel Robot: A User Study

    , IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 67, Pages: 3452-3463, ISSN: 0018-9294
  • Journal article
    Fischer T, Demiris Y, 2020,

    Computational modelling of embodied visual perspective-taking

    , IEEE Transactions on Cognitive and Developmental Systems, Vol: 12, Pages: 723-732, ISSN: 2379-8920

    Humans are inherently social beings that benefit from their perceptional capability to embody another point of view, typically referred to as perspective-taking. Perspective-taking is an essential feature in our daily interactions and is pivotal for human development. However, much remains unknown about the precise mechanisms that underlie perspective-taking. Here we show that formalizing perspective-taking in a computational model can detail the embodied mechanisms employed by humans in perspective-taking. The model's main building block is a set of action primitives that are passed through a forward model. The model employs a process that selects a subset of action primitives to be passed through the forward model to reduce the response time. The model demonstrates results that mimic those captured by human data, including (i) response times differences caused by the angular disparity between the perspective-taker and the other agent, (ii) the impact of task-irrelevant body posture variations in perspective-taking, and (iii) differences in the perspective-taking strategy between individuals. Our results provide support for the hypothesis that perspective-taking is a mental simulation of the physical movements that are required to match another person's visual viewpoint. Furthermore, the model provides several testable predictions, including the prediction that forced early responses lead to an egocentric bias and that a selection process introduces dependencies between two consecutive trials. Our results indicate potential links between perspective-taking and other essential perceptional and cognitive mechanisms, such as active vision and autobiographical memories.

  • Conference paper
    Liu S, Lin Z, Wang Y, Jianming Z, Perazzi F, Johns Eet al., 2020,

    Shape adaptor: a learnable resizing module

    , European Conference on Computer Vision 2020, Publisher: Springer Verlag, Pages: 661-677, ISSN: 0302-9743

    We present a novel resizing module for neural networks: shape adaptor, a drop-in enhancement built on top of traditional resizing layers, such as pooling, bilinear sampling, and strided convolution. Whilst traditional resizing layers have fixed and deterministic reshaping factors, our module allows for a learnable reshaping factor. Our implementation enables shape adaptors to be trained end-to-end without any additional supervision, through which network architectures can be optimised for each individual task, in a fully automated way. We performed experiments across seven image classification datasets, and results show that by simply using a set of our shape adaptors instead of the original resizing layers, performance increases consistently over human-designed networks, across all datasets. Additionally, we show the effectiveness of shape adaptors on two other applications: network compression and transfer learning.

  • Journal article
    Russell F, Kormushev P, Vaidyanathan R, Ellison Pet al., 2020,

    The impact of ACL laxity on a bicondylar robotic knee and implications in human joint biomechanics

    , IEEE Transactions on Biomedical Engineering, Vol: 67, Pages: 2817-2827, ISSN: 0018-9294

    Objective: Elucidating the role of structural mechanisms in the knee can improve joint surgeries, rehabilitation, and understanding of biped locomotion. Identification of key features, however, is challenging due to limitations in simulation and in-vivo studies. In particular the coupling of the patello-femoral and tibio-femoral joints with ligaments and its impact on joint mechanics and movement is not understood. We investigate this coupling experimentally through the design and testing of a robotic sagittal plane model. Methods: We constructed a sagittal plane robot comprised of: 1) elastic links representing cruciate ligaments; 2) a bi-condylar joint; 3) a patella; and 4) actuator hamstrings and quadriceps. Stiffness and geometry were derived from anthropometric data. 10° - 110° squatting tests were executed at speeds of 0.1 - 0.25Hz over a range of anterior cruciate ligament (ACL) slack lengths. Results: Increasing ACL length compromised joint stability, yet did not impact quadriceps mechanical advantage and force required for squat. The trend was consistent through varying condyle contact point and ligament force changes. Conclusion: The geometry of the condyles allows the ratio of quadriceps to patella tendon force to compensate for contact point changes imparted by the removal of the ACL. Thus the system maintains a constant mechanical advantage. Significance: The investigation uncovers critical features of human knee biomechanics. Findings contribute to understanding of knee ligament damage, inform procedures for knee surgery and orthopaedic implant design, and support design of trans-femoral prosthetics and walking robots. Results further demonstrate the utility of robotics as a powerful means of studying human joint biomechanics.

