Statistical and Computational Trade-Offs in Kernel K-Means

We investigate the efficiency of k-means in terms of both statistical and computational requirements. More precisely, we study a Nystrom approach to kernel k-means. We analyze the statistical properties of the proposed method and show that it achieves the same accuracy of exact kernel k-means with only a fraction of computations. Indeed, we prove under basic assumptions that sampling oot pn Nystrom landmarks allows to greatly reduce computational costs without incurring in any loss of accuracy. To the best of our knowledge this is the first result of this kind for unsupervised learning

Physically Interactive Robogames: Definition and Design Guidelines

There is evidence that people expects to be able to play games with autonomous robots, so that robogames could be one of the next killer ap- plications for Robotics. Physically Interactive RoboGames (PIRG) is a new application field where autonomous robots are involved in games requiring physical interaction with people. Since research in this field is moving its first steps, definitions and design guidelines are still largely missing. n this paper, a definition for PIRG is proposed, together with guidelines for their design. Physically Interactive, Competitive RoboGames (PICoRG) are also introduced. They are a particular kind of PIRG where human players are involved in a challenging, highly interactive and competitive game activity with autonomous robots. The development process of a PICoRG, Jedi Trainer , is presented to show a practical application of the proposed guidelines. The game has been successfully played in different unstructured environments, by general public; feedback is reported and analysed

On Fast Leverage Score Sampling and Optimal Learning

Leverage score sampling provides an appealing way to perform approximate computations for large matrices. Indeed, it allows to derive faithful approximations with a complexity adapted to the problem at hand. Yet, performing leverage scores sampling is a challenge in its own right requiring further approximations. In this paper, we study the problem of leverage score sampling for positive definite matrices defined by a kernel. Our contribution is twofold. First we provide a novel algorithm for leverage score sampling and second, we exploit the proposed method in statistical learning by deriving a novel solver for kernel ridge regression. Our main technical contribution is showing that the proposed algorithms are currently the most efficient and accurate for these problems

Constrained DMPs for Feasible Skill Learning on Humanoid Robots

In the context of humanoid skill learning, movement primitives have gained much attention because of their compact representation and convenient combination with a myriad of optimization approaches. Among them, a well-known scheme is to use Dynamic Movement Primitives (DMPs) with reinforcement learning (RL) algorithms. While various remarkable results have been reported, skill learning with physical constraints has not been sufficiently investigated. For example, when RL is employed to optimize the robot joint trajectories, the exploration noise could drive the resulting trajectory out of the joint limits. In this paper, we focus on robot skill learning characterized by joint limit avoidance, by introducing the novel Constrained Dynamic Movement Primitives (CDMPs). By controlling a set of transformed states (called exogenous states) instead of the original DMPs states, CDMPs are capable of maintaining the joint trajectories within the safety limits. We validate CDMPs on the humanoid robot iCub, showing the applicability of our approach

Learning to Sequence Multiple Tasks with Competing Constraints

Imitation learning offers a general framework where robots can efficiently acquire novel motor skills from demonstrations of a human teacher. While many promising achievements have been shown, the majority of them are only focused on single-stroke movements, without taking into account the problem of multi-tasks sequencing. Conceivably, sequencing different atomic tasks can further augment the robot's capabilities as well as avoid repetitive demonstrations. In this paper, we propose to address the issue of multi-tasks sequencing with emphasis on handling the so-called competing constraints, which emerge due to the existence of the concurrent constraints from Cartesian and joint trajectories. Specifically, we explore the null space of the robot from an information-theoretic perspective in order to maintain imitation fidelity during transition between consecutive tasks. The effectiveness of the proposed method is validated through simulated and real experiments on the iCub humanoid robot

Learning to Avoid Obstacles With Minimal Intervention Control

Programming by demonstration has received much attention as it offers a general framework which allows robots to efficiently acquire novel motor skills from a human teacher. While traditional imitation learning that only focuses on either Cartesian or joint space might become inappropriate in situations where both spaces are equally important (e.g., writing or striking task), hybrid imitation learning of skills in both Cartesian and joint spaces simultaneously has been studied recently. However, an important issue which often arises in dynamical or unstructured environments is overlooked, namely how can a robot avoid obstacles? In this paper, we aim to address the problem of avoiding obstacles in the context of hybrid imitation learning. Specifically, we propose to tackle three subproblems: (i) designing a proper potential field so as to bypass obstacles, (ii) guaranteeing joint limits are respected when adjusting trajectories in the process of avoiding obstacles, and (iii) determining proper control commands for robots such that potential human-robot interaction is safe. By solving the aforementioned subproblems, the robot is capable of generalizing observed skills to new situations featuring obstacles in a feasible and safe manner. The effectiveness of the proposed method is validated through a toy example as well as a real transportation experiment on the iCub humanoid robot

