Carlo Ciliberto

Lecturer in Machine Learning 
Imperial College London  

Carlo Ciliberto - Researcher in Machine Learning, Structured Prediction, Multitask Learning, Computer Vision and Robotics

NIPS 2017 Prova
Autori slides

Abstract

                bibtex
              

Publications

  • Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance

    Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto NIPS 2018

    Applications of optimal transport have recently gained remarkable attention thanks to the computational advantages of entropic regularization. However, in most situations the Sinkhorn approximation of the Wasserstein distance is replaced by a regularized version that is less accurate but easy to differentiate. In this work we characterize the differential properties of the original Sinkhorn distance, proving that it enjoys the same smoothness as its regularized version and we explicitly provide an efficient algorithm to compute its gradient. We show that this result benefits both theory and applications: on one hand, high order smoothness confers statistical guarantees to learning with Wasserstein approximations. On the other hand, the gradient formula allows us to efficiently solve learning and optimization problems in practice. Promising preliminary experiments complement our analysis.

    pdf code slides video
    @inproceedings{luise2018differential,
      title={Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance},
      author={Luise, Giulia and Rudi, Alessandro and Pontil, Massimiliano and Ciliberto, Carlo},
      booktitle={Advances in Neural Information Processing Systems},
      year={2018}
    }
    

    Machine Learning, Wasserstein, Optimal Transport, Sinkhorn, Structured Prediction

  • Manifold Structured Prediction

    Alessandro Rudi*, Carlo Ciliberto*, Gian Maria Marconi, Lorenzo Rosasco, Massimiliano Pontil NIPS 2018

    Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite, albeit potentially huge, output spaces, in this paper we discuss how structured prediction can be extended to a continuous scenario. Specifically, we study a structured prediction approach to manifold valued regression. We characterize a class of problems for which the considered approach is statistically consistent and study how geometric optimization can be used to compute the corresponding estimator. Promising experimental results on both simulated and real data complete our study.

    pdf code slides video Manifold Structured Prediction. First work investigating how to impose nonlinear relations and constraints among multiple tasks
    @article{ciliberto2018manifold,
        title={Manifold Structured Prediction},
        author={Rudi, Alessandro and Ciliberto, Carlo and Gian Maria Marconi and Rosasco, Lorenzo},
        journal={Advances in Neural Information Processing Systems},
        year={2018}
    }
                    

    structured-prediction manifold manifold-regression kernel-methods

  • Incremental Learning-to-Learn with Statistical Guarantees

    Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil UAI 2018

    In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks sampled from an unknown meta distribution. In contrast to previous work on batch learning-to-learn, we consider a scenario where tasks are presented sequentially and the algorithm needs to adapt incrementally to improve its performance on future tasks. Key to this setting is for the algorithm to rapidly incorporate new observations into the model as they arrive, without keeping them in memory. We focus on the case where the underlying algorithm is ridge regression parameterized by a positive semidefinite matrix. We propose to learn this matrix by applying a stochastic strategy to minimize the empirical error incurred by ridge regression on future tasks sampled from the meta distribution. We study the statistical properties of the proposed algorithm and prove non-asymptotic bounds on its excess transfer risk, that is, the generalization performance on new tasks from the same meta distribution. We compare our online learning-to-learn approach with a state of the art batch method, both theoretically and empirically.

    pdf code slides video
    @article{denevi2018incremental,
      title={Incremental Learning-to-Learn with Statistical Guarantees},
      author={Denevi, Giulia and Ciliberto, Carlo and Stamos, Dimitris and Pontil, Massimiliano},
      journal={Uncertainty in Artificial Intelligence (UAI)},
      year={2018}
    }
    
                    

    machine learning, Incremental learning, learning to learn, online learning, transfer learning, multitask learning

  • Quantum machine learning: a classical perspective

    Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, Leonard Wossnig Proceeding of the Royal Society A 2018

    Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

    pdf code slides video
    @article {ciliberto2018quantum,
        author = {Ciliberto, Carlo and Herbster, Mark and Ialongo, Alessandro Davide and Pontil, Massimiliano and Rocchetto, Andrea and Severini, Simone and Wossnig, Leonard},
        title = {Quantum machine learning: a classical perspective},
        volume = {474},
        number = {2209},
        year = {2018},
        doi = {10.1098/rspa.2017.0551},
        publisher = {The Royal Society},
        issn = {1364-5021},
        URL = {http://rspa.royalsocietypublishing.org/content/474/2209/20170551},
        eprint = {http://rspa.royalsocietypublishing.org/content/474/2209/20170551.full.pdf},
        journal = {Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences}
    }
    
