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Journal articleAlAttar A, Kormushev P, 2020,
Kinematic-model-free orientation control for robot manipulation using locally weighted dual quaternions
, Robotics, Vol: 9, Pages: 1-12, ISSN: 2218-6581Conventional 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.
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Conference paperDing Z, Lepora N, Johns E, 2020,
Sim-to-real transfer for optical tactile sensing
, IEEE International Conference on Robotics and Automation, Publisher: IEEE, Pages: 1639-1645, ISSN: 2152-4092Deep learning and reinforcement learning meth-ods have been shown to enable learning of flexible and complexrobot controllers. However, the reliance on large amounts oftraining data often requires data collection to be carried outin simulation, with a number of sim-to-real transfer methodsbeing developed in recent years. In this paper, we study thesetechniques for tactile sensing using the TacTip optical tactilesensor, which consists of a deformable tip with a cameraobserving the positions of pins inside this tip. We designeda model for soft body simulation which was implemented usingthe Unity physics engine, and trained a neural network topredict the locations and angles of edges when in contact withthe sensor. Using domain randomisation techniques for sim-to-real transfer, we show how this framework can be used toaccurately predict edges with less than 1 mm prediction errorin real-world testing, without any real-world data at all.
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Journal articleAlwan NA, Attree E, Blair JM, et al., 2020,
From doctors as patients: a manifesto for tackling persisting symptoms of covid-19
, BMJ-BRITISH MEDICAL JOURNAL, Vol: 370, ISSN: 0959-535X- Author Web Link
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- Citations: 37
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Conference paperLertvittayakumjorn P, Specia L, Toni F, 2020,
FIND: Human-in-the-loop debugging deep text classifiers
, 2020 Conference on Empirical Methods in Natural Language Processing, Publisher: ACLSince obtaining a perfect training dataset (i.e., a dataset which is considerably large, unbiased, and well-representative of unseen cases)is hardly possible, many real-world text classifiers are trained on the available, yet imperfect, datasets. These classifiers are thus likely to have undesirable properties. For instance, they may have biases against some sub-populations or may not work effectively in the wild due to overfitting. In this paper, we propose FIND–a framework which enables humans to debug deep learning text classifiers by disabling irrelevant hidden features. Experiments show that by using FIND, humans can improve CNN text classifiers which were trained under different types of imperfect datasets (including datasets with biases and datasets with dissimilar train-test distributions).
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Conference paperAlbini E, Baroni P, Rago A, et al., 2020,
PageRank as an Argumentation Semantics
, Biennial International Conference on Computational Models of Argument (COMMA), Publisher: IOS PRESS, Pages: 55-66, ISSN: 0922-6389 -
Journal articleCursi 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: 2218-6581Accurate 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.
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Journal articleMeyer H, Dawes T, Serrani M, et al., 2020,
Genetic and functional insights into the fractal structure of the heart
, Nature, Vol: 584, Pages: 589-594, ISSN: 0028-0836The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a vestigeof embryonic development.1,2 The function of these trabeculae in adults and their genetic architecture are unknown. Toinvestigate this we performed a genome-wide association study using fractal analysis of trabecular morphology as animage-derived phenotype in 18,096 UK Biobank participants. We identified 16 significant loci containing genes associatedwith haemodynamic phenotypes and regulation of cytoskeletal arborisation.3,4 Using biomechanical simulations and humanobservational data, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Throughgenetic association studies with cardiac disease phenotypes and Mendelian randomisation, we find a causal relationshipbetween trabecular morphology and cardiovascular disease risk. These findings suggest an unexpected role for myocardialtrabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity, and reveal theirinfluence on susceptibility to disease
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Journal articleKostopoulou O, Nurek M, Delaney B, et al., 2020,
Disentangling the relationship between physician and organizational performance: a signal detection approach
, Medical Decision Making, Vol: 40, Pages: 746-755, ISSN: 0272-989XBackground. In previous research, we employed a signal detection approach to measure the performance of general practitioners (GPs) when deciding about urgent referral for suspected lung cancer. We also explored associations between provider and organizational performance. We found that GPs from practices with higher referral positive predictive value (PPV; chance of referrals identifying cancer) were more reluctant to refer than those from practices with lower PPV. Here, we test the generalizability of our findings to a different cancer. Methods. A total of 252 GPs responded to 48 vignettes describing patients with possible colorectal cancer. For each vignette, respondents decided whether urgent referral to a specialist was needed. They then completed the 8-item Stress from Uncertainty scale. We measured GPs’ discrimination (d′) and response bias (criterion; c) and their associations with organizational performance and GP demographics. We also measured correlations of d′ and c between the 2 studies for the 165 GPs who participated in both. Results. As in the lung study, organizational PPV was associated with response bias: in practices with higher PPV, GPs had higher criterion (b = 0.05 [0.03 to 0.07]; P < 0.001), that is, they were less inclined to refer. As in the lung study, female GPs were more inclined to refer than males (b = −0.17 [−0.30 to −0.105]; P = 0.005). In a mediation model, stress from uncertainty did not explain the gender difference. Only response bias correlated between the 2 studies (r = 0.39, P < 0.001). Conclusions. This study confirms our previous findings regarding the relationship between provider and organizational performance and strengthens the finding of gender differences in referral decision making. It also provides evidence that response bias is a relatively stable feature of GP referral decision making.
