Publications
254 results found
Candela E, Doustaly O, Parada L, et al., 2023, Risk-aware controller for autonomous vehicles using model-based collision prediction and reinforcement learning, Artificial Intelligence, Vol: 320, ISSN: 0004-3702
Autonomous Vehicles (AVs) have the potential to save millions of lives and increase the efficiency of transportation services. However, the successful deployment of AVs requires tackling multiple challenges related to modeling and certifying safety. State-of-the-art decision-making methods usually rely on end-to-end learning or imitation learning approaches, which still pose significant safety risks. Hence the necessity of risk-aware AVs that can better predict and handle dangerous situations. Furthermore, current approaches tend to lack explainability due to their reliance on end-to-end Deep Learning, where significant causal relationships are not guaranteed to be learned from data. This paper introduces a novel risk-aware framework for training AV agents using a bespoke collision prediction model and Reinforcement Learning (RL). The collision prediction model is based on Gaussian Processes and vehicle dynamics, and is used to generate the RL state vector. Using an explicit risk model increases the post-hoc explainability of the AV agent, which is vital for reaching and certifying the high safety levels required for AVs and other safety-sensitive applications. Experimental results obtained with a simulator and state-of-the-art RL algorithms show that the risk-aware RL framework decreases average collision rates by 15%, makes AVs more robust to sudden harsh braking situations, and achieves better performance in both safety and speed when compared to a standard rule-based method (the Intelligent Driver Model). Moreover, the proposed collision prediction model outperforms other models in the literature.
Zhang X, Angeloudis P, Demiris Y, 2023, Dual-branch Spatio-Temporal Graph Neural Networks for Pedestrian Trajectory Prediction, Pattern Recognition, ISSN: 0031-3203
Zhang X, Demiris Y, 2023, Visible and Infrared Image Fusion using Deep Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence
Zolotas M, Demiris Y, 2022, Disentangled sequence clustering for human intention inference, IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEE, Pages: 9814-9820, ISSN: 2153-0866
Equipping robots with the ability to infer human intent is a vital precondition for effective collaboration. Most computational approaches towards this objective derive a probability distribution of “intent” conditioned on the robot’s perceived state. However, these approaches typically assumetask-specific labels of human intent are known a priori. To overcome this constraint, we propose the Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE), a clustering framework capable of learning such a distribution of intent in an unsupervised manner. The proposed framework leverages recent advances in unsupervised learning to disentangle latentrepresentations of sequence data, separating time-varying local features from time-invariant global attributes. As a novel extension, the DiSCVAE also infers a discrete variable to form a latent mixture model and thus enable clustering over these global sequence concepts, e.g. high-level intentions. We evaluate the DiSCVAE on a real-world human-robot interaction datasetcollected using a robotic wheelchair. Our findings reveal that the inferred discrete variable coincides with human intent, holding promise for collaborative settings, such as shared control.
Chacon Quesada R, Demiris Y, 2022, Holo-SpoK: Affordance-aware augmented reality control of legged manipulators, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 856-862
Although there is extensive research regarding legged manipulators, comparatively little focuses on their User Interfaces (UIs). Towards extending the state-of-art in this domain, in this work, we integrate a Boston Dynamics(BD) Spot® with a light-weight 7 DoF Kinova® robot arm and a Robotiq® 2F-85 gripper into a legged manipulator. Furthermore, we jointly control the robotic platform using an affordance-aware Augmented Reality (AR) Head-Mounted Display (HMD) UI developed for the Microsoft HoloLens 2. We named the combined platform Holo-SpoK. Moreover, we explain how this manipulator colocalises with the HoloLens 2 for its control through AR. In addition, we present the details of our algorithms for autonomously detecting grasp-ability affordances and for the refinement of the positions obtainedvia vision-based colocalisation. We validate the suitability of our proposed methods with multiple navigation and manipulation experiments. To the best of our knowledge, this is the first demonstration of an AR HMD UI for controlling legged manipulators.