                    

    machine learning, quantum computing, large scale learning

  • Consistent Multitask Learning with Nonlinear Output Relations

    Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil NIPS 2017

    Key to multitask learning is exploiting relationships between different tasks to improve prediction performance. If the relations are linear, regularization approaches can be used successfully. However, in practice assuming the tasks to be linearly related might be restrictive, and allowing for nonlinear structures is a challenge. In this paper, we tackle this issue by casting the problem within the framework of structured prediction. Our main contribution is a novel algorithm for learning multiple tasks which are related by a system of nonlinear equations that their joint outputs need to satisfy. We show that the algorithm is consistent and can be efficiently implemented. Experimental results show the potential of the proposed method.

    pdf code slides video Consistent Multitask Learning with Nonlinear Output Relations. First work investigating how to impose nonlinear relations and constraints among multiple tasks
    @article{ciliberto2017consistent,
        title={Consistent Multitask Learning with Nonlinear Output Relations},
        author={Ciliberto, Carlo and Rudi, Alessandro and Rosasco, Lorenzo and Pontil, Massimiliano},
        journal={Advances in Neural Information Processing Systems},
        year={2017}
    }
                    

    structured-prediction multitask-learning non-linear-multitask learning kernel-methods

  • Visual recognition for humanoid robots

    Sean Ryan Fanello, Carlo Ciliberto, Nicoletta Noceti, Giorgio Metta, Francesca Odone Robotics and Autonomous Systems 2017

    Visual perception is a fundamental component for most robotics systems operating in human environments. Specifically, visual recognition is a prerequisite to a large variety of tasks such as tracking, manipulation, human–robot interaction. As a consequence, the lack of successful recognition often becomes a bottleneck for the application of robotics system to real-world situations. In this paper we aim at improving the robot visual perception capabilities in a natural, human-like fashion, with a very limited amount of constraints to the acquisition scenario. In particular our goal is to build and analyze a learning system that can rapidly be re-trained in order to incorporate new evidence if available. To this purpose, we review the state-of-the-art coding–pooling pipelines for visual recognition and propose two modifications which allow us to improve the quality of the representation, while maintaining real-time performances: a coding scheme, Best Code Entries (BCE), and a new pooling operator, Mid-Level Classification Weights (MLCW). The former focuses entirely on sparsity and improves the stability and computational efficiency of the coding phase, the latter increases the discriminability of the visual representation, and therefore the overall recognition accuracy of the system, by exploiting data supervision. The proposed pipeline is assessed from a qualitative perspective on a Human–Robot Interaction (HRI) application on the iCub platform. Quantitative evaluation of the proposed system is performed both on in-house robotics data-sets (iCubWorld) and on established computer vision benchmarks (Caltech-256, PASCAL VOC 2007). As a byproduct of this work, we provide for the robotics community an implementation of the proposed visual recognition pipeline which can be used as perceptual layer for more complex robotics applications.

    pdf code slides video icub visually recognizes everyday objects
    @article{fanello2017visual,
        title={Visual recognition for humanoid robots},
        author={Fanello, Sean Ryan and Ciliberto, Carlo and Noceti, Nicoletta and Metta, Giorgio and Odone, Francesca},
        journal={Robotics and Autonomous Systems},
        volume={91},
        pages={151--168},
        year={2017},
        publisher={Elsevier}
    }
                    

    machine learning, vision, robotics

  • Low Compute and Fully Parallel Computer Vision with HashMatch

    Sean Ryan Fanello, Julien Valentin, Adarsh Kowdle, Christoph Rhemann, Vladimir Tankovich, Carlo Ciliberto, Philip Davidson, Shahram Izadi ICCV 2017

    Numerous computer vision problems such as stereo depth estimation, object-class segmentation and foreground/ background segmentation can be formulated as per pixel image labeling tasks. Given one or many images as input, the desired output of these methods is usually a spatially smooth assignment of labels. The large amount of such computer vision problems has lead to significant research efforts, with the state of art moving from CRF-based approaches to deep CNNs and more recently, hybrids of the two. Although these approaches have significantly advanced the state of the art, the vast majority has solely focused on improving quantitative results and are not designed for low-compute scenarios. In this paper, we present a new general framework for a variety of computer vision labeling tasks, called HashMatch. Our approach is designed to be both fully parallel, i.e. each pixel is independently processed, and low-compute, with a model complexity an order of magnitude less than existing CNN and CRFbased approaches. We evaluate HashMatch extensively on several problems such as disparity estimation, image retrieval, feature approximation and background subtraction, for which HashMatch achieves high computational efficiency while producing high quality results.