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Journal articleFalck F, Doshi S, Tormento M, et al., 2020,
Robot DE NIRO: a human-centered, autonomous, mobile research platform for cognitively-enhanced manipulation
, Frontiers in Robotics and AI, Vol: A17, ISSN: 2296-9144We 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/
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Journal articleNurek M, Delaney BC, Kostopoulou O, 2020,
Risk assessment and antibiotic prescribing decisions in children presenting to UK primary care with cough: a vignette study
, BMJ Open, Vol: 10, ISSN: 2044-6055Objectives: The validated “STARWAVe” clinical prediction rule (CPR) uses seven variables to guide risk assessment and antimicrobial stewardship in children presenting with cough(Short illness duration, Temperature, Age, Recession, Wheeze, Asthma,Vomiting). We aimed to compare General Practitioners’ (GPs) risk assessments and prescribing decisions to those of STARWAVe, and assess the influence of the CPR’s clinical variables. Setting: Primary care. Participants: 252 GPs, currently practising in the UK. Design: GPs were randomly assigned to view four (of a possible eight) clinical vignettes online. Each vignette depicted a child presenting with cough, who was described in terms of the seven STARWAVe variables. Systematically, we manipulated patient age (20 months vs. 5 years), illness duration (3 vs. 6 days),vomiting (present vs. absent) and wheeze (present vs. absent), holding the remaining STARWAVe variables constant. Outcome measures:Per vignette, GPs assessed risk of hospitalisation and indicated whether they would prescribe antibiotics or not. Results: GPs overestimated risk of hospitalisationin 9% of vignette presentations (88/1008) and underestimated it in 46% (459/1008). Despite underestimating risk, they overprescribed: 78% of prescriptions were unnecessary relative to GPs’ own risk assessments (121/156), while 83% were unnecessary relativeto STARWAVe risk assessments (130/156). All four of the manipulated variables influenced risk assessments, but only three influenced prescribing decisions: a shorter illness duration reduced prescribing odds (OR 0.14, 95% CI 0.08-0.27, p<0.001), while vomiting and wheeze increased them (ORvomit2.17, 95% CI 1.32-3.57, p=0.002; ORwheeze8.98, 95% CI 4.99-16.15, p<0.001). Conclusions: Relative to STARWAVe, GPs underestimated riskof hospitalisation, overprescribed, and appeared to
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Conference paperFlageat 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: 273-282Quality-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.
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Conference paperCarvalho EDC, Clark R, Nicastro A, et al., 2020,
Scalable uncertainty for computer vision with functional variationalinference
, CVPR 2020, Publisher: IEEE, Pages: 12003-12013As 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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Journal articleBiffi C, Cerrolaza Martinez JJ, Tarroni G, et al., 2020,
Explainable anatomical shape analysis through deep hierarchical generative models
, IEEE Transactions on Medical Imaging, Vol: 39, Pages: 2088-2099, ISSN: 0278-0062Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer’s disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating highthroughput analysis of normal anatomy and pathology in largescale studies of volumetric imaging.
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Journal articleBaroni P, Toni F, Verheij B, 2020,
On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games: 25 years later Foreword
, ARGUMENT & COMPUTATION, Vol: 11, Pages: 1-14, ISSN: 1946-2166 -
Conference paperČyras K, Karamlou A, Lee M, et al., 2020,
AI-assisted schedule explainer for nurse rostering
, AAMAS, Pages: 2101-2103, ISSN: 1548-8403We present an argumentation-supported explanation generating system, called Schedule Explainer, that assists with makespan scheduling. Our stand-alone generic tool explains to a lay user why a resource allocation schedule is good or not, and offers actions to improve the schedule given the user's constraints. Schedule Explainer provides actionable textual explanations via an interactive graphical interface. We illustrate our system with a proof-of-concept application tool in a nurse rostering scenario whereby a shift-lead nurse aims to account for unexpected events by rescheduling some patient procedures to nurses and is aided by the system to do so.
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Conference paperAlbini E, Rago A, Baroni P, et al., 2020,
Relation-Based Counterfactual Explanations for Bayesian Network Classifiers
, The 29th International Joint Conference on Artificial Intelligence (IJCAI 2020) -
Journal articleTsai Y-Y, Xiao B, Johns E, et al., 2020,
Constrained-space optimization and reinforcement learning for complex tasks
, IEEE Robotics and Automation Letters, Vol: 5, Pages: 683-690, ISSN: 2377-3766Learning from demonstration is increasingly used for transferring operator manipulation skills to robots. In practice, it is important to cater for limited data and imperfect human demonstrations, as well as underlying safety constraints. This article presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks. Through interactions within the constrained space, the reinforcement learning agent is trained to optimize the manipulation skills according to a defined reward function. After learning, the optimal policy is derived from the well-trained reinforcement learning agent, which is then implemented to guide the robot to conduct tasks that are similar to the experts' demonstrations. The effectiveness of the proposed method is verified with a robotic suturing task, demonstrating that the learned policy outperformed the experts' demonstrations in terms of the smoothness of the joint motion and end-effector trajectories, as well as the overall task completion time.
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Journal articleDur TH, Arcucci R, Mottet L, et al., 2020,
Weak Constraint Gaussian Processes for optimal sensor placement
, JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 42, ISSN: 1877-7503 -
Journal articleWu P, Sun J, Chang X, et al., 2020,
Data-driven reduced order model with temporal convolutional neural network
, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 360, ISSN: 0045-7825- Author Web Link
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- Citations: 53
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Journal articleCalvo RA, Peters D, Cave S, 2020,
Advancing impact assessment for intelligent systems
, Nature Machine Intelligence, Vol: 2, Pages: 89-91, ISSN: 2522-5839
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