MIRO: A robot “Mammal” with a biomimetic brain-based control system

We describe the design of a novel commercial biomimetic brain-based robot, MIRO, developed as a prototype robot companion. The MIRO robot is animal-like in several aspects of its appearance, however, it is also biomimetic in a more significant way, in that its control architecture mimics some of the key principles underlying the design of the mammalian brain as revealed by neuroscience. Specifically, MIRO builds on decades of previous work in developing robots with brain-based control systems using a layered control architecture alongside centralized mechanisms for integration and action selection. MIRO’s control system operates across three core processors, P1-P3, that mimic aspects of spinal cord, brainstem, and forebrain functionality respectively. Whilst designed as a versatile prototype for next generation companion robots, MIRO also provides developers and researchers with a new platform for investigating the potential advantages of brain-based control

Private 5G and its Suitability for Industrial Networking

5G was and is still surrounded by many promises and buzzwords, such as the famous 1 ms, real-time, and Ultra-Reliable and Low-Latency Communications (URLLC). This was partly intended to get the attention of vertical industries to become new customers for mobile networks, which shall be deployed in their factories. With the allowance of federal agencies, companies deployed their own private 5G networks to test new use cases enabled by 5G. But what has been missing, apart from all the marketing, is the knowledge of what 5G can really do? Private 5G networks are envisioned to enable new use cases with strict latency requirements, such as robot control. This work has examined in great detail the capabilities of the current 5G Release 15 as private network, and in particular its suitability with regard to time-critical communications. For that, a testbed was designed to measure One-Way Delays (OWDs) and Round-Trip Times (RTTs) with high accuracy. The measurements were conducted in 5G Non-Standalone (NSA) and Standalone (SA) net-works and are the first published results. The evaluation revealed results that were not obvious or identified by previous work. For example, a strong impact of the packet rate on the resulting OWD and RTT was found. It was also found that typically 95% of the SA downlink end-to-end packet delays are in the range of 4 ms to 10 ms, indicating a fairly wide spread of packet delays, with the Inter-Packet Delay Variation (IPDV) between consecutive packets distributed in the millisecond range. Surprisingly, it also seems to matter for the RTT from which direction, i.e. Downlink (DL) or Uplink (UL), a round-trip communication was initiated. Another important factor plays especially the Inter-Arrival Time (IAT) of packets on the RTT distribution. These examples from the results found demonstrate the need to critically examine 5G and any successors in terms of their real-time capabilities. In addition to the end-to-end OWD and RTT, the delays caused by 4G and 5G Core processing has been investigated as well. Current state-of-the-art 4G and 5G Core implementations exhibit long-tailed delay distributions. To overcome such limitations, modern packet processing have been evaluated in terms of their respective tail-latency. The hardware-based solution was able to process packets with deterministic delay, but the software-based solutions also achieved soft real-time results. These results allow the selection of the right technology for use cases depending on their tail-latency requirements. In summary, many insights into the suitability of 5G for time-critical communications were gained from the study of the current 5G Release 15. The measurement framework, analysis methods, and results will inform the further development and refinement of private 5G campus networks for industrial use cases

Functional network analyses and dynamical modeling of proprioceptive updating of the body schema

Proprioception is an ability to perceive the position and speed of body parts that is important for construction of the body schema in the brain. Proper updating of the body schema is necessary for appropriate voluntary movement. However, the mechanisms mediating such an updating are not well understood. To study these mechanisms when the body part was at rest, electroencephalography (EEG) and evoked potentials studies were employed, and when the body was in motion, kinematic studies were performed. An experimental approach to elicit proprioceptive P300 evoked potentials was developed providing evidence that processing of novel passive movements is similar to processing of novel visual and auditory stimuli. The latencies of the proprioceptive P300 potentials were found to be greater than those elicited by auditory, but not different from those elicited by the visual stimuli. The features of the functional networks that generated the P300s were analyzed for each modality. Cross-correlation networks showed both common features, e.g. connections between frontal and parietal areas, and the stimulus-specific features, e.g. increases of the connectivity for temporal electrodes in the visual and auditory networks, but not in the proprioceptive ones. The magnitude of coherency networks showed a reduction in alpha band connectivity for most of the electrodes groupings for all stimuli modalities, but did not demonstrate modality-specific features. Kinematic study compared performances of 19 models previously proposed in the literature for movements at the shoulder and elbow joints in terms of their ability to reconstruct the speed profiles of the wrist pointing movements. It was found that lognormal and beta function models are most suitable for wrist speed profile modeling. In addition, an investigation of the blinking rates during the P300 potentials recordings revealed significantly lower rates in left-handed participants, compared to the right-handed ones. Future work will include expanding the experimental and analytical methodologies to different kinds of proprioceptive stimuli (displacements and speeds) and experimental paradigms (error-related negativity potentials), and comparing the models of the speed profiles produced by the feet to those of the wrists, as well as replicating the observations made on the blinking rates in a larger scale study