  • Conference paper
    Wang K, Marsh DM, Saputra RP, Chappell D, Jiang Z, Raut A, Kon B, Kormushev Pet al., 2020,

    Design and Control of SLIDER: An Ultra-lightweight, Knee-less, Low-cost Bipedal Walking Robot

    , Las Vegas, USA, International Conference on Intelligence Robots and Systems (IROS), Pages: 3488-3495

    Most state-of-the-art bipedal robots are designedto be highly anthropomorphic and therefore possess legs withknees. Whilst this facilitates more human-like locomotion, thereare implementation issues that make walking with straight ornear-straight legs difficult. Most bipedal robots have to movewith a constant bend in the legs to avoid singularities at theknee joints, and to keep the centre of mass at a constant heightfor control purposes. Furthermore, having a knee on the legincreases the design complexity as well as the weight of the leg,hindering the robot’s performance in agile behaviours such asrunning and jumping.We present SLIDER, an ultra-lightweight, low-cost bipedalwalking robot with a novel knee-less leg design. This nonanthropomorphic straight-legged design reduces the weight ofthe legs significantly whilst keeping the same functionality asanthropomorphic legs. Simulation results show that SLIDER’slow-inertia legs contribute to less vertical motion in the centerof mass (CoM) than anthropomorphic robots during walking,indicating that SLIDER’s model is closer to the widely usedInverted Pendulum (IP) model. Finally, stable walking onflat terrain is demonstrated both in simulation and in thephysical world, and feedback control is implemented to addresschallenges with the physical robot.

  • Journal article
    AlAttar A, Kormushev P, 2020,

    Kinematic-model-free orientation control for robot manipulation using locally weighted dual quaternions

    , Robotics, Vol: 9, Pages: 1-12, ISSN: 2218-6581

    Conventional control of robotic manipulators requires prior knowledge of their kinematic structure. Model-learning controllers have the advantage of being able to control robots without requiring a complete kinematic model and work well in less structured environments. Our recently proposed Encoderless controller has shown promising ability to control a manipulator without requiring any prior kinematic model whatsoever. However, this controller is only limited to position control, leaving orientation control unsolved. The research presented in this paper extends the state-of-the-art kinematic-model-free controller to handle orientation control to manipulate a robotic arm without requiring any prior model of the robot or any joint angle information during control. This paper presents a novel method to simultaneously control the position and orientation of a robot’s end effector using locally weighted dual quaternions. The proposed novel controller is also scaled up to control three-degrees-of-freedom robots.

  • Journal article
    Cursi F, Mylonas GP, Kormushev P, 2020,

    Adaptive kinematic modelling for multiobjective control of a redundant surgical robotic tool

    , Robotics, Vol: 9, Pages: 68-68, ISSN: 2218-6581

    Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which are common for minimally invasive surgery, the high nonlinearities in the transmission make modelling complex. Machine learning techniques are a preferred approach to tackle this problem. However, surgical environments are rarely structured, due to organs being very soft and deformable, and unpredictable, for instance, because of fluids in the system, wear and break of the tendons that lead to changes of the system’s behaviour. Therefore, the model needs to quickly adapt. In this work, we propose a method to learn the kinematic model of a redundant surgical robot and control it to perform surgical tasks both autonomously and in teleoperation. The approach employs Feedforward Artificial Neural Networks (ANN) for building the kinematic model of the robot offline, and an online adaptive strategy in order to allow the system to conform to the changing environment. To prove the capabilities of the method, a comparison with a simple feedback controller for autonomous tracking is carried out. Simulation results show that the proposed method is capable of achieving very small tracking errors, even when unpredicted changes in the system occur, such as broken joints. The method proved effective also in guaranteeing accurate tracking in teleoperation.

  • Journal article
    Escribano Macias J, Goldbeck N, Hsu P-Y, Angeloudis P, Ochieng Wet al., 2020,

    Endogenous stochastic optimisation for relief distribution assisted with unmanned aerial vehicles

    , OR SPECTRUM, Vol: 42, Pages: 1089-1125, ISSN: 0171-6468
  • Journal article
    Falck F, Doshi S, Tormento M, Nersisyan G, Smuts N, Lingi J, Rants K, Saputra RP, Wang K, Kormushev Pet al., 2020,

    Robot DE NIRO: a human-centered, autonomous, mobile research platform for cognitively-enhanced manipulation

