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Conference paperRago A, Russo F, Albini E, et al., 2022,
Forging argumentative explanations from causal models
, Proceedings of the 5th Workshop on Advances in Argumentation in Artificial Intelligence 2021 co-located with the 20th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2021), Publisher: CEUR Workshop Proceedings, Pages: 1-15, ISSN: 1613-0073We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for models' outputs. The conceptualisation is based on reinterpreting properties of semantics of AFs as explanation moulds, which are means for characterising argumentative relations. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement in bipolar AFs, showing how the extracted bipolar AFs may be used as relation-based explanations for the outputs of causal models.
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Journal articleAlAttar 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: 2296-9144Model 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.
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Journal articleWang 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: 2296-9144We 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.
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Journal articleCursi F, Bai W, Yeatman EM, et al., 2022,
GlobDesOpt: a global optimization framework for optimal robot manipulator design
, IEEE Access, Vol: 10, Pages: 5012-5023, ISSN: 2169-3536Robot 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.
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Journal articleBuizza C, Casas CQ, Nadler P, et al., 2022,
Data Learning: Integrating Data Assimilation and Machine Learning
, JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 58, ISSN: 1877-7503- Author Web Link
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- Citations: 18
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Conference paperCursi 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: 201-207Model 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.
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Conference paperCheng S, Quilodran-Casas C, Arcucci R, 2022,
Reduced Order Surrogate Modelling and Latent Assimilation for Dynamical Systems
, 22nd Annual International Conference on Computational Science (ICCS), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 31-44, ISSN: 0302-9743 -
Conference paperLever J, Arcucci R, 2022,
Towards Social Machine Learning for Natural Disasters
, 22nd Annual International Conference on Computational Science (ICCS), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 756-769, ISSN: 0302-9743 -
Conference paperLever J, Arcucci R, Cai J, 2022,
Social Data Assimilation of Human Sensor Networks for Wildfires
, 15th ACM International Conference on Pervasive Technologies Related to Assistive Environments (PETRA), Publisher: ASSOC COMPUTING MACHINERY, Pages: 455-462 -
Conference paperArcucci R, Casas CQ, Joshi A, et al., 2022,
Merging Real Images with Physics Simulations via Data Assimilation
, 27th International European Conference on Parallel and Distributed Computing (Euro-Par), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 255-266, ISSN: 0302-9743 -
Conference paperLim B, Allard M, Grillotti L, et al., 2022,
QDax: On the Benefits of Massive Parallelization for Quality-Diversity
, Genetic and Evolutionary Computation Conference (GECCO), Publisher: ASSOC COMPUTING MACHINERY, Pages: 128-131 -
Conference paperCursi 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, Pages: 8852-8859Robot 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.
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Conference paperCursi 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), Publisher: IEEE, Pages: 4040-4047In 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.
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Conference paperLa Barbera V, Pardo F, Tassa Y, et al., 2021,
OstrichRL: a musculoskeletal ostrich simulation to study bio-mechanical locomotion
, NeurIPS 2021Muscle-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.
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Journal articleLiu Z, Peach R, Lawrance E, et al., 2021,
Listening to mental health crisis needs at scale: using Natural Language Processing to understand and evaluate a mental health crisis text messaging service
, Frontiers in Digital Health, Vol: 3, Pages: 1-14, ISSN: 2673-253XThe current mental health crisis is a growing public health issue requiring a large-scale response that cannot be met with traditional services alone. Digital support tools are proliferating, yet most are not systematically evaluated, and we know little about their users and their needs. Shout is a free mental health text messaging service run by the charity Mental Health Innovations, which provides support for individuals in the UK experiencing mental or emotional distress and seeking help. Here we study a large data set of anonymised text message conversations and post-conversation surveys compiled through Shout. This data provides an opportunity to hear at scale from those experiencing distress; to better understand mental health needs for people not using traditional mental health services; and to evaluate the impact of a novel form of crisis support. We use natural language processing (NLP) to assess the adherence of volunteers to conversation techniques and formats, and to gain insight into demographic user groups and their behavioural expressions of distress. Our textual analyses achieve accurate classification of conversation stages (weighted accuracy = 88%), behaviours (1-hamming loss = 95%) and texter demographics (weighted accuracy = 96%), exemplifying how the application of NLP to frontline mental health data sets can aid with post-hoc analysis and evaluation of quality of service provision in digital mental health services.
