182 results found
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.
Ribes A, Cerquides J, Demiris Y, et 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
Ros R, Oleari E, Pozzi C, et 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
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
Zambelli M, Fischer T, Petit M, et 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.
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
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
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.
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
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
Kucukyilmaz A, Demiris Y, 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.
Lee K, Ognibene D, Chang HJ, et al., 2015, STARE: Spatio-Temporal Attention Relocation for Multiple Structured Activities Detection, IEEE TRANSACTIONS ON IMAGE PROCESSING, Vol: 24, ISSN: 1057-7149
Ribes A, Cerquides J, Demiris Y, et 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
Sarabia M, Lee K, Demiris Y, 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.
Soh H, Demiris Y, 2015, Spatio-Temporal Learning With the Online Finite and Infinite Echo-State Gaussian Processes, IEEE Transactions on Neural Networks and Learning Systems, Vol: 26, Pages: 522-536, ISSN: 2162-237X
Successful biological systems adapt to change. In this paper, we are principally concerned with adaptive systems that operate in environments where data arrives sequentially and is multivariate in nature, for example, sensory streams in robotic systems. We contribute two reservoir inspired methods: 1) the online echostate Gaussian process (OESGP) and 2) its infinite variant, the online infinite echostate Gaussian process (OIESGP) Both algorithms are iterative fixed-budget methods that learn from noisy time series. In particular, the OESGP combines the echo-state network with Bayesian online learning for Gaussian processes. Extending this to infinite reservoirs yields the OIESGP, which uses a novel recursive kernel with automatic relevance determination that enables spatial and temporal feature weighting. When fused with stochastic natural gradient descent, the kernel hyperparameters are iteratively adapted to better model the target system. Furthermore, insights into the underlying system can be gleamed from inspection of the resulting hyperparameters. Experiments on noisy benchmark problems (one-step prediction and system identification) demonstrate that our methods yield high accuracies relative to state-of-the-art methods, and standard kernels with sliding windows, particularly on problems with irrelevant dimensions. In addition, we describe two case studies in robotic learning-by-demonstration involving the Nao humanoid robot and the Assistive Robot Transport for Youngsters (ARTY) smart wheelchair.
Soh H, Demiris Y, 2015, Learning Assistance by Demonstration: Smart Mobility With Shared Control and Paired Haptic Controllers, JOURNAL OF HUMAN-ROBOT INTERACTION, Vol: 4, Pages: 76-100, ISSN: 2163-0364
Zambelli M, Demiris Y, 2015, Online Ensemble Learning of Sensorimotor Contingencies, Workshop on Sensorimotor Contingencies For Robotics at IROS
Forward models play a key role in cognitive agents by providing predictions of the sensory consequences of motor commands, also known as sensorimotor contingencies (SMCs). In continuously evolving environments, the ability to anticipate is fundamental in distinguishing cognitive from reactive agents, and it is particularly relevant for autonomous robots, that must be able to adapt their models in an online manner. Online learning skills, high accuracy of the forward models and multiple-step-ahead predictions are needed to enhance the robots’ anticipation capabilities. We propose an online heterogeneous ensemble learning method for building accurate forward models of SMCs relating motor commands to effects in robots’ sensorimotor system, in particular considering proprioception and vision. Our method achieves up to 98% higher accuracy both in short and long term predictions, compared to single predictors and other online and offline homogeneous ensembles. This method is validated on two different humanoid robots, namely the iCub and the Baxter.