Amadori PV, Fischer T, Wang R, et al., 2022, Predicting secondary task performance: a directly actionable metric for cognitive overload detection, IEEE Transactions on Cognitive and Developmental Systems, Vol: 14, Pages: 1474-1485, ISSN: 2379-8920
In this paper, we address cognitive overload detection from unobtrusive physiological signals for users in dual-tasking scenarios. Anticipating cognitive overload is a pivotal challenge in interactive cognitive systems and could lead to safer shared-control between users and assistance systems. Our framework builds on the assumption that decision mistakes on the cognitive secondary task of dual-tasking users correspond to cognitive overload events, wherein the cognitive resources required to perform the task exceed the ones available to the users. We propose DecNet, an end-to-end sequence-to-sequence deep learning model that infers in real-time the likelihood of user mistakes on the secondary task, i.e., the practical impact of cognitive overload, from eye-gaze and head-pose data. We train and test DecNet on a dataset collected in a simulated driving setup from a cohort of 20 users on two dual-tasking decision-making scenarios, with either visual or auditory decision stimuli. DecNet anticipates cognitive overload events in both scenarios and can perform in time-constrained scenarios, anticipating cognitive overload events up to 2s before they occur. We show that DecNet’s performance gap between audio and visual scenarios is consistent with user perceived difficulty. This suggests that single modality stimulation induces higher cognitive load on users, hindering their decision-making abilities.
Dragostinov Y, Harðardóttir D, McKenna PE, et al., 2022, Preliminary psychometric scale development using the mixed methods Delphi technique, Methods in Psychology, Vol: 7
This study implemented a Delphi Method; a systematic technique which relies on a panel of experts to achieve consensus, to evaluate which questionnaire items would be the most relevant for developing a new Propensity to Trust scale. Following an initial research team moderation phase, two surveys were administered to academic lecturers, professors and Ph.D. candidates specialising in the fields of either individual differences, human-robot interaction, or occupational psychology. Results from 28 experts produced 33 final questionnaire items that were deemed relevant for evaluating trust. We discuss the importance of content validity when implementing scales, while emphasising the need for more documented scale development processes in psychology. Furthermore, we propose that the Delphi technique could be utilised as an effective and economical method for achieving content validity, while also providing greater scale creation transparency.
Nunes UM, Demiris Y, 2022, Robust Event-Based Vision Model Estimation by Dispersion Minimisation, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 44, Pages: 9561-9573, ISSN: 0162-8828
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- Citations: 3
Zhang X, Angeloudis P, Demiris Y, 2022, ST CrossingPose: a spatial-temporal graph convolutional network for skeleton-based pedestrian crossing intention prediction, IEEE Transactions on Intelligent Transportation Systems, Vol: 23, Pages: 20773-20782, ISSN: 1524-9050
Pedestrian crossing intention prediction is crucial for the safety of pedestrians in the context of both autonomous and conventional vehicles and has attracted widespread interest recently. Various methods have been proposed to perform pedestrian crossing intention prediction, among which the skeleton-based methods have been very popular in recent years. However, most existing studies utilize manually designed features to handle skeleton data, limiting the performance of these methods. To solve this issue, we propose to predict pedestrian crossing intention based on spatial-temporal graph convolutional networks using skeleton data (ST CrossingPose). The proposed method can learn both spatial and temporal patterns from skeleton data, thus having a good feature representation ability. Extensive experiments on a public dataset demonstrate that the proposed method achieves very competitive performance in predicting crossing intention while maintaining a fast inference speed. We also analyze the effect of several factors, e.g., size of pedestrians, time to event, and occlusion, on the proposed method.
Zhang X, Feng Y, Angeloudis P, et al., 2022, Monocular visual traffic surveillance: a review, IEEE Transactions on Intelligent Transportation Systems, Vol: 23, Pages: 14148-14165, ISSN: 1524-9050
To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined.
