We present an approach for generating facial animation that combines video and audio input data in real time for low-end devices through deep learning. Our method produces control signals from audiovisual inputs separately, and mixes them to animate a character rig. The architecture relies on two specialized networks that are trained on a combination of synthetic and real world data and are highly engineered to be efficient in order to support quality avatar faces even on low-end devices. In addition, the system supports several levels of detail that degrade gracefully for additional scaling and efficiency. We showcase how user testing has been employed to improve performance and a comparison with state of the art.

The convergence of sensor-based vehicle automation and Inter-Vehicle Communication (IVC) will be a key to achieve the full automation of vehicles. In this paper we present a new method for the design and performance evaluation of Cooperative Automated Driving (CAD) systems, based on a bidirectional coupling of vehicle and network simulators (Webots and ns-3). The coupling exploits the comprehensive capabilities of the simulators at a reasonable computational complexity and allows simulating CAD systems with high accuracy. We demonstrate the capabilities of the simulation tool by a case study of convoy driving with automated vehicles using a fully distributed control algorithm and IVC. The study compares CAD-specific metrics (safety distance, headway, speed) for an ideal and a realistic communication channel. The simulation results underline the need of accurate modeling and give valuable insights for the design of CAD systems.

The Ranger robot was designed to interact with children in order to motivate them to tidy up their room. Its mechanical configuration, together with the limited field of view of its depth camera, make the learning of obstacle avoidance behaviors a hard problem. In this article we introduce two new Particle Swarm Optimization (PSO) algorithms designed to address this noisy, high-dimensional optimization problem. Their aim is to increase the robustness of the generated robotic controllers, as compared to previous PSO algorithms. We show that we can successfully apply this set of PSO algorithms to learn 166 parameters of a robotic controller for the obstacle avoidance task. We also study the impact that an increased evaluation budget has on the robustness and average performance of the optimized controllers. Finally, we validate the control solutions learned in simulation by testing the most robust controller in three different real arenas.

This paper presents an overtaking decision algorithm for networked intelligent vehicles. The algorithm is based on a cooperative tracking and sensor fusion algorithm that we previously developed. The ego vehicle is equipped with lane keeping and lane changing capabilities, as well as a forward-looking lidar sensor. The lidar data are fed to the tracking module which detects other vehicles, such as the vehicle that is to be overtaken (leading) and the oncoming traffic. Based on the estimated distances to the leading and the oncoming vehicles and their speeds, a risk is calculated and a corresponding overtaking decision is made. We compare the performance of the overtaking algorithm between the case when the ego vehicle only relies on its lidar sensor, and the case in which it fuses object estimates received from the leading car which also has a forward-looking lidar. Systematic evaluations are performed in Webots, a calibrated high-fidelity simulator.

Particle Swarm Optimization (PSO) is a meta-heuristic for solving high dimensional optimization problems. Due to the large number of dimensions usually employed with PSO, it is not trivial to visualize and monitor the progress of the algorithm. Because of this, adjusting the parameters that govern the dynamics of the swarm for a specific problem becomes challenging. In this article, we present SwarmViz, an open-source visualization tool for PSO. Through SwarmViz, users are able to set up PSO experiments on canonical benchmark functions or input data from external experiments (e.g., learning robotic controllers), and to visualize the optimization process with state-of-the-art visualization tools. SwarmViz has two main goals. First, to enable researchers to monitor the progress of their specific optimization problem and adjust the relevant PSO parameters. Second, to give a visual insight about PSO to students in the scope of teaching optimization techniques. We demonstrate the features of the software through examples on well-known numerical benchmark functions and a case study on the optimization of a robotic controller.

Particle Swarm Optimization (PSO) is a population-based metaheuristic that can be applied to optimize controllers for multiple robots using only local information. In order to cope with noise in the robotic performance evaluations, different reevaluation strategies were proposed in the past. In this article, we apply a statistical technique called Optimal Computing Budget Allocation to improve the performance of distributed PSO in the presence of noise. In particular, we compare a distributed PSO OCBA algorithm suitable for resource-constrained mobile robots with a centralized version that uses global information for the allocation. We show that the distributed PSO OCBA outperforms a previous distributed noise-resistant PSO variant, and that the performance of the distributed PSO OCBA approaches that of the centralized one as the communication radius is increased. We also explore different parametrizations of the PSO OCBA algorithm, and show that the choice of parameter values differs from previous guidelines proposed for stand-alone OCBA.