Demiris Y, Aziz-Zadeh L, Bonaiuto J, 2014, Information Processing in the Mirror Neuron System in Primates and Machines, Neuroinformatics, Vol: 12, Pages: 63-91, ISSN: 1539-2791
The mirror neuron system in primates matches observations of actions with the motor representations used for their execution, and is a topic of intense research and debate in biological and computational disciplines. In robotics, models of this system have been used for enabling robots to imitate and learn how to perform tasks from human demonstrations. Yet, existing computational and robotic models of these systems are found in multiple levels of description, and although some models offer plausible explanations and testable predictions, the difference in the granularity of the experimental setups, methodologies, computational structures and selected modeled data make principled meta-analyses, common in other fields, difficult. In this paper, we adopt an interdisciplinary approach, using the BODB integrated environment in order to bring together several different but complementary computational models, by functionally decomposing them into brain operating principles (BOPs) which each capture a limited subset of the model’s functionality. We then explore links from these BOPs to neuroimaging and neurophysiological data in order to pinpoint complementary and conflicting explanations and compare predictions against selected sets of neurobiological data. The results of this comparison are used to interpret mirror system neuroimaging results in terms of neural network activity, evaluate the biological plausibility of mirror system models, and suggest new experiments that can shed light on the neural basis of mirror systems.
Ros R, Baroni I, Demiris Y, 2014, Adaptive human-robot interaction in sensorimotor task instruction: From human to robot dance tutors, Robotics and Autonomous Systems, Vol: 62, Pages: 707-720, ISSN: 1872-793X
We explore the potential for humanoid robots to interact with children in a dance activity. In this context, the robot plays the role of an instructor to guide the child through several dance moves to learn a dance phrase. We participated in 30 dance sessions in schools to study human–human interaction between children and a human dance teacher, and to identify the applied methodologies. Based on the strategies observed, both social and task-dependent, we implemented a robotic system capable of autonomously instructing dance sequences to children while displaying basic social cues to engage the child in the task. Experiments were performed in a hospital with the Nao robot interacting with 12 children through multiple encounters, when possible (18 sessions, 236 min). Observational analysis through video recordings and survey evaluations were used to assess the quality of interaction. Moreover, we introduce an involvement measure based on the aggregation of observed behavioral cues to assess the level of interest in the interaction through time. The analysis revealed high levels of involvement, while highlighting the need for further research into social engagement and adaptation with robots over repeated sessions.
Ros R, Coninx A, Demiris Y, et al., 2014, Behavioral Accommodation towards a Dance Robot Tutor, International Conference on Human-Robot Interaction, Publisher: ACM/IEEE, Pages: 278-279
We report first results on children adaptive behavior towards a dance tutoring robot. We can observe that children behavior rapidly evolves through few sessions in order to accommodate with the robotic tutor rhythm and instructions.
Soh H, Demiris Y, 2014, Incrementally Learning Objects by Touch: Online Discriminative and Generative Models for Tactile-Based Recognition, IEEE Transactions on Haptics, Vol: 7, Pages: 512-525, ISSN: 1939-1412
Human beings not only possess the remarkable ability to distinguish objects through tactile feedback but are further able to improve upon recognition competence through experience. In this work, we explore tactile-based object recognition with learners capable of incremental learning. Using the sparse online infinite Echo-State Gaussian process (OIESGP), we propose and compare two novel discriminative and generative tactile learners that produce probability distributions over objects during object grasping/ palpation. To enable iterative improvement, our online methods incorporate training samples as they become available. We also describe incremental unsupervised learning mechanisms, based on novelty scores and extreme value theory, when teacher labels are not available. We present experimental results for both supervised and unsupervised learning tasks using the iCub humanoid, with tactile sensors on its five-fingered anthropomorphic hand, and 10 different object classes. Our classifiers perform comparably to state-of-the-art methods (C4.5 and SVM classifiers) and findings indicate that tactile signals are highly relevant for making accurate object classifications. We also show that accurate “early” classifications are possible using only 20-30 percent of the grasp sequence. For unsupervised learning, our methods generate high quality clusterings relative to the widely-used sequential k-means and self-organising map (SOM), and we present analyses into the differences between the approaches.