ED-BioRob: A Neuromorphic Robotic Arm With FPGA-Based Infrastructure for Bio-Inspired Spiking Motor Controllers

Compared to classic robotics, biological nervous systems respond to stimuli in a fast and efficient way regarding the body motor actions. Decision making, once the sensory information arrives to the brain, is in the order of ms, while the whole process from sensing to movement requires tens of ms. Classic robotic systems usually require complex computational abilities. Key differences between biological systems and robotic machines lie in the way information is coded and transmitted. A neuron is the "basic" element that constitutes biological nervous systems. Neurons communicate in an event-driven way through small currents or ionic pulses (spikes). When neurons are arranged in networks, they allow not only for the processing of sensory information, but also for the actuation over the muscles in the same spiking manner. This paper presents the application of a classic motor control model (proportional-integral-derivative) developed with the biological spike processing principle, including the motor actuation with time enlarged spikes instead of the classic pulse-width-modulation. This closed-loop control model, called spike-based PID controller (sPID), was improved and adapted for a dual FPGA-based system to control the four joints of a bioinspired light robot (BioRob X5), called event-driven BioRob (ED-BioRob). The use of spiking signals allowed the system to achieve a current consumption bellow 1A for the entire 4 DoF working at the same time. Furthermore, the robot joints commands can be received from a population of silicon-neurons running on the Dynap-SE platform. Thus, our proposal aims to bridge the gap between a general purpose processing analog neuromorphic hardware and the spiking actuation of a robotic platform

Improving efficiency and security of IIoT communications using in-network validation of server certificate

The use of advanced communications and smart mechanisms in industry is growing rapidly, making cybersecurity a critical aspect. Currently, most industrial communication protocols rely on the Transport Layer Security (TLS) protocol to build their secure version, providing confidentiality, integrity and authentication. In the case of UDP-based communications, frequently used in Industrial Internet of Things (IIoT) scenarios, the counterpart of TLS is Datagram Transport Layer Security (DTLS), which includes some mechanisms to deal with the high unreliability of the transport layer. However, the (D)TLS handshake is a heavy process, specially for resource-deprived IIoT devices and frequently, security is sacrificed in favour of performance. More specifically, the validation of digital certificates is an expensive process from the time and resource consumption point of view. For this reason, digital certificates are not always properly validated by IIoT devices, including the verification of their revocation status; and when it is done, it introduces an important delay in the communications. In this context, this paper presents the design and implementation of an in-network server certificate validation system that offloads this task from the constrained IIoT devices to a resource-richer network element, leveraging data plane programming (DPP). This approach enhances security as it guarantees that a comprehensive server certificate verification is always performed. Additionally, it increases performance as resource-expensive tasks are moved from IIoT devices to a resource-richer network element. Results show that the proposed solution reduces DTLS handshake times by 50–60 %. Furthermore, CPU use in IIoT devices is also reduced, resulting in an energy saving of about 40 % in such devices.This work was financially supported by the Spanish Ministry of Science and Innovation through the TRUE-5G project PID2019-108713RB-C54/AEI/10.13039/501100011033. It was also partially supported by the Ayudas Cervera para Centros Tecnológicos grant of the Spanish Centre for the Development of Industrial Technology (CDTI) under the project EGIDA (CER-20191012), and by the Basque Country Government under the ELKARTEK Program, project REMEDY - Real tiME control and embeddeD securitY (KK-2021/00091)

Designing Distributed, Component-Based Systems for Industrial Robotic Applications

none3noneM. Amoretti; S. Caselli; M. ReggianiM., Amoretti; S., Caselli; Reggiani, Monic

