Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
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Contact

 

+44 (0)20 7594 6300y.demiris Website

 
 
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Location

 

1014Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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170 results found

Elsdon J, Demiris Y, Assisted painting of 3D structures using shared control with a hand-held robot, IEEE International Conference on Robotics and Automation, Publisher: IEEE

Abstract— We present a shared control method of painting3D geometries, using a handheld robot which has a singleautonomously controlled degree of freedom. The user scansthe robot near to the desired painting location, the singlemovement axis moves the spray head to achieve the requiredpaint distribution. A simultaneous simulation of the sprayingprocedure is performed, giving an open loop approximationof the current state of the painting. An online prediction ofthe best path for the spray nozzle actuation is calculated ina receding horizon fashion. This is calculated by producing amap of the paint required in the 2D space defined by nozzleposition on the gantry and the time into the future. A directedgraph then extracts its edge weights from this paint density mapand Dijkstra’s algorithm is then used to find the candidate forthe most effective path. Due to the heavy parallelisation of thisapproach and the majority of the calculations taking place on aGPU we can run the prediction loop in 32.6ms for a predictionhorizon of 1 second, this approach is computationally efficient,outperforming a greedy algorithm. The path chosen by theproposed method on average chooses a path in the top 15%of all paths as calculated by exhaustive testing. This approachenables development of real time path planning for assistedspray painting onto complicated 3D geometries. This methodcould be applied to applications such as assistive painting forpeople with disabilities, or accurate placement of liquid whenlarge scale positioning of the head is too expensive.

CONFERENCE PAPER

Yoo YJ, Chang H, Yun S, Demiris Y, Choi JYet al., Variational autoencoded regression: high dimensional regression of visual data on complex manifold, IEEE Conference on Computer Vision and Pattern Recognition, Publisher: IEEE

This paper proposes a new high dimensional regression method by merging Gaussian process regression into a variational autoencoder framework. In contrast to other regression methods, the proposed method focuses on the case where output responses are on a complex high dimensional manifold, such as images. Our contributions are summarized as follows: (i) A new regression method estimating high dimensional image responses, which is not handled by existing regression algorithms, is proposed. (ii) The proposed regression method introduces a strategy to learn the latent space as well as the encoder and decoder so that the result of the regressed response in the latent space coincide with the corresponding response in the data space. (iii) The proposed regression is embedded into a generative model, and the whole procedure is developed by the variational autoencoder framework. We demonstrate the robustness and effectiveness of our method through a number of experiments on various visual data regression problems.

CONFERENCE PAPER

Zhang F, Cully ANTOINE, Demiris YIANNIS, Personalized Robot-assisted Dressing using User Modeling in Latent Spaces, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

CONFERENCE PAPER

Chang HJ, Demiris Y, 2017, Highly Articulated Kinematic Structure Estimation combining Motion and Skeleton Information, IEEE Transactions on Pattern Analysis and Machine Intelligence, Pages: 1-1, ISSN: 0162-8828

JOURNAL ARTICLE

Choi J, Chang HJ, Yun S, Fischer T, Demiris Y, Choi JYet al., 2017, Attentional correlation filter network for adaptive visual tracking, IEEE Conference on Computer Vision and Pattern Recognition, Publisher: IEEE

We propose a new tracking framework with an attentional mechanism that chooses a subset of the associated correlation filters for increased robustness and computational efficiency. The subset of filters is adaptively selected by a deep attentional network according to the dynamic properties of the tracking target. Our contributions are manifold, and are summarised as follows: (i) Introducing the Attentional Correlation Filter Network which allows adaptive tracking of dynamic targets. (ii) Utilising an attentional network which shifts the attention to the best candidate modules, as well as predicting the estimated accuracy of currently inactive modules. (iii) Enlarging the variety of correlation filters which cover target drift, blurriness, occlusion, scale changes, and flexible aspect ratio. (iv) Validating the robustness and efficiency of the attentional mechanism for visual tracking through a number of experiments. Our method achieves similar performance to non real-time trackers, and state-of-the-art performance amongst real-time trackers.