Population-based learning techniques have been proven to be effective in dealing with noise in numerical benchmark functions and are thus promising tools for the high-dimensional optimization of controllers for multiple robots with limited sensing capabilities, which have inherently noisy performance evaluations. In this article, we apply a statistical technique called Optimal Computing Budget Allocation to improve the performance of Particle Swarm Optimization in the presence of noise for a multi-robot obstacle avoidance benchmark task. We present a new distributed PSO OCBA algorithm suitable for resource-constrained mobile robots due to its low requirements in terms of memory and limited local communication. Our results from simulation show that PSO OCBA outperforms other techniques for dealing with noise, achieving a more consistent progress and a better estimate of the ground-truth performance of candidate solutions. We then validate our simulations with real robot experiments where we compare the controller learned with our proposed algorithm to a potential field controller for obstacle avoidance in a cluttered environment. We show that they both achieve a high performance through different avoidance behaviors.

In this article we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots, as a canonical example of cooperative robotic behavior. We use five distributed noise-resistant variations of Particle Swarm Optimization (PSO) to learn in simulation a set of 50 weights of an artificial neural network. They differ on the way the particles are allocated and evaluated on the robots, and on how the PSO neighborhood is implemented. In addition, we use a centralized approach that allows for benchmarking with the distributed versions. Regardless of the learning approach, each robot measures locally and individually the performance of the group using exclusively on-board resources. Results show that four of the distributed variations obtain similar fitnesses as the centralized version, and are always able to learn. The other distributed variation fails to properly learn on some of the runs, and results in lower fitness when it succeeds. We test systematically the controllers learned in simulation in real robot experiments.

Population-based learning techniques have been proven to be effective in dealing with noise and are thus promising tools for the optimization of robotic controllers, which have inherently noisy performance evaluations. This article discusses how the results and guidelines derived from tests on benchmark functions can be extended to the fitness distributions encountered in robotic learning. We show that the large-amplitude noise found in robotic evaluations is disruptive to the initial phases of the learning process of PSO. Under these conditions, neither increasing the population size nor increasing the number of iterations are efficient strategies to improve the performance of the learning. We also show that PSO is more sensitive to good spurious evaluations of bad solutions than bad evaluations of good solutions, i.e., there is a non-symmetric effect of noise on the performance of the learning.

The ability to move in complex environments is a fundamental requirement for robots to be a part of our daily lives. Increasing the controller complexity may be a desirable choice in order to obtain an improved performance. However, these two aspects may pose a considerable challenge on the optimization of robotic controllers. In this paper, we study the trade-offs between the complexity of reactive controllers and the complexity of the environment in the optimization of multi-robot obstacle avoidance for resource-constrained platforms. The optimization is carried out in simulation using a distributed, noise-resistant implementation of Particle Swarm Optimization, and the resulting controllers are evaluated both in simulation and with real robots. We show that in a simple environment, linear controllers with only two parameters perform similarly to more complex non-linear controllers with up to twenty parameters, even though the latter ones require more evaluation time to be learned. In a more complicated environment, we show that there is an increase in performance when the controllers can differentiate between front and backwards sensors, but increasing further the number of sensors and adding non-linear activation functions provide no further benefit. In both environments, augmenting reactive control laws with simple memory capabilities causes the highest increase in performance. We also show that in the complex environment the performance measurements are noisier, the optimal parameter region is smaller, and more iterations are required for the optimization process to converge.

Division of labor is a widely studied aspect of colony behavior of social insects. Division of labor models indicate how individuals distribute themselves in order to perform different tasks simultaneously. However, models that study division of labor from a dynamical system point of view cannot be found in the literature. In this paper, we define a division of labor model as a discrete-time dynamical system, in order to study the equilibrium points and their properties related to convergence and stability. By making use of this analytical model, an adaptive algorithm based on division of labor can be designed to satisfy dynamic criteria. In this way, we have designed and tested an algorithm that varies the response thresholds in order to modify the dynamic behavior of the system. This behavior modification allows the system to adapt to specific environmental and collective situations, making the algorithm a good candidate for distributed control applications. The variable threshold algorithm is based on specialization mechanisms. It is able to achieve an asymptotically stable behavior of the system in different environments and independently of the number of individuals. The algorithm has been successfully tested under several initial conditions and number of individuals.