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

COLECCIÓN JOSEFINA DE LA TORRE MILLARES [Material gráfico]

FORMA PARTE DE LA COLECCIÓN FOTOGRÁFICA DE JOSEFINA DE LA TORRE, CUYOS ARTEFACTOS FOTO-QUÍMICOS ESTÁN CUSTODIADOS EN LA CASA MUSEO DE PÉREZ GALDÓS.Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

Diagnostic accuracy of a clinical diagnosis of idiopathic pulmonary fibrosis: An international case-cohort study

We conducted an international study of idiopathic pulmonary fibrosis (IPF) diagnosis among a large group of physicians and compared their diagnostic performance to a panel of IPF experts. A total of 1141 respiratory physicians and 34 IPF experts participated. Participants evaluated 60 cases of interstitial lung disease (ILD) without interdisciplinary consultation. Diagnostic agreement was measured using the weighted kappa coefficient (\u3baw). Prognostic discrimination between IPF and other ILDs was used to validate diagnostic accuracy for first-choice diagnoses of IPF and were compared using the Cindex. A total of 404 physicians completed the study. Agreement for IPF diagnosis was higher among expert physicians (\u3baw=0.65, IQR 0.53-0.72, p20 years of experience (C-index=0.72, IQR 0.0-0.73, p=0.229) and non-university hospital physicians with more than 20 years of experience, attending weekly MDT meetings (C-index=0.72, IQR 0.70-0.72, p=0.052), did not differ significantly (p=0.229 and p=0.052 respectively) from the expert panel (C-index=0.74 IQR 0.72-0.75). Experienced respiratory physicians at university-based institutions diagnose IPF with similar prognostic accuracy to IPF experts. Regular MDT meeting attendance improves the prognostic accuracy of experienced non-university practitioners to levels achieved by IPF experts

On the emergence of whole-body strategies from humanoid robot push-recovery learning

none7Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks. In this context, classical control systems tend to be based on simplified physical models and hard-coded strategies. Although successful in specific scenarios, this approach requires demanding tuning of parameters and switching logic between specifically-designed controllers for handling more general perturbations. We apply model-free Deep Reinforcement Learning for training a general and robust humanoid push-recovery policy in a simulation environment. Our method targets high-dimensional whole-body humanoid control and is validated on the iCub humanoid. Reward components incorporating expert knowledge on humanoid control enable fast learning of several robust behaviors by the same policy, spanning the entire body. We validate our method with extensive quantitative analyses in simulation, including out-of-sample tasks which demonstrate policy robustness and generalization, both key requirements towards real-world robot deployment.openFerigo D.; Camoriano R.; Viceconte P.M.; Calandriello D.; Traversaro S.; Rosasco L.; Pucci D.Ferigo, D.; Camoriano, R.; Viceconte, P. M.; Calandriello, D.; Traversaro, S.; Rosasco, L.; Pucci, D

Impianto XiVe secondo la metodica Flap Less. Case report

Impianto XiVe secondo la metodica Flap Less. Case repor