CONFERENCE PAPER

Cully A, Demiris Y, 2017, Quality and Diversity Optimization: A Unifying Modular Framework, IEEE Transactions on Evolutionary Computation, Pages: 1-1, ISSN: 1089-778X

JOURNAL ARTICLE

Georgiou T, Demiris Y, 2017, Adaptive user modelling in car racing games using behavioural and physiological data, USER MODELING AND USER-ADAPTED INTERACTION, Vol: 27, Pages: 267-311, ISSN: 0924-1868

JOURNAL ARTICLE

Korkinof D, Demiris Y, 2017, Multi-task and multi-kernel Gaussian process dynamical systems, PATTERN RECOGNITION, Vol: 66, Pages: 190-201, ISSN: 0031-3203

JOURNAL ARTICLE

Moulin-Frier C, Fischer T, Petit M, Pointeau G, Puigbo J-Y, Pattacini U, Low SC, Camilleri D, Nguyen P, Hoffmann M, Chang HJ, Zambelli M, Mealier A-L, Damianou A, Metta G, Prescott TJ, Demiris Y, Dominey PF, Verschure PFMJet al., 2017, DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self, IEEE Transactions on Cognitive and Developmental Systems, Pages: 1-1, ISSN: 2379-8920

JOURNAL ARTICLE

Zambelli M, Demiris Y, 2017, Online Multimodal Ensemble Learning Using Self-Learned Sensorimotor Representations, IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, Vol: 9, Pages: 113-126, ISSN: 2379-8920

JOURNAL ARTICLE

Chang HJ, Fischer T, Petit M, Zambelli M, Demiris Yet al., 2016, Kinematic Structure Correspondences via Hypergraph Matching, 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Publisher: IEEE, Pages: 4216-4225, ISSN: 1063-6919

CONFERENCE PAPER

Choi J, Chang HJ, Jeong J, Demiris Y, Choi JYet al., 2016, Visual Tracking Using Attention-Modulated Disintegration and Integration, 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Publisher: IEEE, Pages: 4321-4330, ISSN: 1063-6919

CONFERENCE PAPER

Coninx A, Baxter P, Oleari E, Bellini S, Bierman B, Henkemans OB, Canamero L, Cosi P, Enescu V, Espinoza RR, Hiolle A, Humbert R, Kiefer B, Kruijff-Korbayova I, Looije R-M, Mosconi M, Neerincx M, Paci G, Patsis G, Pozzi C, Sacchitelli F, Sahli H, Sanna A, Sommavilla G, Tesser F, Demiris Y, Belpaeme Tet al., 2016, Towards Long-Term Social Child-Robot Interaction: Using Multi-Activity Switching to Engage Young Users, JOURNAL OF HUMAN-ROBOT INTERACTION, Vol: 5, Pages: 32-67, ISSN: 2163-0364

JOURNAL ARTICLE

Fischer T, Demiris Y, 2016, Markerless Perspective Taking for Humanoid Robots in Unconstrained Environments, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 3309-3316, ISSN: 1050-4729

CONFERENCE PAPER

Gao Y, Chang HJ, Demiris Y, 2016, Personalised assistive dressing by humanoid robots using multi-modal information, Workshop on Human-Robot Interfaces for Enhanced Physical Interactions at ICRA

In this paper, we present an approach to enable a humanoid robot to provide personalised dressing assistance for human users using multi-modal information. A depth sensor is mounted on top of the robot to provide visual information, and the robot end effectors are equipped with force sensors to provide haptic information. We use visual information to model the movement range of human upper-body parts. The robot plans the dressing motions using the movement rangemodels and real-time human pose. During assistive dressing, the force sensors are used to detect external force resistances. We present how the robot locally adjusts its motions based on the detected forces. In the experiments we show that the robot can assist human to wear a sleeveless jacket while reacting tothe force resistances.