    pdf code slides video icub visually recognizes everyday objects
    @inproceedings{fanello2017low,
      title={Low Compute and Fully Parallel Computer Vision with HashMatch},
      author={Fanello, Sean Ryan and Valentin, Julien and Kowdle, Adarsh and Rhemann, Christoph and Tankovich, Vladimir and Ciliberto, Carlo and Davidson, Philip and Izadi, Shahram},
      booktitle={2017 IEEE International Conference on Computer Vision (ICCV)},
      pages={3894--3903},
      year={2017},
      organization={IEEE}
    }
                    

    machine learning, vision, hashing, markov-random-fields, optimization

  • Incremental Robot Learning of New Objects with Fixed Update Time

    Raffaello Camoriano, Giulia Pasquale, Carlo Ciliberto, Lorenzo Natale, Lorenzo Rosasco, Giorgio Metta ICRA 2017

    We consider object recognition in the context of lifelong learning, where a robotic agent learns to discriminate between a growing number of object classes as it accumulates experience about the environment. We propose an incremental variant of the Regularized Least Squares for Classification (RLSC) algorithm, and exploit its structure to seamlessly add new classes to the learned model. The presented algorithm addresses the problem of having an unbalanced proportion of training examples per class, which occurs when new objects are presented to the system for the first time. We evaluate our algorithm on both a machine learning benchmark dataset and two challenging object recognition tasks in a robotic setting. Empirical evidence shows that our approach achieves comparable or higher classification performance than its batch counterpart when classes are unbalanced, while being significantly faster.

    pdf code slides video
    @inproceedings{camoriano2017incremental,
      title={Incremental robot learning of new objects with fixed update time},
      author={Camoriano, Raffaello and Pasquale, Giulia and Ciliberto, Carlo and Natale, Lorenzo and Rosasco, Lorenzo and Metta, Giorgio},
      booktitle={Robotics and Automation (ICRA), 2017 IEEE International Conference on},
      pages={3207--3214},
      year={2017},
      organization={IEEE}
    }
                    

    machine learning, vision, robotics

  • Connecting YARP to the Web with yarp.js

    Carlo Ciliberto Frontiers in Robotics and AI 2017

    We present yarp.js, a JavaScript framework enabling robotics networks to interface and interact with external devices by exploiting modern Web communication protocols. By connecting a YARP server module with a browser client on any external device, yarp.js allows to access on board sensors using standard Web APIs and stream the acquired data through the yarp.js network without the need for any installation. Communication between YARP modules and yarp.js clients is bi-directional, opening also the possibility for robotics applications to exploit the capabilities of modern browsers to process external data, such as speech synthesis, 3D data visualization, or video streaming to name a few. Yarp.js requires only a browser installed on the client device, allowing for fast and easy deployment of novel applications. The code and sample applications to get started with the proposed framework are available for the community at the yarp.js GitHub repository.

    pdf code slides video Example of Yarp js network: YARP network is connected via WebSockets with devices on which YARP has not been installed.
    @article{10.3389/frobt.2017.00067,
        author={Ciliberto, Carlo},    
        title={Connecting YARP to the Web with Yarp.js},      
        journal={Frontiers in Robotics and AI},            
        volume={4},      
        pages={67},     
        year={2017},      
        URL={https://www.frontiersin.org/article/10.3389/frobt.2017.00067},         
        DOI={10.3389/frobt.2017.00067},            
        ISSN={2296-9144},   
    }
    

    YARP, robotics

  • A Consistent Regularization Approach for Structured Prediction

    Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco NIPS 2016

    We propose and analyze a regularization approach for structured prediction problems. We characterize a large class of loss functions that allows to naturally embed structured outputs in a linear space. We exploit this fact to design learning algorithms using a surrogate loss approach and regularization techniques. We prove universal consistency and finite sample bounds characterizing the generalization properties of the proposed method. Experimental results are provided to demonstrate the practical usefulness of the proposed approach.

