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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151 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

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

The optimization of functions to find the best solution according to one or several objectives has a central role in many engineering and research fields. Recently, a new family of optimization algorithms, named Quality-Diversity optimization, has been introduced, and contrasts with classic algorithms. Instead of searching for a single solution, Quality-Diversity algorithms are searching for a large collection of both diverse and high-performing solutions. The role of this collection is to cover the range of possible solution types as much as possible, and to contain the best solution for each type. The contribution of this paper is threefold. Firstly, we present a unifying framework of Quality-Diversity optimization algorithms that covers the two main algorithms of this family (Multi-dimensional Archive of Phenotypic Elites and the Novelty Search with Local Competition), and that highlights the large variety of variants that can be investigated within this family. Secondly, we propose algorithms with a new selection mechanism for Quality-Diversity algorithms that outperforms all the algorithms tested in this paper. Lastly, we present a new collection management that overcomes the erosion issues observed when using unstructured collections. These three contributions are supported by extensive experimental comparisons of Quality-Diversity algorithms on three different experimental scenarios.

JOURNAL ARTICLE

Georgiou T, Demiris Y, Georgiou T, Demiris Y, Georgiou T, Demiris Y, Georgiou T, Demiris Yet al., 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

© 2017, The Author(s). Personalised content adaptation has great potential to increase user engagement in video games. Procedural generation of user-tailored content increases the self-motivation of players as they immerse themselves in the virtual world. An adaptive user model is needed to capture the skills of the player and enable automatic game content altering algorithms to fit the individual user. We propose an adaptive user modelling approach using a combination of unobtrusive physiological data to identify strengths and weaknesses in user performance in car racing games. Our system creates user-tailored tracks to improve driving habits and user experience, and to keep engagement at high levels. The user modelling approach adopts concepts from the Trace Theory framework; it uses machine learning to extract features from the user’s physiological data and game-related actions, and cluster them into low level primitives. These primitives are transformed and evaluated into higher level abstractions such as experience, exploration and attention. These abstractions are subsequently used to provide track alteration decisions for the player. Collection of data and feedback from 52 users allowed us to associate key model variables and outcomes to user responses, and to verify that the model provides statistically significant decisions personalised to the individual player. Tailored game content variations between users in our experiments, as well as the correlations with user satisfaction demonstrate that our algorithm is able to automatically incorporate user feedback in subsequent procedural content generation.

JOURNAL ARTICLE

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

In this work, we propose a novel method for rectifying damaged motion sequences in an unsupervised manner. In order to achieve maximal accuracy, the proposed model takes advantage of three key properties of the data: their sequential nature, the redundancy that manifests itself among repetitions of the same task, and the potential of knowledge transfer across different tasks. In order to do so, we formulate a factor model consisting of Gaussian Process Dynamical Systems (GPDS), where each factor corresponds to a single basic pattern in time and is able to represent their sequential nature. Factors collectively form a dictionary of fundamental trajectories shared among all sequences, thus able to capture recurrent patterns within the same or across different tasks. We employ variational inference to learn directly from incomplete sequences and perform maximum a-posteriori (MAP) estimates of the missing values. We have evaluated our model with a number of motion datasets, including robotic and human motion capture data. We have compared our approach to well-established methods in the literature in terms of their reconstruction error and our results indicate significant accuracy improvement across different datasets and missing data ratios. Concluding, we investigate the performance benefits of the multi-task learning scenario and how this improvement relates to the extent of component sharing that takes place.

JOURNAL ARTICLE

Zambelli M, Demiris Y, Zambelli M, Demiris Y, Zambelli M, Demiris Yet al., 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

Internal models play a key role in cognitive agentsby providing on the one hand predictions of sensory consequencesof motor commands (forward models), and on the other handinverse mappings (inverse models) to realise tasks involvingcontrol loops, such as imitation tasks. The ability to predictand generate new actions in continuously evolving environmentsintrinsically requiring the use of different sensory modalities isparticularly relevant for autonomous robots, which must alsobe able to adapt their models online. We present a learningarchitecture based on self-learned multimodal sensorimotor rep-resentations. To attain accurate forward models, we propose anonline heterogeneous ensemble learning method that allows usto improve the prediction accuracy by leveraging differences ofmultiple diverse predictors. We further propose a method tolearn inverse models on-the-fly to equip a robot with multimodallearning skills to perform imitation tasks using multiple sensorymodalities. We have evaluated the proposed methods on aniCub humanoid robot. Since no assumptions are made on therobot kinematic/dynamic structure, the method can be appliedto different robotic platforms.

