Fast Damage Recovery in Robotics with the T-Resilience Algorithm

Damage recovery is critical for autonomous robots that need to operate for a long time without assistance. Most current methods are complex and costly because they require anticipating each potential damage in order to have a contingency plan ready. As an alternative, we introduce the T-resilience algorithm, a new algorithm that allows robots to quickly and autonomously discover compensatory behaviors in unanticipated situations. This algorithm equips the robot with a self-model and discovers new behaviors by learning to avoid those that perform differently in the self-model and in reality. Our algorithm thus does not identify the damaged parts but it implicitly searches for efficient behaviors that do not use them. We evaluate the T-Resilience algorithm on a hexapod robot that needs to adapt to leg removal, broken legs and motor failures; we compare it to stochastic local search, policy gradient and the self-modeling algorithm proposed by Bongard et al. The behavior of the robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using only 25 tests on the robot and an overall running time of 20 minutes, T-Resilience consistently leads to substantially better results than the other approaches

Crossing the reality gap in evolutionary robotics by promoting transferable controllers

International audienceThe reality gap, that often makes controllers evolved in simulation inefficient once transferred onto the real system, remains a critical issue in Evolutionary Robotics (ER); it prevents ER application to real-world problems. We hypothesize that this gap mainly stems from a conflict between the efficiency of the solutions in simulation and their transferability from simulation to reality: best solutions in simulation often rely on bad simulated phenomena (e.g. the most dynamic ones). This hypothesis leads to a multi-objective formulation of ER in which two main objectives are optimized via a Pareto-based Multi-Objective Evolutionary Algorithm: (1) the fitness and (2) the transferability. To evaluate this second objective, a simulation-to-reality disparity value is approximated for each controller. The proposed method is applied to the evolution of walking controllers for a real 8-DOF quadrupedal robot. It successfully finds effi- cient and well-transferable controllers with only a few experiments in reality

40 GBd 16QAM signaling at 160 Gb/s in a silicon-organic hybrid modulator

We demonstrate for the first time generation of 16-state quadrature amplitude modulation (16QAM) signals at a symbol rate of 40 GBd using silicon-based modulators. Our devices exploit silicon-organic hybrid (SOH) integration, which combines silicon-on-insulator slot waveguides with electro-optic cladding materials to realize highly efficient phase shifters. The devices enable 16QAM signaling and quadrature phase shift keying (QPSK) at symbol rates of 40 GBd and 45 GBd, respectively, leading to line rates of up to 160 Gbit/s on a single wavelength and in a single polarization. This is the highest value demonstrated by a silicon-based device up to now. The energy consumption for 16QAM signaling amounts to less than 120 fJ/bit – one order of magnitude below that of conventional silicon photonic 16QAM modulators

L'approche par transférabilité : une réponse aux problèmes de passage à la réalité, de généralisation et d'adaptation

The design of controllers for robots that have to deal with unknown or poorly controlled environments is a difficult engineering problem. Evolutionary robotics tackles this challenge by developing automatic methods to design controllers that are based on black-box optimization processes using evolutionary algorithms. In this context, the performance values are estimated for each controller either directly on the robot or with a simulation. Assessing performance with the real robot and in all the possible situations is usually not consistent with the evaluation budgets required by these optimization methods. We here propose a general approach, which combines a multi-objective optimization process in a fixed simulation model, a surrogate model and a few experiments on the robot. This transferability approach can be applied to any optimization process conducted in a simplified environment (simulation) for a targeted full environment (robot). It looks for controllers that maximize two objectives: the performance in simulation and a transferability objective, which reflects how well the behavior observed in simulation matches the behavior on the robot. The second objective is estimated by a surrogate model built by performing a few transfer experiments on the robot while optimizing controllers. The approach is applied on three open problems from evolutionary robotics: the reality gap problem, the optimization of controllers with generalization abilities and the adaptation of a robot to its environment.Il est difficile de concevoir des contrôleurs pour des robots devant fonctionner dans des environnements peu maîtrisés voire inconnus. Dans cette optique, la robotique évolutionniste cherche à élaborer des méthodes de conception automatique de contrôleurs via un processus d'optimisation ''boîte noire'' utilisant des algorithmes évolutionnistes. Les valeurs de performance d'un contrôleur donné sont alors estimées soit directement sur le robot, soit à l'aide d'une simulation, par essence simplificatrice. Partant du constat qu'évaluer sur le robot et dans toutes ses situations d'utilisation est généralement incompatible avec le nombre d'évaluations requis par de tels processus d'optimisation, nous proposons une approche générale combinant un processus d'optimisation multi-objectif dans un simulateur fixe, un modèle de substitution et quelques tests sur le robot. Cette approche par transférabilité peut s'appliquer à tout processus d'optimisation mené dans un environnement simplifié (simulation) pour un environnement complet ciblé (robot) et recherche les contrôleurs qui maximisent deux objectifs : la performance dans l'environnement simplifié et un objectif de transférabilité qui indique à quel point le comportement dans l'environnement simplifié est proche de celui dans l'environnement complet. Ce deuxième objectif est estimé par un modèle de substitution construit en effectuant quelques expériences de transfert sur le robot pendant l'optimisation. L'approche est validée sur trois problèmes de robotique évolutionniste : le passage de la simulation à la réalité, l'optimisation de contrôleurs dotés de capacités de généralisation et l'adaptation d'un robot à son environnement