    pdf code slides video A Consistent Regularization Approach for Structured Prediction. First work proposing a structured prediction framework and algorithm for which it is possible to prove universal consistency and non asymptotic learning rates (generalization bounds)
    @inproceedings{ciliberto2016consistent,
      title={A Consistent Regularization Approach for Structured Prediction},
      author={Ciliberto, Carlo and Rosasco, Lorenzo and Rudi, Alessandro},
      booktitle={Advances in Neural Information Processing Systems},
      pages={4412--4420},
      year={2016}
    }
    

    machine learning, robotics, incremental learning

  • Combining sensory modalities and exploratory procedures to improve haptic object recognition in robotics

    Bertrand Higy, Carlo Ciliberto, Lorenzo Rosasco, Lorenzo Natale Humanoids 2016

    In this paper we tackle the problem of object recognition using haptic feedback from a robot holding and manipulating different objects. One of the main challenges in this setting is to understand the role of different sensory modalities (namely proprioception, object weight from F/T sensors and touch) and how to combine them to correctly discriminate different objects. We investigated these aspects by considering multiple sensory channels and different exploratory strategies to gather meaningful information regarding the object’s physical properties. We propose a novel strategy to train a learning machine able to efficiently combine sensory modalities by first learning individual object features and then combine them in a single classifier. To evaluate our approach and compare it with previous methods we collected a dataset for haptic object recognition, comprising 11 objects that were held in the hands of the iCub robot while performing different exploration strategies. Results show that our strategy consistently outperforms previous approaches.

    pdf code slides video
    @inproceedings{higy2016combining,
      title={Combining sensory modalities and exploratory procedures to improve haptic object recognition in robotics},
      author={Higy, Bertrand and Ciliberto, Carlo and Rosasco, Lorenzo and Natale, Lorenzo},
      booktitle={Humanoid Robots (Humanoids), 2016 IEEE-RAS 16th International Conference on},
      pages={117--124},
      year={2016},
      organization={IEEE}
    }
    

    machine learning, robotics, haptic recognition

  • Active perception: Building objects' models using tactile exploration

    Nawid Jamali, Carlo Ciliberto, Lorenzo Rosasco, Lorenzo Natale Humanoids 2016

    In this paper we present an efficient active learning strategy applied to the problem of tactile exploration of an object's surface. The method uses Gaussian process (GPs) classification to efficiently sample the surface of the object in order to reconstruct its shape. The proposed method iteratively samples the surface of the object, while, simultaneously constructing a probabilistic model of the object's surface. The probabilities in the model are used to guide the exploration. At each iteration, the estimate of the object's shape is used to slice the object in equally spaced intervals along the height of the object. The sampled locations are then labelled according to the interval in which their height falls. In its simple form, the data are labelled as belonging to the object and not belonging to the object: object and no-object, respectively. A GP classifier is trained to learn the object/no-object decision boundary. The next location to be sampled is selected at the classification boundary, in this way, the exploration is biased towards more informative areas. Complex features of the object's surface is captured by increasing the number of intervals as the number of sampled locations is increased. We validated our approach on six objects of different shapes using the iCub humanoid robot. Our experiments show that the method outperforms random selection and previous work based on GP regression by sampling more points on and near-the-boundary of the object.

    pdf code slides video
    @inproceedings{jamali2016active,
        title={Active perception: Building objects' models using tactile exploration},
        author={Jamali, Nawid and Ciliberto, Carlo and Rosasco, Lorenzo and Natale, Lorenzo},
        booktitle={Humanoid Robots (Humanoids), 2016 IEEE-RAS 16th International Conference on},
        pages={179--185},
        year={2016},
        organization={IEEE}
    }
    

    machine learning, robotics, haptic recognition, active learning

  • Object identification from few examples by improving the invariance of a deep convolutional neural network

    Giulia Pasquale, Carlo Ciliberto, Lorenzo Rosasco, Lorenzo Natale IROS 2016

    The development of reliable and robust visual recognition systems is a main challenge towards the deployment of autonomous robotic agents in unconstrained environments. Learning to recognize objects requires image representations that are discriminative to relevant information while being invariant to nuisances, such as scaling, rotations, light and background changes, and so forth. Deep Convolutional Neural Networks can learn such representations from large web-collected image datasets and a natural question is how these systems can be best adapted to the robotics context where little supervision is often available. In this work, we investigate different training strategies for deep architectures on a new dataset collected in a real-world robotic setting. In particular we show how deep networks can be tuned to improve invariance and discriminability properties and perform object identification tasks with minimal supervision.