JOURNAL ARTICLE

Chang HJ, Fischer T, Petit M, Zambelli M, Demiris Y, 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

In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching. In contrast to prior appearance and graph alignment based matching methods which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Our main contributions can be summarised as follows: (i) casting the kinematic structure correspondence problem into a hypergraph matching problem, incorporating multi-order similarities with normalising weights, (ii) a structural topology similarity measure by a new topology constrained subgraph isomorphism aggregation, (iii) a kinematic correlation measure between pairwise nodes, and (iv) a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on complex articulated synthetic and real data.

CONFERENCE PAPER

Choi J, Chang HJ, Jeong J, Demiris Y, Choi JY, Choi J, Chang H, 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

In this paper, we present a novel attention-modulatedvisual tracking algorithm that decomposes an object intointo multiple cognitive units, and trains multiple elemen-tary trackers in order to modulate the distribution of at-tention according to various feature and kernel types. Inthe integration stage it recombines the units to memorizeand recognize the target object effectively. With respectto the elementary trackers, we present a novel attentionalfeature-based correlation filter (AtCF) that focuses on dis-tinctive attentional features. The effectiveness of the pro-posed algorithm is validated through experimental compar-ison with state-of-the-art methods on widely-used trackingbenchmark datasets.

CONFERENCE PAPER

Fischer T, Demiris Y, Fischer T, Demiris Yet al., 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

Perspective taking enables humans to imagine the world from another viewpoint. This allows reasoning about the state of other agents, which in turn is used to more accurately predict their behavior. In this paper, we equip an iCub humanoid robot with the ability to perform visuospatial perspective taking (PT) using a single depth camera mounted above the robot. Our approach has the distinct benefit that the robot can be used in unconstrained environments, as opposed to previous works which employ marker-based motion capture systems. Prior to and during the PT, the iCub learns the environment, recognizes objects within the environment, and estimates the gaze of surrounding humans. We propose a new head pose estimation algorithm which shows a performance boost by normalizing the depth data to be aligned with the human head. Inspired by psychological studies, we employ two separate mechanisms for the two different types of PT. We implement line of sight tracing to determine whether an object is visible to the humans (level 1 PT). For more complex PT tasks (level 2 PT), the acquired point cloud is mentally rotated, which allows algorithms to reason as if the input data was acquired from an egocentric perspective. We show that this can be used to better judge where object are in relation to the humans. The multifaceted improvements to the PT pipeline advance the state of the art, and move PT in robots to markerless, unconstrained environments.

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, Gao Y, Chang HJ, Demiris Yet al., 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

We propose an online iterative path optimisationmethod to enable a Baxter humanoid robot to assist humanusers to dress. The robot searches for the optimal personaliseddressing path using vision and force sensor information: visioninformation is used to recognise the human pose and model themovement space of upper-body joints; force sensor informationis used for the robot to detect external force resistance andto locally adjust its motion. We propose a new stochastic pathoptimisation method based on adaptive moment estimation. Wefirst compare the proposed method with other path optimisationalgorithms on synthetic data. Experimental results show thatthe performance of the method achieves the smallest error withfewer iterations and less computation time. We also evaluatereal-world data by enabling the Baxter robot to assist realhuman users with their dressing.

CONFERENCE PAPER

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

© 2016 IEEE. Real-time adaptation of computer games' content to the users' skills and abilities can enhance the player's engagement and immersion. Understanding of the user's potential while playing is of high importance in order to allow the successful procedural generation of user-tailored content. We investigate how player models can be created in car racing games. Our user model uses a combination of data from unobtrusive sensors, while the user is playing a car racing simulator. It extracts features through machine learning techniques, which are then used to comprehend the user's gameplay, by utilising the educational theoretical frameworks of the Concept of Flow and Zone of Proximal Development. The end result is to provide at a next stage a new track that fits to the user needs, which aids both the training of the driver and their engagement in the game. In order to validate that the system is designing personalised tracks, we associated the average performance from 41 users that played the game, with the difficulty factor of the generated track. In addition, the variation in paths of the implemented tracks between users provides a good indicator for the suitability of the system.

