Exploring New Horizons in Evolutionary Design of Robots

International audienceThis introduction paper to the 2009 IROS workshop “Exploring new horizons in Evolutionary Design of Robots” considers the field of Evolutionary Robotics (ER) from the perspective of its potential users: roboticists. The core hypothesis motivating this field of research will be discussed, as well as the potential use of ER in a robot design process. Three main aspects of ER will be presented: (a) ER as an automatic parameter tuning procedure, which is the most mature application and is used to solve real robotics problem, (b) evolutionary-aided design, which may benefit the designer as an efficient tool to build robotic systems and (c) automatic synthesis, which corresponds to the automatic design of a mechatronic device. Critical issues will also be presented as well as current trends and pespectives in ER

Learning to Grasp: from Somewhere to Anywhere

Robotic grasping is still a partially solved, multidisciplinary problem where data-driven techniques play an increasing role. The sparse nature of rewards make the automatic generation of grasping datasets challenging, especially for unconventional morphologies or highly actuated end-effectors. Most approaches for obtaining large-scale datasets rely on numerous human-provided demonstrations or heavily engineered solutions that do not scale well. Recent advances in Quality-Diversity (QD) methods have investigated how to learn object grasping at a specific pose with different robot morphologies. The present work introduces a pipeline for adapting QD-generated trajectories to new object poses. Using an RGB-D data stream, the vision pipeline first detects the targeted object, predicts its 6-DOF pose, and finally tracks it. An automatically generated reach-and-grasp trajectory can then be adapted by projecting it relatively to the object frame. Hundreds of trajectories have been deployed into the real world on several objects and with different robotic setups: a Franka Research 3 with a parallel gripper and a UR5 with a dexterous SIH Schunk hand. The transfer ratio obtained when applying transformation to the object pose matches the one obtained when the object pose matches the simulation, demonstrating the efficiency of the proposed approach

Flapping-Wing Mechanism for a Bird-Sized UAVs: Design, Modeling and Control

International audienceBirds daily execute complex maneuvers out of reach of current UAVs of comparable size. These capabilities are at least partly linked to the efficient flapping kinematics. This article describes the flapping wing mechanism developed within the ROBUR project to create a bird-sized UAV relying on such advanced kinematics

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

Speeding up 6-DoF Grasp Sampling with Quality-Diversity

Recent advances in AI have led to significant results in robotic learning, including natural language-conditioned planning and efficient optimization of controllers using generative models. However, the interaction data remains the bottleneck for generalization. Getting data for grasping is a critical challenge, as this skill is required to complete many manipulation tasks. Quality-Diversity (QD) algorithms optimize a set of solutions to get diverse, high-performing solutions to a given problem. This paper investigates how QD can be combined with priors to speed up the generation of diverse grasps poses in simulation compared to standard 6-DoF grasp sampling schemes. Experiments conducted on 4 grippers with 2-to-5 fingers on standard objects show that QD outperforms commonly used methods by a large margin. Further experiments show that QD optimization automatically finds some efficient priors that are usually hard coded. The deployment of generated grasps on a 2-finger gripper and an Allegro hand shows that the diversity produced maintains sim-to-real transferability. We believe these results to be a significant step toward the generation of large datasets that can lead to robust and generalizing robotic grasping policies.Comment: 7 pages, 8 figures. Preprint versio

Real-World Evolution Adapts Robot Morphology and Control to Hardware Limitations

For robots to handle the numerous factors that can affect them in the real world, they must adapt to changes and unexpected events. Evolutionary robotics tries to solve some of these issues by automatically optimizing a robot for a specific environment. Most of the research in this field, however, uses simplified representations of the robotic system in software simulations. The large gap between performance in simulation and the real world makes it challenging to transfer the resulting robots to the real world. In this paper, we apply real world multi-objective evolutionary optimization to optimize both control and morphology of a four-legged mammal-inspired robot. We change the supply voltage of the system, reducing the available torque and speed of all joints, and study how this affects both the fitness, as well as the morphology and control of the solutions. In addition to demonstrating that this real-world evolutionary scheme for morphology and control is indeed feasible with relatively few evaluations, we show that evolution under the different hardware limitations results in comparable performance for low and moderate speeds, and that the search achieves this by adapting both the control and the morphology of the robot.Comment: Accepted to the 2018 Genetic and Evolutionary Computation Conference (GECCO