    pdf code slides video
    @inproceedings{pasquale2016object,
        title={Object identification from few examples by improving the invariance of a deep convolutional neural network},
        author={Pasquale, Giulia and Ciliberto, Carlo and Rosasco, Lorenzo and Natale, Lorenzo},
        booktitle={Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on},
        pages={4904--4911},
        year={2016},
        organization={IEEE}
    }
    

    machine learning, robotics, object recognition, representation learning, deep learning

  • Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives

    Giulia Pasquale, Tanis Mar, Carlo Ciliberto, Lorenzo Rosasco, Lorenzo Natale Frontiers in Robotics and AI 2016

    Reliable depth perception eases and enables a large variety of attentional and interactive behaviors on humanoid robots. However, the use of depth in real-world scenarios is hindered by the difficulty of computing real-time and robust binocular disparity maps from moving stereo cameras. On the iCub humanoid robot, we recently adopted the Efficient Large-scale Stereo (ELAS) Matching algorithm (Geiger et al., 2010) for computation of the disparity map. In this technical report, we show that this algorithm allows reliable depth perception and experimental evidence that demonstrates that it can be used to solve challenging visual tasks in real-world indoor settings. As a case study, we consider the common situation where the robot is asked to focus the attention on one object close in the scene, showing how a simple but effective disparity-based segmentation solves the problem in this case. This example paves the way to a variety of other similar applications.

    pdf code slides video
    @article{10.3389/frobt.2016.00035,
        author={Pasquale, Giulia and Mar, Tanis and Ciliberto, Carlo and Rosasco, Lorenzo and Natale, Lorenzo},   
        title={Enabling Depth-Driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives},      
        journal={Frontiers in Robotics and AI},      
        volume={3},      
        pages={35},     
        year={2016},       
        URL={https://www.frontiersin.org/article/10.3389/frobt.2016.00035},       
        DOI={10.3389/frobt.2016.00035},        
        ISSN={2296-9144},   
    }
    

    computer vision, robotics, depth estimation

  • Convex Learning of Multiple Tasks and their Structure

    Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco ICML 2015

    Reducing the amount of human supervision is a key problem in machine learning and a natural approach is that of exploiting the relations (structure) among different tasks. This is the idea at the core of multi-task learning. In this context a fundamental question is how to incorporate the tasks structure in the learning problem.We tackle this question by studying a general computational framework that allows to encode a-priori knowledge of the tasks structure in the form of a convex penalty; in this setting a variety of previously proposed methods can be recovered as special cases, including linear and non-linear approaches. Within this framework, we show that tasks and their structure can be efficiently learned considering a convex optimization problem that can be approached by means of block coordinate methods such as alternating minimization and for which we prove convergence to the global minimum.

    pdf code slides video Unifying framework for multitask learning and their structure of relations.
    @inproceedings{ciliberto2015convex,
        title={Convex learning of multiple tasks and their structure},
        author={Ciliberto, Carlo and Mroueh, Youssef and Poggio, Tomaso and Rosasco, Lorenzo},
        booktitle={International Conference on Machine Learning},
        pages={1548--1557},
        year={2015}
    }
    

    machine learning, multitask learning, convex optimization, kernels, kernel methods

  • Learning Multiple Visual Tasks while Discovering their Structure

    Carlo Ciliberto, Lorenzo Rosasco, Silvia Villa CVPR 2015

    Multi-task learning is a natural approach for computer vision applications that require the simultaneous solution of several distinct but related problems, e.g. object detection, classification, tracking of multiple agents, or denoising, to name a few. The key idea is that exploring task relatedness (structure) can lead to improved performances. In this paper, we propose and study a novel sparse, non-parametric approach exploiting the theory of Reproducing Kernel Hilbert Spaces for vector-valued functions. We develop a suitable regularization framework which can be formulated as a convex optimization problem, and is provably solvable using an alternating minimization approach. Empirical tests show that the proposed method compares favorably to state of the art techniques and further allows to recover interpretable structures, a problem of interest in its own right.