Al-Hindawi A, Vizcaychipi M, Demiris Y, 2022, Faster, better blink detection through curriculum learning by augmentation, ETRA '22: 2022 Symposium on Eye Tracking Research and Applications, Publisher: ACM, Pages: 1-7
Blinking is a useful biological signal that can gate gaze regression models to avoid the use of incorrect data in downstream tasks. Existing datasets are imbalanced both in frequency of class but also in intra-class difficulty which we demonstrate is a barrier for curriculum learning. We thus propose a novel curriculum augmentation scheme that aims to address frequency and difficulty imbalances implicitly which are are terming Curriculum Learning by Augmentation (CLbA).Using Curriculum Learning by Augmentation (CLbA), we achieve a state-of-the-art performance of mean Average Precision (mAP) 0.971 using ResNet-18 up from the previous state-of-the-art of mean Average Precision (mAP) of 0.757 using DenseNet-121 whilst outcompeting Curriculum Learning by Bootstrapping (CLbB) by a significant margin with improved calibration. This new training scheme thus allows the use of smaller and more performant Convolutional Neural Network (CNN) backbones fulfilling Nyquist criteria to achieve a sampling frequency of 102.3Hz. This paves the way for inference of blinking in real-time applications.
Al-Hindawi A, Vizcaychipi MP, Demiris Y, 2022, What is the patient looking at? Robust gaze-scene intersection under free-viewing conditions, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 2430-2434, ISSN: 1520-6149
Locating the user’s gaze in the scene, also known as Point of Regard (PoR) estimation, following gaze regression is important for many downstream tasks. Current techniques either require the user to wear and calibrate instruments, require significant pre-processing of the scene information, or place restrictions on user’s head movements.We propose a geometrically inspired algorithm that, despite its simplicity, provides high accuracy and O(J) performance under a variety of challenging situations including sparse depth maps, high noise, and high dynamic parallax between the user and the scene camera. We demonstrate the utility of the proposed algorithm in regressing the PoR from scenes captured in the Intensive Care Unit (ICU) at Chelsea & Westminster Hospital NHS Foundation Trust a .
Bin Razali MH, Demiris Y, 2022, Using a single input to forecast human action keystates in everyday pick and place actions, IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, Pages: 3488-3492
We define action keystates as the start or end of an actionthat contains information such as the human pose and time.Existing methods that forecast the human pose use recurrentnetworks that input and output a sequence of poses. In this pa-per, we present a method tailored for everyday pick and placeactions where the object of interest is known. In contrast toexisting methods, ours uses an input from a single timestep todirectly forecast (i) the key pose the instant the pick or placeaction is performed and (ii) the time it takes to get to the pre-dicted key pose. Experimental results show that our methodoutperforms the state-of-the-art for key pose forecasting andis comparable for time forecasting while running at least anorder of magnitude faster. Further ablative studies reveal thesignificance of the object of interest in enabling the total num-ber of parameters across all existing methods to be reduced byat least 90% without any degradation in performance.
Zhang F, Demiris Y, 2022, Learning garment manipulation policies toward robot-assisted dressing., Science Robotics, Vol: 7, Pages: eabm6010-eabm6010, ISSN: 2470-9476
Assistive robots have the potential to support people with disabilities in a variety of activities of daily living, such as dressing. People who have completely lost their upper limb movement functionality may benefit from robot-assisted dressing, which involves complex deformable garment manipulation. Here, we report a dressing pipeline intended for these people and experimentally validate it on a medical training manikin. The pipeline is composed of the robot grasping a hospital gown hung on a rail, fully unfolding the gown, navigating around a bed, and lifting up the user's arms in sequence to finally dress the user. To automate this pipeline, we address two fundamental challenges: first, learning manipulation policies to bring the garment from an uncertain state into a configuration that facilitates robust dressing; second, transferring the deformable object manipulation policies learned in simulation to real world to leverage cost-effective data generation. We tackle the first challenge by proposing an active pre-grasp manipulation approach that learns to isolate the garment grasping area before grasping. The approach combines prehensile and nonprehensile actions and thus alleviates grasping-only behavioral uncertainties. For the second challenge, we bridge the sim-to-real gap of deformable object policy transfer by approximating the simulator to real-world garment physics. A contrastive neural network is introduced to compare pairs of real and simulated garment observations, measure their physical similarity, and account for simulator parameters inaccuracies. The proposed method enables a dual-arm robot to put back-opening hospital gowns onto a medical manikin with a success rate of more than 90%.