    , Frontiers in Robotics and AI, Vol: A17, ISSN: 2296-9144

    We introduceRobot DE NIRO, an autonomous, collaborative, humanoid robot for mobilemanipulation. We built DE NIRO to perform a wide variety of manipulation behaviors, with afocus on pick-and-place tasks. DE NIRO is designed to be used in a domestic environment,especially in support of caregivers working with the elderly. Given this design focus, DE NIRO caninteract naturally, reliably, and safely with humans, autonomously navigate through environmentson command, intelligently retrieve or move target objects, and avoid collisions efficiently. Wedescribe DE NIRO’s hardware and software, including an extensive vision sensor suite of 2Dand 3D LIDARs, a depth camera, and a 360-degree camera rig; two types of custom grippers;and a custom-built exoskeleton called DE VITO. We demonstrate DE NIRO’s manipulationcapabilities in three illustrative challenges: First, we have DE NIRO perform a fetch-an-objectchallenge. Next, we add more cognition to DE NIRO’s object recognition and grasping abilities,confronting it with small objects of unknown shape. Finally, we extend DE NIRO’s capabilitiesinto dual-arm manipulation of larger objects. We put particular emphasis on the features thatenable DE NIRO to interact safely and naturally with humans. Our contribution is in sharinghow a humanoid robot with complex capabilities can be designed and built quickly with off-the-shelf hardware and open-source software. Supplementary material including our code, adocumentation, videos and the CAD models of several hardware parts are openly availableavailable athttps://www.imperial.ac.uk/robot-intelligence/software/

  • Conference paper
    Flageat M, Cully A, 2020,

    Fast and stable MAP-Elites in noisy domains using deep grids

    , 2020 Conference on Artificial Life, Publisher: Massachusetts Institute of Technology, Pages: 273-282

    Quality-Diversity optimisation algorithms enable the evolutionof collections of both high-performing and diverse solutions.These collections offer the possibility to quickly adapt andswitch from one solution to another in case it is not workingas expected. It therefore finds many applications in real-worlddomain problems such as robotic control. However, QD algo-rithms, like most optimisation algorithms, are very sensitive touncertainty on the fitness function, but also on the behaviouraldescriptors. Yet, such uncertainties are frequent in real-worldapplications. Few works have explored this issue in the spe-cific case of QD algorithms, and inspired by the literature inEvolutionary Computation, mainly focus on using samplingto approximate the ”true” value of the performances of a solu-tion. However, sampling approaches require a high number ofevaluations, which in many applications such as robotics, canquickly become impractical.In this work, we propose Deep-Grid MAP-Elites, a variantof the MAP-Elites algorithm that uses an archive of similarpreviously encountered solutions to approximate the perfor-mance of a solution. We compare our approach to previouslyexplored ones on three noisy tasks: a standard optimisationtask, the control of a redundant arm and a simulated Hexapodrobot. The experimental results show that this simple approachis significantly more resilient to noise on the behavioural de-scriptors, while achieving competitive performances in termsof fitness optimisation, and being more sample-efficient thanother existing approaches.

  • Conference paper
    Lu Q, Baron N, Clark A, Rojas Net al., 2020,

    The RUTH Gripper: systematic object-invariant prehensile in-hand manipulation via reconfigurable underactuation

    , Robotics: Science and Systems, Publisher: RSS

    We introduce a reconfigurable underactuated robothand able to perform systematic prehensile in-hand manipu-lations regardless of object size or shape. The hand utilisesa two-degree-of-freedom five-bar linkage as the palm of thegripper, with three three-phalanx underactuated fingers—jointlycontrolled by a single actuator—connected to the mobile revolutejoints of the palm. Three actuators are used in the robot handsystem, one for controlling the force exerted on objects by thefingers and two for changing the configuration of the palm.This novel layout allows decoupling grasping and manipulation,facilitating the planning and execution of in-hand manipulationoperations. The reconfigurable palm provides the hand withlarge grasping versatility, and allows easy computation of amap between task space and joint space for manipulation basedon distance-based linkage kinematics. The motion of objects ofdifferent sizes and shapes from one pose to another is thenstraightforward and systematic, provided the objects are keptgrasped. This is guaranteed independently and passively by theunderactuated fingers using a custom tendon routing method,which allows no tendon length variation when the relative fingerbase position changes with palm reconfigurations. We analysethe theoretical grasping workspace and manipulation capabilityof the hand, present algorithms for computing the manipulationmap and in-hand manipulation planning, and evaluate all theseexperimentally. Numerical and empirical results of several ma-nipulation trajectories with objects of different size and shapeclearly demonstrate the viability of the proposed concept.