    pdf code slides video
    @inproceedings{ciliberto2015learning,
      title={Learning multiple visual tasks while discovering their structure},
      author={Ciliberto, Carlo and Rosasco, Lorenzo and Villa, Silvia},
      booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
      pages={131--139},
      year={2015}
    }
    

    machine learning, multitask learning, convex optimization, kernels, kernel methods, computer vision, sparsity, structure learning

  • Characterizing the input-output function of the olfactory-limbic pathway in the guinea pig

    Gian Luca Breschi, Carlo Ciliberto, Thierry Nieus, Lorenzo Rosasco, Stefano Taverna, Michela Chiappalone, Valentina Pasquale Computational intelligence and neuroscience 2015

    Nowadays the neuroscientific community is taking more and more advantage of the continuous interaction between engineers and computational neuroscientists in order to develop neuroprostheses aimed at replacing damaged brain areas with artificial devices. To this end, a technological effort is required to develop neural network models which can be fed with the recorded electrophysiological patterns to yield the correct brain stimulation to recover the desired functions. In this paper we present a machine learning approach to derive the input-output function of the olfactory-limbic pathway in the in vitro whole brain of guinea pig, less complex and more controllable than an in vivo system. We first experimentally characterized the neuronal pathway by delivering different sets of electrical stimuli from the lateral olfactory tract (LOT) and by recording the corresponding responses in the lateral entorhinal cortex (l-ERC). As a second step, we used information theory to evaluate how much information output features carry about the input. Finally we used the acquired data to learn the LOT-l-ERC "I/O function," by means of the kernel regularized least squares method, able to predict l-ERC responses on the basis of LOT stimulation features. Our modeling approach can be further exploited for brain prostheses applications.

    pdf code slides video
    @article{breschi2015characterizing,
        title={Characterizing the input-output function of the olfactory-limbic pathway in the guinea pig},
        author={Breschi, Gian Luca and Ciliberto, Carlo and Nieus, Thierry and Rosasco, Lorenzo and Taverna, Stefano and Chiappalone, Michela and Pasquale, Valentina},
        journal={Computational intelligence and neuroscience},
        volume={2015},
        pages={60},
        year={2015},
        publisher={Hindawi Publishing Corp.}
    }
    

    machine learning, multitask learning, convex optimization, kernels, kernel methods, computer vision, sparsity, structure learning

  • Exploiting global force torque measurements for local compliance estimation in tactile arrays

    Carlo Ciliberto, Luca Fiorio, Marco Maggiali, Lorenzo Natale, Lorenzo Rosasco, Giorgio Metta, Giulio Sandini, Francesco Nori IROS 2014

    In this paper we tackle the problem of estimating the local compliance of tactile arrays exploiting global measurements from a single force and torque sensor. The proposed procedure exploits a transformation matrix (describing the relative position between the local tactile elements and the global force/torque measurements) to define a linear regression problem on the unknown local stiffness. Experiments have been conducted on the foot of the iCub robot, sensorized with a single force/torque sensor and a tactile array of 250 tactile elements (taxels) on the foot sole. Results show that a simple calibration procedure can be employed to estimate the stiffness parameters of virtual springs over a tactile array and to use these model to predict normal forces exerted on the array based only on the tactile feedback. Leveraging on previous works [1] the proposed procedure does not necessarily need a-priori information on the transformation matrix of the taxels which can be directly estimated from available measurements.

    pdf code slides video
    @inproceedings{ciliberto2014exploiting,
        title={Exploiting global force torque measurements for local compliance estimation in tactile arrays},
        author={Ciliberto, Carlo and Fiorio, Luca and Maggiali, Marco and Natale, Lorenzo and Rosasco, Lorenzo and Metta, Giorgio and Sandini, Giulio and Nori, Francesco},
        booktitle={Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on},
        pages={3994--3999},
        year={2014},
        organization={IEEE}
    }
    

    machine learning, robotics, artificial skin, calibration, estimation

  • Ask the image: supervised pooling to preserve feature locality

    Sean Ryan Fanello, Nicoletta Noceti, Carlo Ciliberto, Giorgio Metta, Francesca Odone CVPR 2014

    In this paper we propose a weighted supervised pooling method for visual recognition systems. We combine a standard Spatial Pyramid Representation which is commonly adopted to encode spatial information, with an appropriate Feature Space Representation favoring semantic information in an appropriate feature space. For the latter, we propose a weighted pooling strategy exploiting data supervision to weigh each local descriptor coherently with its likelihood to belong to a given object class. The two representations are then combined adaptively with Multiple Kernel Learning. Experiments on common benchmarks (Caltech256 and PASCAL VOC-2007) show that our image representation improves the current visual recognition pipeline and it is competitive with similar state-of-art pooling methods. We also evaluate our method on a real Human-Robot Interaction setting, where the pure Spatial Pyramid Representation does not provide sufficient discriminative power, obtaining a remarkable improvement.