Taniguchi T, Nagai T, Shimoda S, et al., 2022, Special issue on symbol emergence in robotics and cognitive systems (II), ADVANCED ROBOTICS, Vol: 36, Pages: 217-218, ISSN: 0169-1864
Kaptein F, Kiefer B, Cully A, et al., 2022, A cloud-based robot system for long-term interaction: principles, implementation, lessons learned, ACM Transactions on Human-Robot Interaction, Vol: 11, ISSN: 2573-9522
Making the transition to long-term interaction with social-robot systems has been identified as one of the main challenges in human-robot interaction. This article identifies four design principles to address this challenge and applies them in a real-world implementation: cloud-based robot control, a modular design, one common knowledge base for all applications, and hybrid artificial intelligence for decision making and reasoning. The control architecture for this robot includes a common Knowledge-base (ontologies), Data-base, “Hybrid Artificial Brain” (dialogue manager, action selection and explainable AI), Activities Centre (Timeline, Quiz, Break and Sort, Memory, Tip of the Day, ), Embodied Conversational Agent (ECA, i.e., robot and avatar), and Dashboards (for authoring and monitoring the interaction). Further, the ECA is integrated with an expandable set of (mobile) health applications. The resulting system is a Personal Assistant for a healthy Lifestyle (PAL), which supports diabetic children with self-management and educates them on health-related issues (48 children, aged 6–14, recruited via hospitals in the Netherlands and in Italy). It is capable of autonomous interaction “in the wild” for prolonged periods of time without the need for a “Wizard-of-Oz” (up until 6 months online). PAL is an exemplary system that provides personalised, stable and diverse, long-term human-robot interaction.
Jang Y, Demiris Y, 2022, Message passing framework for vision prediction stability in human robot interaction, IEEE International Conference on Robotics and Automation 2022, Publisher: IEEE, ISSN: 2152-4092
In Human Robot Interaction (HRI) scenarios, robot systems would benefit from an understanding of the user's state, actions and their effects on the environments to enable better interactions. While there are specialised vision algorithms for different perceptual channels, such as objects, scenes, human pose, and human actions, it is worth considering how their interaction can help improve each other's output. In computer vision, individual prediction modules for these perceptual channels frequently produce noisy outputs due to the limited datasets used for training and the compartmentalisation of the perceptual channels, often resulting in noisy or unstable prediction outcomes. To stabilise vision prediction results in HRI, this paper presents a novel message passing framework that uses the memory of individual modules to correct each other's outputs. The proposed framework is designed utilising common-sense rules of physics (such as the law of gravity) to reduce noise while introducing a pipeline that helps to effectively improve the output of each other's modules. The proposed framework aims to analyse primitive human activities such as grasping an object in a video captured from the perspective of a robot. Experimental results show that the proposed framework significantly reduces the output noise of individual modules compared to the case of running independently. This pipeline can be used to measure human reactions when interacting with a robot in various HRI scenarios.
