Journals 2021

In January 2021, the Hellenic Institute of Advanced Study (HIAS) assembled a panel including world leading roboticists from the Hellenic diaspora, who volunteered their scientific expertise to provide a vision for Robotics in Greece. This monograph, entitled “Robotics in the Artificial Intelligence (AI) era,” will hopefully trigger a dialogue towards the development of a national robotics strategy. Our vision is that Robotics in the AI era will be an essential technology of the future for the safety and security of the Hellenic nation, its environment and its citizens, for modernizing its economy towards Industry 4.0, and for inspiring and educating the next generation workforce for the challenges of the 21st century. To contribute towards making this vision a reality, after reviewing global trends in robotics and assessing the Greek robotics ecosystem, we arrived at the following key findings and recommendations: Firstly, we think that Greece should develop a national Hellenic Robotics Initiative that serves as the nation’s long-term vision and strategy across the entire Greek robotics ecosystem. Also, certain societal drivers should be key in the areas of focus. Safety and security is an area of national importance necessitating a national initiative, while agrifood, maritime and logistics provide opportunities for internationally leading innovation.

We recommend the establishment of a mission-driven, government-funded, organization advancing unmanned vehicles in societal drivers of national importance, and Greece should leverage its unique geography and become a living testbed of robotics innovation turning the country into a development site for exportable technologies. In our opinion, universities should create Centers of Excellence in robotics and AI as well as consider innovation-leading research institutes such as the Italian Institute of Technology. We recommend investing in robotics education using Maker Spaces in order to prepare the workforce with 21st century skills to become Industry 4.0 innovators. Furthermore, we believe that the government should collect, measure, and analyze data on the robotics industry, robotics uses, labor shifts, and brain gain and promote awareness via a Hellenic Robotics Day. And finally, the government should regulate robot safety without stifling innovation, provide safe experimentation areas and mechanisms for certifying safety of locally developed robots. This monograph has many additional suggestions that enhance the above main recommendations. As authors, we advocate bringing the robotics ecosystem together in order to sharpen and expand these findings to an ambitious, long-term, and detailed national strategy and roadmap for robotics in the AI era.

Artificial bacteria flagella (ABFs) are magnetic helical microswimmers that can be remotely controlled via a uniform, rotating magnetic field. Previous studies have used the heterogeneous response of microswimmers to external magnetic fields for achieving independent control. Herein, analytical and reinforcement learning control strategies for path planning to a target by multiple swimmers using a uniform magnetic field are introduced. The comparison of the two algorithms shows the superiority of reinforcement learning in achieving minimal travel time to a target. The results demonstrate, for the first time, the effective independent navigation of realistic microswimmers with a uniform magnetic field in a viscous flow field.

Efficient point-to-point navigation in the presence of a background flow field is important for robotic applications such as ocean surveying. In such applications, robots may only have knowledge of their immediate surroundings or be faced with time-varying currents, which limits the use of optimal control techniques. Here, we apply a recently introduced Reinforcement Learning algorithm to discover time-efficient navigation policies to steer a fixed- speed swimmer through unsteady two-dimensional flow fields. The algorithm entails input- ting environmental cues into a deep neural network that determines the swimmer’s actions, and deploying Remember and Forget Experience Replay. We find that the resulting swimmers successfully exploit the background flow to reach the target, but that this success depends on the sensed environmental cue. Surprisingly, a velocity sensing approach significantly out- performed a bio-mimetic vorticity sensing approach, and achieved a near 100% success rate in reaching the target locations while approaching the time-efficiency of optimal navigation trajectories. 

 

We present Korali, an open-source framework for large-scale Bayesian uncertainty quantification and stochastic optimization. The framework relies on non-intrusive sampling of complex multiphysics models and enables their exploitation for optimization and decision-making. In addition, its distributed sampling engine makes efficient use of massively-parallel architectures while introducing novel fault tolerance and load balancing mechanisms. We demonstrate these features by interfacing Korali with existing high-performance software such as APHROS, LAMMPS (CPU-based), and MIRHEO (GPU-based) and show efficient scaling for up to 512 nodes of the CSCS Piz Daint supercomputer. Finally, we present benchmarks demonstrating that Korali outperforms related state-of-the-art software frameworks.

The tunable polarity of water can be exploited in emerging technologies including catalysis, gas storage, and green chemistry. Recent experimental and theoretical studies have shown that water can be rendered into an effectively apolar solvent under nanoconfinement. We furthermore demonstrate, through molecular simulations, that the static dielectric constant of water can be modified by changing the wettability of the confining material. We find the out-of-plane dielectric response to be highly sensitive to the level of confinement and can be reduced up to 40× , in accordance with experimental data. By altering the surface wettability from superhydrophilic to super- hydrophobic, we observe a 36% increase for the out-of-plane and a 31% decrease for the in-plane dielectric constants. Our findings demonstrate the feasibility of tunable water polarity, a phenomenon with great potential for scientific and technological impact.