ONLINE DISCOVERY OF LOCOMOTION MODES FOR WHEEL-LEGGED HYBRID ROBOTS: A TRANSFERABILITY-BASED APPROACH

International audienceWheel-legged hybrid robots promise to combine the e ciency of wheeled robots with the versatility of legged robots: they are able to roll on simple terrains, to dynamically adapt their posture and even to walk on uneven grounds. Al- though di erent locomotion modes of such robots have been studied, a pivotal question remains: how to automatically adapt the locomotion mode when the environment changes? We here propose that the robot autonomously discov- ers its locomotion mode using optimization-based learning. To that aim, we introduce a new algorithm that relies on a forward model and a stochastic multi-objective optimization. Three objectives are optimized: (1) the average displacement speed, (2) the expended energy and (3) the transferability score, which re ects how well the behavior of the robot is in agreement with the pre- dictions of the forward model. This transferability function is approximated by conducting 20 experiments of one second on the real robot during the op- timization. In the three investigated situations ( at ground, grass-like terrain, tunnel-like environment), our method found e cient controllers for forward locomotion in 1 to 2 minutes: the robot used its wheels on the at ground, it walked on the grass-like terrain and moved with a lowered body in the tunnel- like environment

Automatic

system identification based on coevolution of models and test

L' approche par transférabilité (une réponse aux problèmes de passage à la réalité, de généralisation et d'adaptation)

Il est difficile de concevoir des contrôleurs pour des robots devant fonctionner dans des environnements peu maîtrisés voire inconnus. Dans cette optique, la robotique évolutionniste cherche à élaborer des méthodes de conception automatique de contrôleurs via un processus d'optimisation boîte noire'' utilisant des algorithmes évolutionnistes. Les valeurs de performance d'un contrôleur donné sont alors estimées soit directement sur le robot, soit à l'aide d'une simulation, par essence simplificatrice. Partant du constat qu'évaluer sur le robot et dans toutes ses situations d'utilisation est généralement incompatible avec le nombre d'évaluations requis par de tels processus d'optimisation, nous proposons une approche générale combinant un processus d'optimisation multi-objectif dans un simulateur fixe, un modèle de substitution et quelques tests sur le robot. Cette approche par transférabilité peut s'appliquer à tout processus d'optimisation mené dans un environnement simplifié (simulation) pour un environnement complet ciblé (robot) et recherche les contrôleurs qui maximisent deux objectifs : la performance dans l'environnement simplifié et un objectif de transférabilité qui indique à quel point le comportement dans l'environnement simplifié est proche de celui dans l'environnement complet. Ce deuxième objectif est estimé par un modèle de substitution construit en effectuant quelques expériences de transfert sur le robot pendant l'optimisation. L'approche est validée sur trois problèmes de robotique évolutionniste : le passage de la simulation à la réalité, l'optimisation de contrôleurs dotés de capacités de généralisation et l'adaptation d'un robot à son environnement.PARIS-BIUSJ-Mathématiques rech (751052111) / SudocSudocFranceF

Automatic system identification based on coevolution of models and tests

International audienceIn evolutionary robotics, controllers are often designed in simulation, then transferred onto the real system. Nevertheless, when no accurate model is available, controller transfer from simulation to reality means potential performance loss. It is the reality gap problem. Unmanned aerial vehicles are typical systems where it may arise. Their locomotion dynamics may be hard to model because of a limited knowledge about the underlying physics. Moreover, a batch identification approach is difficult to use due to costly and time consuming experiments. An automatic identification method is then needed that builds a relevant local model of the system concerning a target issue. This paper deals with such an approach that is based on coevolution of models and tests. It aims at improving both modeling and control of a given system with a limited number of manipulations carried out on it. Experiments conducted with a simulated quadrotor helicopter show promising initial results about test learning and control improvement