Su Y, Dong W, Wu Y, et al., 2014, Increasing the Accuracy and the Repeatability of Position Control for Micromanipulations Using Heteroscedastic Gaussian Processes, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 4692-4698, ISSN: 1050-4729
Wu Y, Su Y, Demiris Y, 2014, A morphable template framework for robot learning by demonstration: Integrating one-shot and incremental learning approaches, Robotics and Autonomous Systems, Vol: 62, Pages: 1517-1530
Robot learning by demonstration is key to bringing robots into daily social environments to interact with and learn from human and other agents. However, teaching a robot to acquire new knowledge is a tedious and repetitive process and often restrictive to a specific setup of the environment. We propose a template-based learning framework for robot learning by demonstration to address both generalisation and adaptability. This novel framework is based upon a one-shot learning model integrated with spectral clustering and an online learning model to learn and adapt actions in similar scenarios. A set of statistical experiments is used to benchmark the framework components and shows that this approach requires no extensive training for generalisation and can adapt to environmental changes flexibly. Two real-world applications of an iCub humanoid robot playing the tic-tac-toe game and soldering a circuit board are used to demonstrate the relative merits of the framework.
Belpaeme T, Baxter PE, Read R, et al., 2013, Multimodal Child-Robot Interaction: Building Social Bonds, Journal of Human-Robot Interaction, Vol: 1, Pages: 33-53
For robots to interact effectively with human users they must be capable of coordinated, timely behavior in response to social context. The Adaptive Strategies for Sustainable Long-Term Social Interaction (ALIZ-E) project focuses on the design of long-term, adaptive social interaction between robots and child users in real-world settings. In this paper, we report on the iterative approach taken to scientific and technical developments toward this goal: advancing individual technical competen- cies and integrating them to form an autonomous robotic system for evaluation “in the wild.” The first evaluation iterations have shown the potential of this methodology in terms of adaptation of the robot to the interactant and the resulting influences on engagement. This sets the foundation for an ongoing research program that seeks to develop technologies for social robot companions.
Chatzis S, Demiris Y, 2013, The Infinite-Order Conditional Random Field Model for Sequential Data Modeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol: 6, Pages: 1523-1534, ISSN: 0162-8828
Sequential data labeling is a fundamental task in machine learning applications, with speech and natural language processing, activity recognition in video sequences, and biomedical data analysis being characteristic examples, to name just a few. The conditional random field (CRF), a log-linear model representing the conditional distribution of the observation labels, is one of the most successful approaches for sequential data labeling and classification, and has lately received significant attention in machine learning as it achieves superb prediction performance in a variety of scenarios. Nevertheless, existing CRF formulations can capture only one- or few-timestep interactions and neglect higher order dependences, which are potentially useful in many real-life sequential data modeling applications. To resolve these issues, in this paper we introduce a novel CRF formulation, based on the postulation of an energy function which entails infinitely long time-dependences between the modeled data. Building blocks of our novel approach are: 1) the sequence memoizer (SM), a recently proposed nonparametric Bayesian approach for modeling label sequences with infinitely long time dependences, and 2) a mean-field-like approximation of the model marginal likelihood, which allows for the derivation of computationally efficient inference algorithms for our model. The efficacy of the so-obtained infinite-order CRF model is experimentally demonstrated.
Chinellato E, Ognibene D, Sartori L, et al., 2013, Time to change: Deciding when to switch action plans during a social interaction, Pages: 47-58, ISSN: 0302-9743
Building on the extensive cognitive science literature on the subject, this paper introduces a model of the brain mechanisms underlying social interactions in humans and other primates. The fundamental components of the model are the "Action Observation" and "Action Planning" Systems, dedicated respectively to interpreting/recognizing the partner's movements and to plan actions suited to achieve certain goals. We have implemented a version of the model including reaching and grasping actions, and tuned on real experimental data coming from human psychophysical studies. The system is able to automatically detect the switching point in which the Action Planning System takes control over the Action Observation System, overriding the automatic imitation behaviour with a complementary social response. With such computational implementation we aim at validating the model and also at endowing an artificial agent with the ability of performing meaningful complementary responses to observed actions in social scenarios. © 2013 Springer-Verlag Berlin Heidelberg.