Bin Razali MH, Demiris Y, 2022, Using eye-gaze to forecast human pose in everyday pick and place actions, IEEE International Conference on Robotics and Automation
Collaborative robots that operate alongside hu-mans require the ability to understand their intent and forecasttheir pose. Among the various indicators of intent, the eyegaze is particularly important as it signals action towards thegazed object. By observing a person’s gaze, one can effectivelypredict the object of interest and subsequently, forecast theperson’s pose. We leverage this and present a method thatforecasts the human pose using gaze information for everydaypick and place actions in a home environment. Our method firstattends to fixations to locate the coordinates of the object ofinterest before inputting said coordinates to a pose forecastingnetwork. Experiments on the MoGaze dataset show that ourgaze network lowers the errors of existing pose forecastingmethods and that incorporating prior in the form of textualinstructions further lowers the errors by a significant amount.Furthermore, the use of eye gaze now allows a simple multilayerperceptron network to directly forecast the keypose.
Quesada RC, Demiris Y, 2022, Proactive robot assistance: affordance-aware augmented reality user interfaces, IEEE Robotics and Automation magazine, Vol: 29, ISSN: 1070-9932
Assistive robots have the potential to increase the autonomy and quality of life of people with disabilities [1] . Their applications include rehabilitation robots, smart wheelchairs, companion robots, mobile manipulators, and educational robots [2] . However, designing an intuitive user interface (UI) for the control of assistive robots remains a challenge, as most UIs leverage traditional control interfaces, such as joysticks and keyboards, which might be challenging and even impossible for some users. Augmented reality (AR) UIs introduce more natural interactions between people and assistive robots, potentially reaching a more diverse user base.
Girbes-Juan V, Schettino V, Gracia L, et al., 2022, Combining haptics and inertial motion capture to enhance remote control of a dual-arm robot, JOURNAL ON MULTIMODAL USER INTERFACES, Vol: 16, Pages: 219-238, ISSN: 1783-7677
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- Citations: 3
Taniguchi T, Nagai T, Shimoda S, et al., 2022, Special issue on Symbol Emergence in Robotics and Cognitive Systems (I) PREFACE, ADVANCED ROBOTICS, Vol: 36, Pages: 1-2, ISSN: 0169-1864
Candela E, Parada L, Marques L, et al., 2022, Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Real, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 8814-8820, ISSN: 2153-0858
Nunes UM, Demiris Y, 2022, Kinematic Structure Estimation of Arbitrary Articulated Rigid Objects for Event Cameras, Pages: 508-514, ISSN: 1050-4729
We propose a novel method that estimates the Kinematic Structure (KS) of arbitrary articulated rigid objects from event-based data. Event cameras are emerging sensors that asynchronously report brightness changes with a time resolution of microseconds, making them suitable candidates for motion-related perception. By assuming that an articulated rigid object is composed of body parts whose shape can be approximately described by a Gaussian distribution, we jointly segment the different parts by combining an adapted Bayesian inference approach and incremental event-based motion estimation. The respective KS is then generated based on the segmented parts and their respective biharmonic distance, which is estimated by building an affinity matrix of points sampled from the estimated Gaussian distributions. The method outperforms frame-based methods in sequences obtained by simulating events from video sequences and achieves a solid performance on new high-speed motions sequences, which frame-based KS estimation methods can not handle.