  • Conference paper
    Goncalves Nunes UM, Demiris Y, 2020,

    Entropy minimisation framework for event-based vision model estimation

    , 16th European Conference on Computer Vision 2020, Publisher: Springer

    We propose a novel Entropy Minimisation (EMin) frame-work for event-based vision model estimation. The framework extendsprevious event-based motion compensation algorithms to handle modelswhose outputs have arbitrary dimensions. The main motivation comesfrom estimating motion from events directly in 3D space (e.g.eventsaugmented with depth), without projecting them onto an image plane.This is achieved by modelling the event alignment according to candidateparameters and minimising the resultant dispersion. We provide a familyof suitable entropy loss functions and an efficient approximation whosecomplexity is only linear with the number of events (e.g.the complexitydoes not depend on the number of image pixels). The framework is eval-uated on several motion estimation problems, including optical flow androtational motion. As proof of concept, we also test our framework on6-DOF estimation by performing the optimisation directly in 3D space.

  • Journal article
    Kinross JM, Mason SE, Mylonas G, Darzi Aet al., 2020,

    Next-generation robotics in gastrointestinal surgery

    , Nature Reviews Gastroenterology and Hepatology, Vol: 17, Pages: 430-440, ISSN: 1759-5045

    The global numbers of robotic gastrointestinal surgeries are increasing. However, the evidence base for robotic gastrointestinal surgery does not yet support its widespread adoption or justify its cost. The reasons for its continued popularity are complex, but a notable driver is the push for innovation — robotic surgery is seen as a compelling solution for delivering on the promise of minimally invasive precision surgery — and a changing commercial landscape delivers the promise of increased affordability. Novel systems will leverage the robot as a data-driven platform, integrating advances in imaging, artificial intelligence and machine learning for decision support. However, if this vision is to be realized, lessons must be heeded from current clinical trials and translational strategies, which have failed to demonstrate patient benefit. In this Perspective, we critically appraise current research to define the principles on which the next generation of gastrointestinal robotics trials should be based. We also discuss the emerging commercial landscape and define existing and new technologies.

  • Conference paper
    Johns E, Garcia-Hernando G, Kim T-K, 2020,

    Physics-Based Dexterous Manipulations with Estimated Hand Poses and Residual Reinforcement Learning

    , 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems
  • Conference paper
    Valassakis P, Ding Z, Johns E, 2020,

    Crossing the gap: a deep dive into zero-shot sim-to-real transfer for dynamics

    , 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEE

    Zero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and unsolved problem. A number of solutions have been proposed in recent years, but we have found that many works do not present a thorough evaluation in the real world, or underplay the significant engineering effort and task-specific fine tuning that is required to achieve the published results. In this paper, we dive deeper into the sim-to-real transfer challenge, investigate why this issuch a difficult problem, and present objective evaluations of anumber of transfer methods across a range of real-world tasks.Surprisingly, we found that a method which simply injects random forces into the simulation performs just as well as more complex methods, such as those which randomise the simulator's dynamics parameters

  • Conference paper
    Murai R, Saeedi S, Kelly P, 2020,

    BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal Plane

    , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
  • Conference paper
    Carvalho EDC, Clark R, Nicastro A, Kelly PHJet al., 2020,

    Scalable uncertainty for computer vision with functional variationalinference

    , CVPR 2020, Publisher: IEEE, Pages: 12003-12013

    As Deep Learning continues to yield successful applications in ComputerVision, the ability to quantify all forms of uncertainty is a paramountrequirement for its safe and reliable deployment in the real-world. In thiswork, we leverage the formulation of variational inference in function space,where we associate Gaussian Processes (GPs) to both Bayesian CNN priors andvariational family. Since GPs are fully determined by their mean and covariancefunctions, we are able to obtain predictive uncertainty estimates at the costof a single forward pass through any chosen CNN architecture and for anysupervised learning task. By leveraging the structure of the induced covariancematrices, we propose numerically efficient algorithms which enable fasttraining in the context of high-dimensional tasks such as depth estimation andsemantic segmentation. Additionally, we provide sufficient conditions forconstructing regression loss functions whose probabilistic counterparts arecompatible with aleatoric uncertainty quantification.

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