Efficient automatic design of robots

Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior. Despite this, almost every detail of every robot built to date has been manually determined by a human designer after several months or years of iterative ideation, prototyping, and testing. Inspired by evolutionary design in nature, the automated design of robots using evolutionary algorithms has been attempted for two decades, but it too remains inefficient: days of supercomputing are required to design robots in simulation that, when manufactured, exhibit desired behavior. Here we show for the first time de-novo optimization of a robot's structure to exhibit a desired behavior, within seconds on a single consumer-grade computer, and the manufactured robot's retention of that behavior. Unlike other gradient-based robot design methods, this algorithm does not presuppose any particular anatomical form; starting instead from a randomly-generated apodous body plan, it consistently discovers legged locomotion, the most efficient known form of terrestrial movement. If combined with automated fabrication and scaled up to more challenging tasks, this advance promises near instantaneous design, manufacture, and deployment of unique and useful machines for medical, environmental, vehicular, and space-based tasks

Differentiable Stripe Patterns for Inverse Design of Structured Surfaces

Stripe patterns are ubiquitous in nature and everyday life. While the synthesis of these patterns has been thoroughly studied in the literature, their potential to control the mechanics of structured materials remains largely unexplored. In this work, we introduce Differentiable Stripe Patterns -- a computational approach for automated design of physical surfaces structured with stripe-shaped bi-material distributions. Our method builds on the work by Knoppel and colleagues for generating globally-continuous and equally-spaced stripe patterns. To unlock the full potential of this design space, we propose a gradient-based optimization tool to automatically compute stripe patterns that best approximate macromechanical performance goals. Specifically, we propose a computational model that combines solid shell finite elements with XFEM for accurate and fully-differentiable modeling of elastic bi-material surfaces. To resolve non-uniqueness problems in the original method, we furthermore propose a robust formulation that yields unique and differentiable stripe patterns. %Finally, we introduce design space regularizers to avoid numerical singularities and improve stripe neatness We combine these components with equilibrium state derivatives into an end-to-end differentiable pipeline that enables inverse design of mechanical stripe patterns. We demonstrate our method on a diverse set of examples that illustrate the potential of stripe patterns as a design space for structured materials. Our simulation results are experimentally validated on physical prototypes.Comment: 14 page

Stability-aware simplification of curve networks

La conception de réseaux de courbes nécessite la considération de plusieurs facteurs: la stabilité de la structure, l'efficience matérielle, et l'aspect esthétique - des objectifs complexes et interdépendants rendant la conception manuelle difficile. Nous présentons une nouvelle méthode permettant de simplifier des réseaux de courbes destinés à la fabrication. Pour un ensemble de courbes 3D donné, notre algorithme en sélectionne un sous-ensemble stable. Bien que la stabilité soit traditionnellement mesurée par l'ordre de grandeur des déformations entraînées par des charges prédéfinies, une telle approche peut s'avérer limitante. Elle ne tient ni compte des effets de vibration pour les structures de grandes tailles, ni des multiples possibilités de forces appliquées pour les structures et objets de plus petite taille. Ainsi, nous optimisons directement pour une déformation minimale avec la charge dans le pire des cas (de l'anglais "worst-case"). Notre contribution technique est une nouvelle formulation de la simplification de réseaux de courbes pour la stabilité dans le pire des cas. Celle-ci mène à un problème d'optimisation semi-définie positive en nombres entiers (MI-SDP). Malgré que résoudre ce problème MI-SDP directement est irréaliste dans la plupart des cas, une intuition physique nous mène à un algorithme vorace efficace. Enfin, nous démontrons le potentiel de notre approache à l'aide plusieurs réseaux de courbes et validons l'efficacité de notre méthode en la comparant de façon quantitative à des approaches plus simples.Designing curve networks for fabrication requires simultaneous consideration of structural stability, cost effectiveness, and visual appeal - complex, interrelated objectives that make manual design a difficult and tedious task. We present a novel method for fabrication-aware simplification of curve networks, algorithmically selecting a stable subset of given 3D curves. While traditionally, stability is measured as the magnitude of deformation induced by a set of predefined loads, predicting applied forces for common day objects can be challenging. Instead, we directly optimize for minimal deformation under the worst-case load. Our technical contribution is a novel formulation of 3D curve network simplification for worst-case stability, leading to a mixed-integer semi-definite programming problem (MI-SDP). We show that while solving MI-SDP directly is impractical, a physical insight suggests an efficient greedy heuristic algorithm. We demonstrate the potential of our approach on a variety of curve network designs and validate its effectiveness compared to simpler alternatives using numerical experiments

State of the Art on Stylized Fabrication

© 2018 The Authors Computer Graphics Forum © 2018 The Eurographics Association and John Wiley & Sons Ltd. Digital fabrication devices are powerful tools for creating tangible reproductions of 3D digital models. Most available printing technologies aim at producing an accurate copy of a tridimensional shape. However, fabrication technologies can also be used to create a stylistic representation of a digital shape. We refer to this class of methods as ‘stylized fabrication methods’. These methods abstract geometric and physical features of a given shape to create an unconventional representation, to produce an optical illusion or to devise a particular interaction with the fabricated model. In this state-of-the-art report, we classify and overview this broad and emerging class of approaches and also propose possible directions for future research