Robot Learning and Control Using Error-Related Cognitive Brain Signals

Durante los últimos años, el campo de los interfaces cerebro-máquina (BMIs en inglés) ha demostrado cómo humanos y animales son capaces de controlar dispositivos neuroprotésicos directamente de la modulación voluntaria de sus señales cerebrales, tanto en aproximaciones invasivas como no invasivas. Todos estos BMIs comparten un paradigma común, donde el usuario trasmite información relacionada con el control de la neuroprótesis. Esta información se recoge de la actividad cerebral del usuario, para luego ser traducida en comandos de control para el dispositivo. Cuando el dispositivo recibe y ejecuta la orden, el usuario recibe una retroalimentación del rendimiento del sistema, cerrando de esta manera el bucle entre usuario y dispositivo. La mayoría de los BMIs decodifican parámetros de control de áreas corticales para generar la secuencia de movimientos para la neuroprótesis. Esta aproximación simula al control motor típico, dado que enlaza la actividad neural con el comportamiento o la ejecución motora. La ejecución motora, sin embargo, es el resultado de la actividad combinada del córtex cerebral, áreas subcorticales y la médula espinal. De hecho, numerosos movimientos complejos, desde la manipulación a andar, se tratan principalmente al nivel de la médula espinal, mientras que las áreas corticales simplemente proveen el punto del espacio a alcanzar y el momento de inicio del movimiento. Esta tesis propone un paradigma BMI alternativo que trata de emular el rol de los niveles subcorticales durante el control motor. El paradigma se basa en señales cerebrales que transportan información cognitiva asociada con procesos de toma de decisiones en movimientos orientados a un objetivo, y cuya implementación de bajo nivel se maneja en niveles subcorticales. A lo largo de la tesis, se presenta el primer paso hacia el desarrollo de este paradigma centrándose en una señal cognitiva específica relacionada con el procesamiento de errores humano: los potenciales de error (ErrPs) medibles mediante electroencefalograma (EEG). En esta propuesta de paradigma, la neuroprótesis ejecuta activamente una tarea de alcance mientras el usuario simplemente monitoriza el rendimiento del dispositivo mediante la evaluación de la calidad de las acciones ejecutadas por el dispositivo. Estas evaluaciones se traducen (gracias a los ErrPs) en retroalimentación para el dispositivo, el cual las usa en un contexto de aprendizaje por refuerzo para mejorar su comportamiento. Esta tesis demuestra por primera vez este paradigma BMI de enseñanza con doce sujetos en tres experimentos en bucle cerrado concluyendo con la operación de un manipulador robótico real. Como la mayoría de BMIs, el paradigma propuesto requiere una etapa de calibración específica para cada sujeto y tarea. Esta fase, un proceso que requiere mucho tiempo y extenuante para el usuario, dificulta la distribución de los BMIs a aplicaciones fuera del laboratorio. En el caso particular del paradigma propuesto, una fase de calibración para cada tarea es altamente impráctico ya que el tiempo necesario para esta fase se suma al tiempo de aprendizaje de la tarea, retrasando sustancialmente el control final del dispositivo. Así, sería conveniente poder entrenar clasificadores capaces de funcionar independientemente de la tarea de aprendizaje que se esté ejecutando. Esta tesis analiza desde un punto de vista electrofisiológico cómo los potenciales se ven afectados por diferentes tareas ejecutadas por el dispositivo, mostrando cambios principalmente en la latencia la señal; y estudia cómo transferir el clasificador entre tareas de dos maneras: primero, aplicando clasificadores adaptativos del estado del arte, y segundo corrigiendo la latencia entre las señales de dos tareas para poder generalizar entre ambas. Otro reto importante bajo este paradigma viene del tiempo necesario para aprender la tarea. Debido al bajo ratio de información transferida por minuto del BMI, el sistema tiene una pobre escalabilidad: el tiempo de aprendizaje crece exponencialmente con el tamaño del espacio de aprendizaje, y por tanto resulta impráctico obtener el comportamiento motor óptimo mediante aprendizaje por refuerzo. Sin embargo, este problema puede resolverse explotando la estructura de la tarea de aprendizaje. Por ejemplo, si el número de posiciones a alcanzar es discreto se puede pre-calcular la política óptima para cada posible posición. En esta tesis, se muestra cómo se puede usar la estructura de la tarea dentro del paradigma propuesto para reducir enormemente el tiempo de aprendizaje de la tarea (de diez minutos a apenas medio minuto), mejorando enormemente así la escalabilidad del sistema. Finalmente, esta tesis muestra cómo, gracias a las lecciones aprendidas en los descubrimientos anteriores, es posible eliminar completamente la etapa de calibración del paradigma propuesto mediante el aprendizaje no supervisado del clasificador al mismo tiempo que se está ejecutando la tarea. La idea fundamental es calcular un conjunto de clasificadores que sigan las restricciones de la tarea anteriormente usadas, para a continuación seleccionar el mejor clasificador del conjunto. De esta manera, esta tesis presenta un BMI plug-and-play que sigue el paradigma propuesto, aprende la tarea y el clasificador y finalmente alcanza la posición del espacio deseada por el usuario