    pdf code slides video
    @inproceedings{fanello2014,
        title={Ask the image: supervised pooling to preserve feature locality},
        author={Fanello, Sean Ryan and Noceti, Nicoletta and Ciliberto, Carlo and Metta, Giorgio and Odone, Francesca},
        booktitle={Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on},
        year={2014},
        organization={IEEE}
    }
    

    machine learning, computer vision, object recognition

  • On the impact of learning hierarchical representations for visual recognition in robotics

    Carlo Ciliberto, Sean Ryan Fanello, Matteo Santoro, Lorenzo Natale, Giorgio Metta, Lorenzo Rosasco IROS 2013

    Recent developments in learning sophisticated, hierarchical image representations have led to remarkable progress in the context of visual recognition. While these methods are becoming standard in modern computer vision systems, they are rarely adopted in robotics. The question arises of whether solutions, which have been primarily developed for image retrieval, can perform well in more dynamic and unstructured scenarios. In this paper we tackle this question performing an extensive evaluation of state of the art methods for visual recognition on a iCub robot. We consider the problem of classifying 15 different objects shown by a human demonstrator in a challenging Human-Robot Interaction scenario. The classification performance of hierarchical learning approaches are shown to outperform benchmark solutions based on local descriptors and template matching. Our results show that hierarchical learning systems are computationally efficient and can be used for real-time training and recognition of objects.

    pdf code slides video
    @inproceedings{ciliberto2013impact,
      title={On the impact of learning hierarchical representations for visual recognition in robotics},
      author={Ciliberto, Carlo and Fanello, Sean Ryan and Santoro, Matteo and Natale, Lorenzo and Metta, Giorgio and Rosasco, Lorenzo},
      booktitle={Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on},
      pages={3759--3764},
      year={2013},
      organization={IEEE}
    }
    

    machine learning, computer vision, object recognition, representation learning

  • Weakly supervised strategies for natural object recognition in robotics

    Sean Ryan Fanello, Carlo Ciliberto, Lorenzo Natale, Giorgio Metta ICRA 2013

    The paper aims at building a computer vision system for automatic image labeling in robotics scenarios. We show that the weak supervision provided by a human demonstrator, through the exploitation of the independent motion, enables a realistic Human-Robot Interaction (HRI) and achieves an automatic image labeling. We start by reviewing the underlying principles of our previous method for egomotion compensation [1], then we extend our approach removing the dependency on a known kinematics in order to provide a general method for a wide range of devices. From sparse salient features we predict the egomotion of the camera through a heteroscedastic learning method. Subsequently we use an object recognition framework for testing the automatic image labeling process: we rely on the State of the Art method from Yang et al. [2], employing local features augmented through a sparse coding stage and classified with linear SVMs. The application has been implemented and validated on the iCub humanoid robot and experiments are presented to show the effectiveness of the proposed approach. The contribution of the paper is twofold: first we overcome the dependency on the kinematics in the independent motion detection method, secondly we present a practical application for automatic image labeling through a natural HRI.

    pdf code slides video
    @inproceedings{fanello2013weakly,
      title={Weakly supervised strategies for natural object recognition in robotics},
      author={Fanello, Sean Ryan and Ciliberto, Carlo and Natale, Lorenzo and Metta, Giorgio},
      booktitle={Robotics and Automation (ICRA), 2013 IEEE International Conference on},
      pages={4223--4229},
      year={2013},
      organization={IEEE}
    }
    

    machine learning, computer vision, object recognition, independent motion detection

  • A Heteroscedastic Approach to Independent Motion Detection for Actuated Visual Sensors