Korkinof D, Demiris Y, 2013, Online Quantum Mixture Regression for Trajectory Learning by Demonstration, IROS 2013, Publisher: IEEE, Pages: 3222-3229
In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quan- tum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propose an efficient stochastic online learning algorithm based on the online Expectation Maximization (EM), as well as a generation and decay scheme for model components. Our method is suitable for complex robotic applications, where data is abundant or where we wish to iteratively refine our model and conduct predictions during the course of learning. With a synthetic example, we show that the algorithm can achieve higher numerical stability. We also empirically demonstrate the efficacy of our method in well-known regression benchmark datasets. Under a trajectory Learning by Demonstration setting we employ a multi-shot learning application in joint angle space, where we observe higher quality of learning and reproduction. We compare against popular and well-established methods, widely adopted across the robotics community.
Korkinof D, Demiris Y, 2013, Online Quantum Mixture Regression for Trajectory Learning by Demonstration, International Conference on Intelligent Systems and Robots (IROS), Publisher: IEEE, Pages: 3222-3229, ISSN: 2153-0858
In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quantum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propose an efficient stochastic online learning algorithm based on the online Expectation Maximization (EM), as well as a generation and decay scheme for model components. Our method is suitable for complex robotic applications, where data is abundant or where we wish to iteratively refine our model and conduct predictions during the course of learning. With a synthetic example, we show that the algorithm can achieve higher numerical stability. We also empirically demonstrate the efficacy of our method in well-known regression benchmark datasets. Under a trajectory Learning by Demonstration setting we employ a multi-shot learning application in joint angle space, where we observe higher quality of learning and reproduction. We compare against popular and well-established methods, widely adopted across the robotics community.
Lee K, Su Y, Kim T-K, et al., 2013, A syntactic approach to robot imitation learning using probabilistic activity grammars, Robotics and Autonomous Systems, Vol: 61, Pages: 1323-1334, ISSN: 0921-8890
This paper describes a syntactic approach to imitation learning that captures important task structures in the form of probabilistic activity grammars from a reasonably small number of samples under noisy conditions. We show that these learned grammars can be recursively applied to help recognize unforeseen, more complicated tasks that share underlying structures. The grammars enforce an observation to be consistent with the previously observed behaviors which can correct unexpected, out-of-context actions due to errors of the observer and/or demonstrator. To achieve this goal, our method (1) actively searches for frequently occurring action symbols that are subsets of input samples to uncover the hierarchical structure of the demonstration, and (2) considers the uncertainties of input symbols due to imperfect low-level detectors.We evaluate the proposed method using both synthetic data and two sets of real-world humanoid robot experiments. In our Towers of Hanoi experiment, the robot learns the important constraints of the puzzle after observing demonstrators solving it. In our Dance Imitation experiment, the robot learns 3 types of dances from human demonstrations. The results suggest that under reasonable amount of noise, our method is capable of capturing the reusable task structures and generalizing them to cope with recursions.
Ognibene D, Chinellato E, Sarabia M, et al., 2013, Contextual action recognition and target localization with an active allocation of attention on a humanoid robot, Bioinspiration & Biomimetics, Vol: 8
Exploratory gaze movements are fundamental for gathering the most relevant information regarding the partner during social interactions. Inspired by the cognitive mechanisms underlying human social behaviour, we have designed and implemented a system for a dynamic attention allocation which is able to actively control gaze movements during a visual action recognition task exploiting its own action execution predictions. Our humanoid robot is able, during the observation of a partner's reaching movement, to contextually estimate the goal position of the partner's hand and the location in space of the candidate targets. This is done while actively gazing around the environment, with the purpose of optimizing the gathering of information relevant for the task. Experimental results on a simulated environment show that active gaze control, based on the internal simulation of actions, provides a relevant advantage with respect to other action perception approaches, both in terms of estimation precision and of time required to recognize an action. Moreover, our model reproduces and extends some experimental results on human attention during an action perception.
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