Shipman A, Mead D, Feng Y, et al., 2022, Novel trajectory prediction algorithm using a full dataset: comparison and ablation studies, IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), Publisher: IEEE, Pages: 2401-2406, ISSN: 2153-0009
Casado FE, Demiris Y, 2022, Federated Learning from Demonstration for Active Assistance to Smart Wheelchair Users, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 9326-9331, ISSN: 2153-0858
Nunes UM, Demiris Y, 2022, Kinematic Structure Estimation of Arbitrary Articulated Rigid Objects for Event Cameras, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE
Razali H, Demiris Y, 2021, Multitask variational autoencoding of human-to-human object handover, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 7315-7320, ISSN: 2153-0858
Assistive robots that operate alongside humans require the ability to understand and replicate human behaviours during a handover. A handover is defined as a joint action between two participants in which a giver hands an object over to the receiver. In this paper, we present a method for learning human-to-human handovers observed from motion capture data. Given the giver and receiver pose from a single timestep, and the object label in the form of a word embedding, our Multitask Variational Autoencoder jointly forecasts their pose as well as the orientation of the object held by the giver at handover. Our method is in large contrast to existing works for human pose forecasting that employ deep autoregressive models requiring a sequence of inputs. Furthermore, our method is novel in that it learns both the human pose and object orientation in a joint manner. Experimental results on the publicly available Handover Orientation and Motion Capture Dataset show that our proposed method outperforms the autoregressive baselines for handover pose forecasting by approximately 20% while being on-par for object orientation prediction with a runtime that is 5x faster. a
Al-Hindawi A, Vizcaychipi MP, Demiris Y, 2021, Continuous non-invasive eye tracking in intensive care, 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC), Publisher: IEEE, Pages: 1869-1873, ISSN: 1557-170X
Delirium, an acute confusional state, is a common occurrence in Intensive Care Units (ICUs). Patients who develop delirium have globally worse outcomes than those who do not and thus the diagnosis of delirium is of importance. Current diagnostic methods have several limitations leading to the suggestion of eye-tracking for its diagnosis through in-attention. To ascertain the requirements for an eye-tracking system in an adult ICU, measurements were carried out at Chelsea & Westminster Hospital NHS Foundation Trust. Clinical criteria guided empirical requirements of invasiveness and calibration methods while accuracy and precision were measured. A non-invasive system was then developed utilising a patient-facing RGB camera and a scene-facing RGBD camera. The system’s performance was measured in a replicated laboratory environment with healthy volunteers revealing an accuracy and precision that outperforms what is required while simultaneously being non-invasive and calibration-free The system was then deployed as part of CONfuSED, a clinical feasibility study where we report aggregated data from 5 patients as well as the acceptability of the system to bedside nursing staff. To the best of our knowledge, the system is the first eye-tracking systems to be deployed in an ICU for delirium monitoring.
Nunes UM, Demiris Y, 2021, Live demonstration: incremental motion estimation for event-based cameras by dispersion minimisation, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE COMPUTER SOC, Pages: 1322-1323, ISSN: 2160-7508
Live demonstration setup. (Left) The setup consists of a DAVIS346B event camera connected to a standard consumer laptop and undergoes some motion. (Right) The motion estimates are plotted in red and, for rotation-like motions, the angular velocities provided by the camera IMU are also plotted in blue. This plot exemplifies an event camera undergoing large rotational motions (up to ~ 1000 deg/s) around the (a) x-axis, (b) y-axis and (c) z-axis. Overall, the incremental motion estimation method follows the IMU measurements. Optionally, the resultant global optical flow can also be shown, as well as the corresponding generated events by accumulating them onto the image plane (bottom left corner).
Chacon-Quesada R, Demiris Y, 2021, Augmented reality eser interfaces for heterogeneous multirobot control, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 11439-11444, ISSN: 2153-0858
Recent advances in the design of head-mounted augmented reality (AR) interfaces for assistive human-robot interaction (HRI) have allowed untrained users to rapidly and fluently control single-robot platforms. In this paper, we investigate how such interfaces transfer onto multirobot architectures, as several assistive robotics applications need to be distributed among robots that are different both physically and in terms of software. As part of this investigation, we introduce a novel head-mounted AR interface for heterogeneous multirobot control. This interface generates and displays dynamic joint-affordance signifiers, i.e. signifiers that combine and show multiple actions from different robots that can be applied simultaneously to an object. We present a user study with 15 participants analysing the effects of our approach on their perceived fluency. Participants were given the task of filling-out a cup with water making use of a multirobot platform. Our results show a clear improvement in standard HRI fluency metrics when users applied dynamic joint-affordance signifiers, as opposed to a sequence of independent actions.
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