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 Z, Kristan M, Leonardis A, Matas J, Felsberg M, Pflugfelder R, Čehovin L, Vojír T, Häger G, Lukežič A, Fernández G, Gupta A, Petrosino A, Memarmoghadam A, Martin AG, Montero AS, Vedaldi A, Robinson A, Ma AJ, Varfolomieiev A, Alatan A, Erdem A, Ghanem B, Liu B, Han B, Martinez B, Chang CM, Xu C, Sun C, Kim D, Chen D, Du D, Mishra D, Yeung DY, Gundogdu E, Erdem E, Khan F, Porikli F, Zhao F, Bunyak F, Battistone F, Zhu G, Roffo G, Sai Subrahmanyam GRK, 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 JC, Cho JI, Lee JY, Zhu J, Feng J, Gaoet 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

© Springer International Publishing Switzerland 2016. The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http: //votchallenge.net).

CONFERENCE PAPER

Petit M, Demiris Y, Petit M, Demiris Yet al., 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

Learning by instruction allows humans programming a robot to achieve a task using spoken language, without the requirement of being able to do the task themselves, which can be problematic for users with motor impairments. We provide a developmental framework to program the humanoid robot iCub without any hand-coded a-priori knowledge about any motor skills. Inspired by child development theories, the system involves hierarchical learning, starting with the human verbally labelling robot body parts. The robot can then focus its attention on a precise body part during robot motor babbling, and link the on-the-fly spoken descriptions of proto-actions to angle values of a specific joint. The direct grounding of proto-actions is possible through the use of a linear model which calculates the effects on the joint of the proto-action and the body part used, allowing a generalisation of the proto-action if the joint has never been used before. Eventually, transferring the grounding is allowed via learning by instructions where humans can combine the newly acquired proto-actions to build primitives and more complex actions by scaffolding them. The framework has been validated using a humanoid robot iCub, which is able to learn without any prior knowledge: 1) the name of its fingers and the corresponding joint number, 2) how to fold and unfold them and 3) how to close or open its hand and how to show numbers with its fingers.

CONFERENCE PAPER

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

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

Robot systems that interact with humans over extended periods of time will benefit from storing and recalling large amounts of accumulated sensorimotor and interaction data. We provide a principled framework for the cumulative organisation of streaming autobiographical data so that data can be continuously processed and augmented as the processing and reasoning abilities of the agent develop and further interactions with humans take place. As an example, we show how a kinematic structure learning algorithm reasons a-posteriori about the skeleton of a human hand. A partner can be asked to provide feedback about the augmented memories, which can in turn be supplied to the reasoning processes in order to adapt their parameters. We employ active, multi-modal remembering, so the robot as well as humans can gain insights of both the original and augmented memories. Our framework is capable of storing discrete and continuous data in real-time. The data can cover multiple modalities and several layers of abstraction (e.g. from raw sound signals over sentences to extracted meanings). We show a typical interaction with a human partner using an iCub humanoid robot. The framework is implemented in a platform-independent manner. In particular, we validate its multi platform capabilities using the iCub, Baxter and NAO robots. We also provide an interface to cloud based services, which allow automatic annotation of episodes. Our framework is geared towards the developmental robotics community, as it 1) provides a variety of interfaces for other modules, 2) unifies previous works on autobiographical memory, and 3) is licensed as open source software.

JOURNAL ARTICLE

Ribes A, Cerquides J, Demiris Y, Lopez de Mantaras R, 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 Y, Ros R, Oleari E, Pozzi C, Sacchitelli F, Baranzini D, Bagherzadhalimi A, Sanna A, Demiris Y, 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

We examine the use of role-switching as an intrinsic motivational mechanism to increase engagement in long-term child–robot interaction. The present study describes a learning framework where children between 9 and 11-years-old interact with a robot to improve their knowledge and habits with regards to healthy life-styles. Experiments were carried out in Italy where 41 children were divided in three groups interacting with: (i) a robot with a role-switching mechanism, (ii) a robot without a role-switching mechanism and (iii) an interactive video. Additionally, a control group composed of 43 more children, who were not exposed to any interactive approach, was used as a baseline of the study. During the intervention period, the three groups were exposed to three interactive sessions once a week. The aim of the study was to find any difference in healthy-habits acquisition based on alternative interactive systems, and to evaluate the effectiveness of the role-switch approach as a trigger for engagement and motivation while interacting with a robot. The results provide evidence that the rate of children adopting healthy habits during the intervention period was higher for those interacting with a robot. Moreover, alignment with the robot behaviour and achievement of higher engagement levels were also observed for those children interacting with the robot that used the role-switching mechanism. This supports the notion that role-switching facilitates sustained long-interactions between a child and a robot.