CONFERENCE PAPER

Gao Y, Chang HJ, Demiris Y, 2016, Iterative Path Optimisation for Personalised Dressing Assistance using Vision and Force Information, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 4398-4403

CONFERENCE PAPER

Georgiou T, Demiris Y, 2016, Personalised Track Design in Car Racing Games, IEEE Conference on Computational Intelligence and Games (CIG), Publisher: IEEE, ISSN: 2325-4270

CONFERENCE PAPER

Kristan M, Leonardis A, Matas J, Felsberg M, Pflugfelder R, Cehovin L, Vojir T, Hager G, Lukezic A, Fernandez G, Gupta A, Petrosino A, Memarmoghadam A, Garcia-Martin A, Montero AS, Vedaldi A, Robinson A, Ma AJ, Varfolomieiev A, Alatan A, Erdem A, Ghanem B, Liu B, Han B, Martinez B, Chang C-M, Xu C, Sun C, Kim D, Chen D, Du D, Mishra D, Yeung D-Y, Gundogdu E, Erdem E, Khan F, Porikli F, Zhao F, Bunyak F, Battistone F, Zhu G, Roffo G, Subrahmanyam GRKS, Bastos G, Seetharaman G, Medeiros H, Li H, Qi H, Bischof H, Possegger H, Lu H, Lee H, Nam H, Chang HJ, Drummond I, Valmadre J, Jeong J-C, Cho J-I, Lee J-Y, Zhu J, Feng J, Gao J, Choi JY, Xiao J, Kim J-W, Jeong J, Henriques JF, Lang J, Choi J, Martinez JM, Xing J, Gao J, Palaniappan K, Lebeda K, Gao K, Mikolajczyk K, Qin L, Wang L, Wen L, Bertinetto L, Rapuru MK, Poostchi M, Maresca M, Danelljan M, Mueller M, Zhang M, Arens M, Valstar M, Tang M, Baek M, Khan MH, Wang N, Fan N, Al-Shakarji N, Miksik O, Akin O, Moallem P, Senna P, Torr PHS, Yuen PC, Huang Q, Martin-Nieto R, Pelapur R, Bowden R, Laganiere R, Stolkin R, Walsh R, Krah SB, Li S, Zhang S, Yao S, Hadfield S, Melzi S, Lyu S, Li S, Becker S, Golodetz S, Kakanuru S, Choi S, Hu T, Mauthner T, Zhang T, Pridmore T, Santopietro V, Hu W, Li W, Huebner W, Lan X, Wang X, Li X, Li Y, Demiris Y, Wang Y, Qi Y, Yuan Z, Cai Z, Xu Z, He Z, Chi Zet al., 2016, The Visual Object Tracking VOT2016 Challenge Results, 14th European Conference on Computer Vision (ECCV), Publisher: SPRINGER INT PUBLISHING AG, Pages: 777-823, ISSN: 0302-9743

CONFERENCE PAPER

Petit M, Demiris Y, 2016, Hierarchical Action Learning by Instruction Through Interactive Grounding of Body Parts and Proto-actions, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 3375-3382, ISSN: 1050-4729

CONFERENCE PAPER

Petit M, Fischer T, Demiris Y, 2016, Lifelong Augmentation of Multimodal Streaming Autobiographical Memories, IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, Vol: 8, Pages: 201-213, ISSN: 2379-8920

JOURNAL ARTICLE

Petit M, Fischer T, Demiris Y, 2016, Towards the Emergence of Procedural Memories from Lifelong Multi-Modal Streaming Memories for Cognitive Robots, Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics at IEEE/RSJ IROS

Various research topics are emerging as the demand for intelligent lifelong interactions between robot and humans increases. Among them, we can find the examination of persistent storage, the continuous unsupervised annotation of memories and the usage of data at high-frequency over long periods of time. We recently proposed a lifelong autobiographical memory architecture tackling some of these challenges, allowing the iCub humanoid robot to 1) create new memories for both actions that are self-executed and observed from humans, 2) continuously annotate these actions in an unsupervised manner, and 3) use reasoning modules to augment these memories a-posteriori. In this paper, we present a reasoning algorithm which generalises the robots’ understanding of actions by finding the point of commonalities with the former ones. In particular, we generated and labelled templates of pointing actions in different directions. This represents a first step towards the emergence of a procedural memory within a long-term autobiographical memory framework for robots.