    Carlo Ciliberto, Sean Ryan Fanello, Lorenzo Natale, Giorgio Metta IROS 2012

    We present an original method for independent motion detection in dynamic scenes. The algorithm is designed for robotics real-time applications and it overcomes the shortcomings of current approaches for the egomotion estimation in presence of many outliers, occlusions and cluttered background. The method relies on a stereo system which performs the reprojection of a sparse set of features following the camera displacement. We assume that noisy prior knowledge of the motion is available (i.e. a robot’s kinematic model). Since this estimation leads to a heteroscedastic regression problem due to input-dependent noise, we employ a simple, but computationally efficient approach in order to accurately determine the latent egomotion subspace spanned by the Degrees of Freedom (DOFs) of the robot. The algorithm has been implemented and validated on the iCub humanoid robot. Qualitative and quantitative experiments are presented to show the effectiveness of the proposed approach. The contribution of the paper is a modular framework for independent motion detection naturally extendable to any architecture featuring a visual sensor that can be directly controllable.

    pdf code slides video
    @inproceedings{ciliberto2012heteroscedastic,
      title={A heteroscedastic approach to independent motion detection for actuated visual sensors},
      author={Ciliberto, Carlo and Fanello, Sean Ryan and Natale, Lorenzo and Metta, Giorgio},
      booktitle={Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on},
      pages={3907--3913},
      year={2012},
      organization={IEEE}
    }
    

    machine learning, computer vision, object recognition, independent motion detection

  • Online multiple instance learning applied to hand detection in a humanoid robot

    Carlo Ciliberto, Fabrizio Smeraldi, Lorenzo Natale, Giorgio Metta IROS 2011

    Algorithms with limited supervision requirements are particularly useful for intelligent autonomous agents, that must be able to learn with minimal supervision and adapt online to a changing environment. We propose an online Multiple Instance Learning algorithm based on boosting and apply it to the problem of visual detection of the hand of a humanoid robot. Our approach solves the multiple instance problem at the level of the weak learners, allowing the detection of objects represented by more than one positive instance. Feature selection and adaptation are performed online as new data are fed to the algorithm. Experiments in real-world conditions on an iCub humanoid robot confirm that the algorithm can learn the appearance of the hand, reaching an accuracy comparable with off-line algorithms. This remains true when supervision is generated by the robot itself in a completely autonomous fashion. To the best of our knowledge, this is the first implementation of online multiple-instance learning on a robotic platform.

    pdf code slides video
    @inproceedings{ciliberto2011online,
      title={Online multiple instance learning applied to hand detection in a humanoid robot},
      author={Ciliberto, Carlo and Smeraldi, Fabrizio and Natale, Lorenzo and Metta, Giorgio},
      booktitle={Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on},
      pages={1526--1532},
      year={2011},
      organization={IEEE}
    }
    

    machine learning, computer vision, object recognition, independent motion detection

  • Reexamining lucas-kanade method for real-time independent motion detection: Application to the icub humanoid robot

    Carlo Ciliberto, Ugo Pattacini, Lorenzo Natale, Francesco Nori, Giorgio Metta IROS 2011

    Visual motion is a simple yet powerful cue widely used by biological systems to improve their perception and adaptation to the environment. Examples of tasks that greatly benefit from the ability to detect movement are object segmentation, 3D scene reconstruction and control of attention. In computer vision several algorithms for computing visual motion and optic flow exists. However their application in robotics is not straightforward as in these platforms visual motion is often dominated by (self) motion produced by the movement of the robot (egomotion) making it difficult to disambiguate between motion induced by the scene dynamics or by the own actions of the robot. Independent motion detection is an active field in computer vision and robotics, however approaches in this area typically require that some models of both the environment and the robot visual system are available and are hardly suitable for real-time control. In this paper we describe the motionCUT, a derivation of the Lucas-Kanade optical flow algorithm that allows detecting moving objects, irrespectively of the egomotion produced by the robot. Our method is purely visual and does not require information other than the images coming from the cameras. As such it can be easily adapted to any robotic platform. The system was tested on a stereo tracking task on the iCub humanoid robot, demonstrating that the algorithm performs well and can easily execute in real-time.

    pdf code slides video
    @inproceedings{ciliberto2011reexamining,
      title={Reexamining lucas-kanade method for real-time independent motion detection: Application to the icub humanoid robot},
      author={Ciliberto, Carlo and Pattacini, Ugo and Natale, Lorenzo and Nori, Francesco and Metta, Giorgio},
      booktitle={Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on},
      pages={4154--4160},
      year={2011},
      organization={IEEE}
    }
    

    machine learning, computer vision, object recognition, independent motion detection


Carlo Ciliberto
c.ciliberto@imperial.ac.uk
 
Imperial College London,
Exhibition Road, London, SW7 2BT, UK
United Kingdom

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