JOURNAL ARTICLE

Zambelli M, Demiris Y, Zambelli M, Demiris Yet al., 2016, Multimodal Imitation using Self-learned Sensorimotor Representations, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Publisher: IEEE, Pages: 3953-3958, ISSN: 2153-0866

Although many tasks intrinsically involve multiplemodalities, often only data from a single modality are used toimprove complex robots acquisition of new skills. We presenta method to equip robots with multimodal learning skills toachieve multimodal imitation on-the-fly on multiple concurrenttask spaces, including vision, touch and proprioception, onlyusing self-learned multimodal sensorimotor relations, withoutthe need of solving inverse kinematic problems or explicit analyticalmodels formulation. We evaluate the proposed methodon a humanoid iCub robot learning to interact with a pianokeyboard and imitating a human demonstration. Since noassumptions are made on the kinematic structure of the robot,the method can be also applied to different robotic platforms.

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, Chang HJ, Demiris Yet al., 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

In this paper we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single-view image sequence. In contrast to prior motion information based methods, which estimate relatively simple articulations, our method can generate arbitrarily complex kinematic structures with skeletal topology by a successive iterative merge process. The iterative merge process is guided by a skeleton distance function which is generated from a novel object boundary generation method from sparse points. Our main contributions can be summarised as follows: (i) Unsupervised complex articulated kinematic structure learning by combining motion and skeleton information. (ii) Iterative fine-to-coarse merging strategy for adaptive motion segmentation and structure smoothing. (iii) Skeleton estimation from sparse feature points. (iv) A new highly articulated object dataset containing multi-stage complexity with ground truth. Our experiments show that the proposed method out-performs state-of-the-art methods both quantitatively and qualitatively.

CONFERENCE PAPER

Gao Y, Chang HJ, Demiris Y, Gao Y, Chang HJ, Demiris Yet al., 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

Assistive robots can improve the well-being of disabled or frail human users by reducing the burden that activities of daily living impose on them. To enable personalised assistance, such robots benefit from building a user-specific model, so that the assistance is customised to the particular set of user abilities. In this paper, we present an end-to-end approach for home-environment assistive humanoid robots to provide personalised assistance through a dressing application for users who have upper-body movement limitations. We use randomised decision forests to estimate the upper-body pose of users captured by a top-view depth camera, and model the movement space of upper-body joints using Gaussian mixture models. The movement space of each upper-body joint consists of regions with different reaching capabilities. We propose a method which is based on real-time upper-body pose and user models to plan robot motions for assistive dressing. We validate each part of our approach and test the whole system, allowing a Baxter humanoid robot to assist human to wear a sleeveless jacket.

CONFERENCE PAPER

Georgiou T, Demiris Y, Georgiou T, Demiris Y, Georgiou T, Demiris Yet al., 2015, Predicting car states through learned models of vehicle dynamics and user behaviours, Intelligent Vehicles Symposium (IV), Publisher: IEEE, Pages: 1240-1245, ISSN: 1931-0587

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, Kormushev P, Demiris Y, Caldwell DGet al., 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

Encoders have been an inseparable part of robotssince the very beginning of modern robotics in the 1950s. As aresult, the foundations of robot control are built on the conceptsof kinematics and dynamics of articulated rigid bodies, whichrely on explicitly measuring the robot configuration in termsof joint angles – done by encoders.In this paper, we propose a radically new concept forcontrolling robots called Encoderless Robot Control (EnRoCo).The concept is based on our hypothesis that it is possible tocontrol a robot without explicitly measuring its joint angles, bymeasuring instead the effects of the actuation on its end-effector.To prove the feasibility of this unconventional control approach,we propose a proof-of-concept control algorithm for encoderlessposition control of a robot’s end-effector in task space. Wedemonstrate a prototype implementation of this controller ina dynamics simulation of a two-link robot manipulator. Theprototype controller is able to successfully control the robot’send-effector to reach a reference position, as well as to trackcontinuously a desired trajectory.Notably, we demonstrate how this novel controller can copewith something that traditional control approaches fail to do:adapt on-the-fly to changes in the kinematics of the robot, suchas changing the lengths of the links.

CONFERENCE PAPER

Kormushev P, Demiris Y, Caldwell DG, Kormushev P, Demiris Y, Caldwell DGet al., 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

This paper challenges the well-established as-sumption in robotics that in order to control a robot itis necessary to know its kinematic information, that is, thearrangement of links and joints, the link dimensions and thejoint positions. We propose a kinematic-free robot controlconcept that does not require any prior kinematic knowledge.The concept is based on our hypothesis that it is possible tocontrol a robot without explicitly measuring its joint angles, bymeasuring instead the effects of the actuation on its end-effector.We implement a proof-of-concept encoderless robot con-troller and apply it for the position control of a physical 2-DOF planar robot arm. The prototype controller is able tosuccessfully control the robot to reach a reference position, aswell as to track a continuous reference trajectory. Notably, wedemonstrate how this novel controller can cope with somethingthat traditional control approaches fail to do: adapt to drastickinematic changes such as 100% elongation of a link, 35-degreeangular offset of a joint, and even a complete overhaul of thekinematics involving the addition of new joints and links.