CONFERENCE PAPER

Ribes A, Cerquides J, Demiris Y, Lopez de Mantaras Ret al., 2016, Active Learning of Object and Body Models with Time Constraints on a Humanoid Robot, IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, Vol: 8, Pages: 26-41, ISSN: 2379-8920

JOURNAL ARTICLE

Ros R, Oleari E, Pozzi C, Sacchitelli F, Baranzini D, Bagherzadhalimi A, Sanna A, Demiris Yet al., 2016, A Motivational Approach to Support Healthy Habits in Long-term Child-Robot Interaction, International Journal of Social Robotics, Vol: 8, Pages: 599-617, ISSN: 1875-4791

JOURNAL ARTICLE

Zambelli M, Demiris Y, 2016, Multimodal Imitation using Self-learned Sensorimotor Representations, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 3953-3958

CONFERENCE PAPER

Zambelli M, Fischer T, Petit M, Chang HJ, Cully A, Demiris Yet al., 2016, Towards Anchoring Self-Learned Representations to Those of Other Agents, Workshop on Bio-inspired Social Robot Learning in Home Scenarios IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: Institute of Electrical and Electronics Engineers (IEEE)

In the future, robots will support humans in their every day activities. One particular challenge that robots will face is understanding and reasoning about the actions of other agents in order to cooperate effectively with humans. We propose to tackle this using a developmental framework, where the robot incrementally acquires knowledge, and in particular 1) self-learns a mapping between motor commands and sensory consequences, 2) rapidly acquires primitives and complex actions by verbal descriptions and instructions from a human partner, 3) discoverscorrespondences between the robots body and other articulated objects and agents, and 4) employs these correspondences to transfer the knowledge acquired from the robots point of view to the viewpoint of the other agent. We show that our approach requires very little a-priori knowledge to achieve imitation learning, to find correspondent body parts of humans, and allows taking the perspective of another agent. This represents a step towards the emergence of a mirror neuron like system based on self-learned representations.

CONFERENCE PAPER

Chang HJ, Demiris Y, 2015, Unsupervised Learning of Complex Articulated Kinematic Structures combining Motion and Skeleton Information, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 3138-3146, ISSN: 1063-6919

CONFERENCE PAPER

Gao Y, Chang HJ, Demiris Y, 2015, User Modelling for Personalised Dressing Assistance by Humanoid Robots, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 1840-1845, ISSN: 2153-0858

CONFERENCE PAPER

Georgiou T, Demiris Y, 2015, Predicting car states through learned models of vehicle dynamics and user behaviours, Intelligent Vehicles Symposium (IV), Publisher: IEEE, Pages: 1240-1245

The ability to predict forthcoming car states is crucial for the development of smart assistance systems. Forthcoming car states do not only depend on vehicle dynamics but also on user behaviour. In this paper, we describe a novel prediction methodology by combining information from both sources - vehicle and user - using Gaussian Processes. We then apply this method in the context of high speed car racing. Results show that the forthcoming position and speed of the car can be predicted with low Root Mean Square Error through the trained model.

CONFERENCE PAPER

Kormushev P, Demiris Y, Caldwell DG, 2015, Encoderless Position Control of a Two-Link Robot Manipulator, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE COMPUTER SOC, Pages: 943-949, ISSN: 1050-4729

CONFERENCE PAPER

Kormushev P, Demiris Y, Caldwell DG, 2015, Kinematic-free Position Control of a 2-DOF Planar Robot Arm, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 5518-5525, ISSN: 2153-0858

CONFERENCE PAPER

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