Implementation of Memory Centric Scheduling for COTS Multi-Core Real-Time Systems

The demands for high performance computing with a low cost and low power consumption are driving a transition towards multi-core processors in many consumer and industrial applications. However, the adoption of multi-core processors in the domain of real-time systems faces a series of challenges that has been the focus of great research intensity during the last decade. These challenges arise in great part from the non real-time nature of the hardware arbiters that schedule the access to shared resources, such as the main memory. One solution proposed in the literature is called Memory Centric Scheduling, which defines a separate software scheduler for the sections of the tasks that will access the main memory, hence circumventing the low level unpredictable hardware arbiters. Several Memory Centric schedulers and associated theoretical analyses have been proposed, but as far as we know, no actual implementation of the required OS-level underpinnings to support dynamic event-driven Memory Centric Scheduling has been presented before. In this paper we aim to fill this gap, targeting cache based COTS multi-core systems. We will confirm via measurements the main theoretical benefits of Memory Centric Scheduling (e.g. task isolation). Furthermore, we will describe an effective schedulability analysis using concepts from distributed systems

Humanoid odometric localization integrating kinematic, inertial and visual information

We present a method for odometric localization of humanoid robots using standard sensing equipment, i.e., a monocular camera, an inertial measurement unit (IMU), joint encoders and foot pressure sensors. Data from all these sources are integrated using the prediction-correction paradigm of the Extended Kalman Filter. Position and orientation of the torso, defined as the representative body of the robot, are predicted through kinematic computations based on joint encoder readings; an asynchronous mechanism triggered by the pressure sensors is used to update the placement of the support foot. The correction step of the filter uses as measurements the torso orientation, provided by the IMU, and the head pose, reconstructed by a VSLAM algorithm. The proposed method is validated on the humanoid NAO through two sets of experiments: open-loop motions aimed at assessing the accuracy of localization with respect to a ground truth, and closed-loop motions where the humanoid pose estimates are used in real-time as feedback signals for trajectory control

Imitation Learning-based Visual Servoing for Tracking Moving Objects

In everyday life collaboration tasks between human operators and robots, the former necessitate simple ways for programming new skills, the latter have to show adaptive capabilities to cope with environmental changes. The joint use of visual servoing and imitation learning allows us to pursue the objective of realizing friendly robotic interfaces that (i) are able to adapt to the environment thanks to the use of visual perception and (ii) avoid explicit programming thanks to the emulation of previous demonstrations. This work aims to exploit imitation learning for the visual servoing paradigm to address the specific problem of tracking moving objects. In particular, we show that it is possible to infer from data the compensation term required for realizing the tracking controller, avoiding the explicit implementation of estimators or observers. The effectiveness of the proposed method has been validated through simulations with a robotic manipulator.Comment: International Workshop on Human-Friendly Robotics (HFR), 202

Self-Supervised Prediction of the Intention to Interact with a Service Robot

A service robot can provide a smoother interaction experience if it has the ability to proactively detect whether a nearby user intends to interact, in order to adapt its behavior e.g. by explicitly showing that it is available to provide a service. In this work, we propose a learning-based approach to predict the probability that a human user will interact with a robot before the interaction actually begins; the approach is self-supervised because after each encounter with a human, the robot can automatically label it depending on whether it resulted in an interaction or not. We explore different classification approaches, using different sets of features considering the pose and the motion of the user. We validate and deploy the approach in three scenarios. The first collects $3442$ natural sequences (both interacting and non-interacting) representing employees in an office break area: a real-world, challenging setting, where we consider a coffee machine in place of a service robot. The other two scenarios represent researchers interacting with service robots ($200$ and $72$ sequences, respectively). Results show that, even in challenging real-world settings, our approach can learn without external supervision, and can achieve accurate classification (i.e. AUROC greater than $0.9$) of the user's intention to interact with an advance of more than $3$s before the interaction actually occurs.Comment: Paper under revision for Robotics and Autonomous Systems journa

Changes in emergency psychiatric consultations in time of COVID-19: a retrospective observational study in the Verona Academic Hospital over the two pandemic years 2020-2021

Background: During the first months of the COVID-19 pandemic, local health authorities in most Italian regions prescribed a reduction of ordinary outpatient and community mental health care. The aim of this study was to assess the impact of the COVID-19 pandemic on access to the emergency departments (ED) for psychiatric consultation in the pandemic years 2020 and 2021 compared to 2019. Methods: This is a retrospective study conducted by using routinely collected administrative data of the two EDs of the Verona Academic Hospital Trust (Verona, Italy). All ED psychiatry consultations registered from 01.01.2020 to 31.12.2021 were compared with those registered in the pre-pandemic year (01.01.2019 to 31.12.2019). The association between each recorded characteristic and the year considered was estimated by chi-square or Fisher's exact test. Results: A significant reduction was observed between 2020 and 2019 (-23.3%) and between 2021 and 2019 (-16.3%). This reduction was most evident in the lockdown period of 2020 (-40.3%) and in the phase corresponding to the second and third pandemic waves (-36.1%). In 2021, young adults and people with diagnosis of psychosis showed an increase in requests for psychiatric consultation. Conclusions: Fear of contagion may have been an important factor in the overall reduction in psychiatric consultations. However, psychiatric consultations for people with psychosis and for young adults increased. This finding underlines the need for mental health services to implement alternative outreach strategies aimed to support, in times of crisis, these vulnerable segments of the population