CONFERENCE PAPER

Kucukyilmaz A, Demiris Y, Kucukyilmaz A, Demiris Y, Kucukyilmaz A, Demiris Y, Küçükyilmaz A, Demiris Yet al., 2015, One-shot assistance estimation from expert demonstrations for a shared control wheelchair system, International Symposium on Robot and Human Interactive Communication (RO-MAN), Publisher: IEEE, Pages: 438-443

An emerging research problem in the field of assistive robotics is the design of methodologies that allow robots to provide human-like assistance to the users. Especially within the rehabilitation domain, a grand challenge is to program a robot to mimic the operation of an occupational therapist, intervening with the user when necessary so as to improve the therapeutic power of the assistive robotic system. We propose a method to estimate assistance policies from expert demonstrations to present human-like intervention during navigation in a powered wheelchair setup. For this purpose, we constructed a setting, where a human offers assistance to the user over a haptic shared control system. The robot learns from human assistance demonstrations while the user is actively driving the wheelchair in an unconstrained environment. We train a Gaussian process regression model to learn assistance commands given past and current actions of the user and the state of the environment. The results indicate that the model can estimate human assistance after only a single demonstration, i.e. in one-shot, so that the robot can help the user by selecting the appropriate assistance in a human-like fashion.

CONFERENCE PAPER

Lee K, Ognibene D, Chang HJ, Kim T-K, Demiris Y, Lee K, Ognibene D, Chang HJ, Kim TK, Demiris Y, Lee K, Ognibene D, Chang HJ, Kim T-K, Demiris Y, Lee K, Ognibene D, Chang H, Kim T-K, Demiris Yet al., 2015, STARE: Spatio-Temporal Attention Relocation for Multiple Structured Activities Detection, IEEE TRANSACTIONS ON IMAGE PROCESSING, Vol: 24, Pages: 5916-5927, ISSN: 1057-7149

© 2015 IEEE. We present a spatiooral attention relocation (STARE) method, an information-theoretic approach for efficient detection of simultaneously occurring structured activities. Given multiple human activities in a scene, our method dynamically focuses on the currently most informative activity. Each activity can be detected without complete observation, as the structure of sequential actions plays an important role on making the system robust to unattended observations. For such systems, the ability to decide where and when to focus is crucial to achieving high detection performances under resource bounded condition. Our main contributions can be summarized as follows: 1) information-theoretic dynamic attention relocation framework that allows the detection of multiple activities efficiently by exploiting the activity structure information and 2) a new high-resolution data set of temporally-structured concurrent activities. Our experiments on applications show that the STARE method performs efficiently while maintaining a reasonable level of accuracy.

JOURNAL ARTICLE

Ribes A, Cerquides J, Demiris Y, Lopez de Mantaras Ret al., 2015, Where is my keyboard? Model-based active adaptation of action-space in a humanoid robot, 15th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Publisher: IEEE, Pages: 602-609, ISSN: 2164-0572

CONFERENCE PAPER

Sarabia M, Lee K, Demiris Y, Sarabia M, Lee K, Demiris Y, Sarabia M, Lee K, Demiris Y, Sarabia M, Lee K, Demiris Yet al., 2015, Towards a Synchronised Grammars Framework for Adaptive Musical Human-Robot Collaboration, IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Publisher: IEEE, Pages: 715-721

We present an adaptive musical collaboration framework for interaction between a human and a robot. The aim of our work is to develop a system that receives feedback from the user in real time and learns the music progression style of the user over time. To tackle this problem, we represent a song as a hierarchically structured sequence of music primitives. By exploiting the sequential constraints of these primitives inferred from the structural information combined with user feedback, we show that a robot can play music in accordance with the user’s anticipated actions. We use Stochastic Context-Free Grammars augmented with the knowledge of the learnt user’s preferences.We provide synthetic experiments as well as a pilot study with a Baxter robot and a tangible music table. The synthetic results show the synchronisation and adaptivity features of our framework and the pilot study suggest these are applicable to create an effective musical collaboration experience.

CONFERENCE PAPER

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