Andrea Cherubini

Bio: Andrea Cherubini is an academic researcher from University of Montpellier. The author has contributed to research in topics: Robot & Visual servoing. The author has an hindex of 22, co-authored 102 publications receiving 2003 citations. Previous affiliations of Andrea Cherubini include Sapienza University of Rome & French Institute for Research in Computer Science and Automation.

Collaborative manufacturing with physical human–robot interaction

Abstract: Although the concept of industrial cobots dates back to 1999, most present day hybrid human-machine assembly systems are merely weight compensators. Here, we present results on the development of a collaborative human-robot manufacturing cell for homokinetic joint assembly. The robot alternates active and passive behaviours during assembly, to lighten the burden on the operator in the first case, and to comply to his/her needs in the latter. Our approach can successfully manage direct physical contact between robot and human, and between robot and environment. Furthermore, it can be applied to standard position (and not torque) controlled robots, common in the industry. The approach is validated in a series of assembly experiments. The human workload is reduced, diminishing the risk of strain injuries. Besides, a complete risk analysis indicates that the proposed setup is compatible with the safety standards, and could be certified.

Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics.

Abstract: In this review, we give a brief outline of robot-mediated gait training for stroke patients, as an important emerging field in rehabilitation. Technological innovations are allowing rehabilitation to move toward more integrated processes, with improved efficiency and less long-term impairments. In particular, robot-mediated neurorehabilitation is a rapidly advancing field, which uses robotic systems to define new methods for treating neurological injuries, especially stroke. The use of robots in gait training can enhance rehabilitation, but it needs to be used according to well-defined neuroscientific principles. The field of robot-mediated neurorehabilitation brings challenges to both bioengineering and clinical practice. This article reviews the state of the art (including commercially available systems) and perspectives of robotics in poststroke rehabilitation for walking recovery. A critical revision, including the problems at stake regarding robotic clinical use, is also presented.

Collaborative Human-Humanoid Carrying Using Vision and Haptic Sensing

Abstract: We propose a framework for combining vision and haptic information in human-robot joint actions. It consists of a hybrid controller that uses both visual servoing and impedance controllers. This can be applied to tasks that cannot be done with vision or haptic information alone. In this framework, the state of the task can be obtained from visual information while haptic information is crucial for safe physical interaction with the human partner. The approach is validated on the task of jointly carrying a flat surface (e.g. a table) and then preventing an object (e.g. a ball) on top from falling off. The results show that this task can be successfully achieved. Furthermore, the framework presented allows for a more collaborative setup, by imparting task knowledge to the robot as opposed to a passive follower.

The Three Laws of Neurorobotics: A Review on What Neurorehabilitation Robots Should Do for Patients and Clinicians.

Abstract: Most studies and reviews on robots for neurorehabilitation focus on their effectiveness. These studies often report inconsistent results. This and many other reasons limit the credit given to these robots by therapists and patients. Further, neurorehabilitation is often still based on therapists’ expertise, with competition among different schools of thought, generating substantial uncertainty about what exactly a neurorehabilitation robot should do. Little attention has been given to ethics. This review adopts a new approach, inspired by Asimov’s three laws of robotics and based on the most recent studies in neurorobotics, for proposing new guidelines for designing and using robots for neurorehabilitation. We propose three laws of neurorobotics based on the ethical need for safe and effective robots, the redefinition of their role as therapist helpers, and the need for clear and transparent human–machine interfaces. These laws may allow engineers and clinicians to work closely together on a new generation of neurorobots.

Brain Computer Interfaces, a Review

Abstract: A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces

Abstract: Brain computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional Neural Networks (CNNs), which have been used in computer vision and speech recognition, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible. In this work we introduce EEGNet, a compact convolutional network for EEG-based BCIs. We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI. We compare EEGNet to current state-of-the-art approaches across four BCI paradigms: P300 visual-evoked potentials, error-related negativity responses (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR). We show that EEGNet generalizes across paradigms better than the reference algorithms when only limited training data is available. We demonstrate three different approaches to visualize the contents of a trained EEGNet model to enable interpretation of the learned features. Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks, suggesting that the observed performances were not due to artifact or noise sources in the data.

L2 Gain And Passivity Techniques In Nonlinear Control

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