The extreme liquid transport properties of carbon nanotubes present new opportunities for surpassing conventional technologies in water filtration and purification. We demonstrate that carbon nanotubes with wettability surface patterns act as nanopumps for the ultrafast transport of picoliter water droplets without requiring externally imposed pressure gradients. Large-scale molecular dynamics simulations evidence unprecedented speeds and accelerations on the order of 10¹⁰ g of droplet propulsion caused by interfacial energy gradients. This phenomenon is persistent for nanotubes of varying sizes, stepwise pattern configurations, and initial conditions. We present a scaling law for water transport as a function of wettability gradients through simple models for the droplet dynamic contact angle and friction coefficient. Our results show that patterned nanotubes are energy-efficient nanopumps offering a realistic path toward ultrafast water nanofiltration and precision drug delivery.

Swimming organisms can escape their predators by creating and harnessing unsteady flow fields through their body motions. Stochastic optimization and flow simulations have identified escape patterns that are consistent with those observed in natural larval swimmers. However, these patterns have been limited by the specification of a particular cost function and depend on a prescribed functional form of the body motion. Here, we deploy reinforcement learning to discover swimmer escape patterns for larval fish under energy constraints. The identified patterns include the C-start mechanism, in addition to more energetically efficient escapes. We find that maximizing distance with limited energy requires swimming via short bursts of accelerating motion interlinked with phases of gliding. The present, data efficient, reinforcement learning algorithm results in an array of patterns that reveal practical flow optimization principles for efficient swimming and the methodology can be transferred to the control of aquatic robotic devices operating under energy constraints.

Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a Bayesian uncertainty quantification framework to infer model parameters for a disease spreading on a network of communities from limited, noisy observations; the state-of-the-art computational framework compensates for the model complexity by exploiting massively parallel computing architectures. Using noisy, synthetic data, we show the potential of the approach to perform robust model fitting and additionally demonstrate that we can effectively identify the disease origin via Bayesian model selection. As disease-related data are increasingly available, the proposed framework has broad practical relevance for the prediction and management of epidemics.

We present high-fidelity numerical simulations of expiratory biosol transport during normal breathing under indoor, stagnant air conditions with and without a facile mask. We investigate mask efficacy to suppress the spread of saliva particles that is underpinnings existing social distancing recommendations. The present simulations incorporate the effect of human anatomy and consider a spectrum of saliva particulate sizes that range from 0.1 to 10 μm while also accounting for their evaporation. The simulations elucidate the vorticity dynamics of human breathing and show that without a facile mask, saliva particulates could travel over 2.2 m away from the person. However, a non-medical grade face mask can drastically reduce saliva particulate propagation to 0.72 m away from the person. This study provides new quantitative evidence that facile masks can successfully suppress the spreading of saliva particulates due to normal breathing in indoor environments.

Simulations of blood flows in microfluidic devices and physiological systems are gaining importance in complementing experimental and clinical studies. The predictive capabilities of these simulations hinge on the parameters of the red blood cell (RBC) model that are usually calibrated from experimental data. However, these parameter values may vary drastically when calibrated using different experimental quan- tities or experimental settings. In turn, the results of existing blood flow simulations largely depend on the utilized parameters that have been chosen to validate a particular experiment. We suggest a revision to this type of model calibration to properly integrate experimental data in the computational models and accordingly inform their predictions. In this context, we introduce the calibration of a popular RBC model using data-driven, hierarchical Bayesian inference. We employ data from classical experiments of RBC stretching by optical tweezers and tank treading in shear flows, and distinguish the calibration of the model parameters through single-level and hierarchical Bayesian uncertainty quantification. We find that the optimal model parameters depend not only on the data used for the inference but also on the way the data are used in the inference process. Single-level Bayesian models predict well the data used in their calibration, but are inferior to the hierarchical Bayesian model at predicting previously unseen data. This work demonstrates that the proper integration of experimental data is essential for the development of a robust and transferable RBC model. We believe that the present study can serve as a prototype across scientific fields, in revising the integration of computational models and heterogeneous experimental data.

Turbulent flow models are critical for applications such as aircraft design, weather forecasting and climate prediction. Existing
models are largely based on physical insight and engineering intuition. More recently, machine learning has been contributing
to this endeavour with promising results. However, all efforts have focused on supervised learning, which is difficult to generalize beyond training data. Here we introduce multi-agent reinforcement learning as an automated discovery tool of turbulence
models. We demonstrate the potential of this approach on large-eddy simulations of isotropic turbulence, using the recovery of
statistical properties of direct numerical simulations as a reward. The closure model is a control policy enacted by cooperating
agents, which detect critical spatio-temporal patterns in the flow field to estimate the unresolved subgrid-scale physics. Results
obtained with multi-agent reinforcement learning algorithms based on experience replay compare favourably with established
modelling approaches. Moreover, we show that the learned turbulence models generalize across grid sizes and flow conditions.