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Conference paperSukpanichnant P, Rago A, Lertvittayakumjorn P, et al., 2021,
LRP-based argumentative explanations for neural networks
, XAI.it 2021 - Italian Workshop on Explainable Artificial Intelligence, Pages: 71-84, ISSN: 1613-0073In recent years, there have been many attempts to combine XAI with the field of symbolic AI in order to generate explanations for neural networks that are more interpretable and better align with human reasoning, with one prominent candidate for this synergy being the sub-field of computational argumentation. One method is to represent neural networks with quantitative bipolar argumentation frameworks (QBAFs) equipped with a particular semantics. The resulting QBAF can then be viewed as an explanation for the associated neural network. In this paper, we explore a novel LRP-based semantics under a new QBAF variant, namely neural QBAFs (nQBAFs). Since an nQBAF of a neural network is typically large, the nQBAF must be simplified before being used as an explanation. Our empirical evaluation indicates that the manner of this simplification is all important for the quality of the resulting explanation.
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Conference paperLiu Z, Barahona M, 2021,
Similarity measure for sparse time course data based on Gaussian processes
, Uncertainty in Artificial Intelligence 2021, Publisher: PMLR, Pages: 1332-1341We propose a similarity measure for sparsely sampled time course data in the form of a log-likelihood ratio of Gaussian processes (GP). The proposed GP similarity is similar to a Bayes factor and provides enhanced robustness to noise in sparse time series, such as those found in various biological settings, e.g., gene transcriptomics. We show that the GP measure is equivalent to the Euclidean distance when the noise variance in the GP is negligible compared to the noise variance of the signal. Our numerical experiments on both synthetic and real data show improved performance of the GP similarity when used in conjunction with two distance-based clustering methods.
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Conference paperKouvaros P, Kyono T, Leofante F, et al., 2021,
Formal analysis of neural network-based systems in the aircraft domain
, International Symposium on Formal Methods, Publisher: Springer International Publishing, Pages: 730-740, ISSN: 0302-9743Neural networks are being increasingly used for efficient decision making in the aircraft domain. Given the safety-critical nature of the applications involved, stringent safety requirements must be met by these networks. In this work we present a formal study of two neural network-based systems developed by Boeing. The Venus verifier is used to analyse the conditions under which these systems can operate safely, or generate counterexamples that show when safety cannot be guaranteed. Our results confirm the applicability of formal verification to the settings considered.
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Conference paperKotonya N, Spooner T, Magazzeni D, et al., 2021,
Graph reasoning with context-aware linearization for interpretable fact extraction and verification
, FEVER 2021, Publisher: Association for Computational Linguistics, Pages: 21-30This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset. We experiment with both a multi-task learning paradigm to jointly train a graph attention network for both the task of evidence extraction and veracity prediction, as well as a single objective graph model for solely learning veracity prediction and separate evidence extraction. In both instances, we employ a framework for per-cell linearization of tabular evidence, thus allowing us to treat evidence from tables as sequences. The templates we employ for linearizing tables capture the context as well as the content of table data. We furthermore provide a case study to show the interpretability our approach. Our best performing system achieves a FEVEROUS score of 0.23 and 53% label accuracy on the blind test data.
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Journal articleHay AD, Moore M, Taylor J, et al., 2021,
Immediate oral versus immediate topical versus delayed oral antibiotics for children with acute otitis media with discharge: the REST three-arm non-inferiority electronic platform-supported RCT Introduction
, HEALTH TECHNOLOGY ASSESSMENT, Vol: 25, Pages: 1-+, ISSN: 1366-5278
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