Pattern electroretinogram detects localized glaucoma defects

Purpose: We evaluated the clinical ability of pattern electroretinogram (PERG) to detect functional losses in the affected hemifield of open-angle glaucoma patients with localized perimetric defects. Methods: Hemifield (horizontally-defined) steady-state PERGs (h-PERGs) were recorded in response to 1.7 c/deg alternating gratings from 32 eyes of 29 glaucomatous patients with a perimetric, focal one-hemifield defect, 10 eyes of 10 glaucomatous patients with a diffuse perimetric defect, and 18 eyes of 18 age-matched normal subjects. Standard automated perimetry (SAP) and spectral-domain optical coherence tomography (SD-OCT) for retinal nerve fiber layer (RNFL) thickness also were performed. h-PERG amplitudes and ratios, calculated corresponding hemifield perimetric deviations, as well as hemiretina RNFL thicknesses were analyzed. Results: h-PERG amplitudes, perimetric deviations, and RNFL thicknesses showed losses (P < 0.001) when comparing affected with unaffected hemifields of localized glaucomatous eyes. No differences were found in h-PERG amplitudes between hemifields of normal or diffuse glaucomatous eyes. h-PERG amplitude ratios (affected/ unaffected hemifield) in localized glaucoma were lower (P < 0.001) than the ratios from normal or diffuse glaucomatous eyes. The areas under the receiver operating characteristic curves for h-PERG amplitude ratios, comparing localized-defect glaucomatous eyes with normal or diffuse glaucomatous eyes, were 0.93 and 0.91, respectively. Conclusions: h-PERG assessment showed good diagnostic accuracy to confirm localized glaucomatous defects detected perimetrically. This test may be particularly useful in cognitively impaired patients or young/nonverbal patients unable to provide reliable visual fields. Translational Relevance: h-PERG provides a sensitive objective measure to confirm

Quantitative computed tomography predicts outcomes in idiopathic pulmonary fibrosis

First published: 25 July 2022Background and objective: Prediction of disease course in patients with progressive pulmonary fibrosis remains challenging. The purpose of this study was to assess the prognostic value of lung fibrosis extent quantified at computed tomography (CT) using data-driven texture analysis (DTA) in a large cohort of well-characterized patients with idiopathic pulmonary fibrosis (IPF) enrolled in a national registry. Methods: This retrospective analysis included participants in the Australian IPF Registry with available CT between 2007 and 2016. CT scans were analysed using the DTA method to quantify the extent of lung fibrosis. Demographics, longitudinal pulmonary function and quantitative CT metrics were compared using descriptive statistics. Linear mixed models, and Cox analyses adjusted for age, gender, BMI, smoking history and treatment with anti-fibrotics were performed to assess the relationships between baseline DTA, pulmonary function metrics and outcomes. Results: CT scans of 393 participants were analysed, 221 of which had available pulmonary function testing obtained within 90 days of CT. Linear mixed-effect modelling showed that baseline DTA score was significantly associated with annual rate of decline in forced vital capacity and diffusing capacity of carbon monoxide. In multivariable Cox proportional hazard models, greater extent of lung fibrosis was associated with poorer transplant-free survival (hazard ratio [HR] 1.20, p < 0.0001) and progression-free survival (HR 1.14, p < 0.0001). Conclusion: In a multi-centre observational registry of patients with IPF, the extent of fibrotic abnormality on baseline CT quantified using DTA is associated with outcomes independent of pulmonary function.Stephen M. Humphries, John A. Mackintosh, Helen E. Jo, Simon L. F. Walsh, Mario Silva, Lucio Calandriello, Sally Chapman, Samantha Ellis, Ian Glaspole, Nicole Goh, Christopher Grainge, Peter M. A. Hopkins, Gregory J. Keir, Yuben Moodley, Paul N. Reynolds, E. Haydn Walters, David Baraghoshi, Athol U. Wells, David A. Lynch, Tamera J. Cort