Genetic algorithms for the generation of models with micropopulations

Proceedings of: EvoWorkshops 2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, and EvoSTIM Essex, UK, April 14–16, 2003The present article puts forward a method for an interactive model generation through the use of Genetic Algorithms applied to small populations. Micropopulations actually worsen the problem of the premature convergence of the algorithm, since genetic diversity is very limited. In addition, some key factors, which modify the changing likelihood of alleles, cause the likelihood of premature convergence to decrease. The present technique has been applied to the design of 3D models, starting from generic and standard pieces, using objective searches and searches with no defined objective

"Sticky Hands": learning and generalization for cooperative physical interactions with a humanoid robot

"Sticky Hands" is a physical game for two people involving gentle contact with the hands. The aim is to develop relaxed and elegant motion together, achieve physical sensitivity-improving reactions, and experience an interaction at an intimate yet comfortable level for spiritual development and physical relaxation. We developed a control system for a humanoid robot allowing it to play Sticky Hands with a human partner. We present a real implementation including a physical system, robot control, and a motion learning algorithm based on a generalizable intelligent system capable itself of generalizing observed trajectories' translation, orientation, scale and velocity to new data, operating with scalable speed and storage efficiency bounds, and coping with contact trajectories that evolve over time. Our robot control is capable of physical cooperation in a force domain, using minimal sensor input. We analyze robot-human interaction and relate characteristics of our motion learning algorithm with recorded motion profiles. We discuss our results in the context of realistic motion generation and present a theoretical discussion of stylistic and affective motion generation based on, and motivating cross-disciplinary research in computer graphics, human motion production and motion perception

Simulation and analysis of complex human tasks

We discuss how the combination of a realistic human figure with a high-level behavioral control interface allow the construction of detailed simulations of humans performing manual tasks from which inferences about human performance requirements can be made. The Jack human modeling environment facilitates the real-time simulation of humans performing sequences of tasks such as walking, lifting, reaching, and grasping in a complex simulated environment. Analysis capabilities include strength, reachability, and visibility; moreover results from these tests can affect an unfolding simulation

Hierarchical spacetime control

Specifying the motion of an animated linked figure such that it achieves given tasks (e.g., throwing a ball into a basket) and performs the tasks in a realistic fashion (e.g., gracefully, and following physical laws such as gravity) has been an elusive goal for computer animators. The spacetime constraints paradigm has been shown to be a valuable approach to this problem, but it suffers from computational complexity growth as creatures and tasks approach those one would like to animate. The complexity is shown to be, in part, due to the choice of finite basis with which to represent the trajectories of the generalized degrees of freedom. This paper describes new features to the spacetime constraints paradigm to address this problem.The functions through time of the generalized degrees of freedom are reformulated in a hierarchical wavelet representation. This provides a means to automatically add detailed motion only where it is required, thus minimizing the number of discrete variables. In addition the wavelet basis is shown to lead to better conditioned systems of equations and thus faster convergence.Engineering and Applied Science

Linear Bellman combination for control of character animation

Controllers are necessary for physically-based synthesis of character animation. However, creating controllers requires either manual tuning or expensive computer optimization. We introduce linear Bellman combination as a method for reusing existing controllers. Given a set of controllers for related tasks, this combination creates a controller that performs a new task. It naturally weights the contribution of each component controller by its relevance to the current state and goal of the system. We demonstrate that linear Bellman combination outperforms naive combination often succeeding where naive combination fails. Furthermore, this combination is provably optimal for a new task if the component controllers are also optimal for related tasks. We demonstrate the applicability of linear Bellman combination to interactive character control of stepping motions and acrobatic maneuvers.Singapore-MIT GAMBIT Game LabNational Science Foundation (U.S.) (Grant 2007043041)National Science Foundation (U.S.) (Grant CCF-0810888)Adobe SystemsPixar (Firm

Interactive simulation of stylized human locomotion

Animating natural human motion in dynamic environments is difficult because of complex geometric and physical interactions. Simulation provides an automatic solution to parts of this problem, but it needs control systems to produce lifelike motions. This paper describes the systematic computation of controllers that can reproduce a range of locomotion styles in interactive simulations. Given a reference motion that describes the desired style, a derived control system can reproduce that style in simulation and in new environments. Because it produces high-quality motions that are both geometrically and physically consistent with simulated surroundings, interactive animation systems could begin to use this approach along with more established kinematic methods.Singapore-MIT GAMBIT Game LabNational Science Foundation (U.S.) (Fellowship 2007043041)Pixar (Firm

Un système interactif pour le prototypage virtuel coopératif

We present in this thesis the study and implementation of an interactive system for cooperative prototyping of virtual models. These works make use of several technologies from different scientific backgrounds; Virtual Reality is at the crossroads of many disciplines. Our goal is not to replace right now a CAD system with a system such as that we propose in this thesis. Indeed, the power of the machines does not allow yet the management of virtual objects with an accuracy comparable to that of CAD tools. While our system is intuitive and interactive but does not have enough machine power to compete with such precision tools; This precision is however necessary for the industry. This development will be achieved, for sure, but it is more reasonable for the moment to see virtual reality as a complement to CAD.Nous présentons dans ce mémoire l’étude et la réalisation d’un système interactif pour le prototypage coopératif de maquettes virtuelles. Ces travaux font usage de plusieurs technologies issues de milieux scientifiques variés ; la réalité virtuelle n’est elle pas à la croisée des chemins de nombreuses disciplines ? Notre objectif n’est pas de remplacer dès à présent un système de CAO par un système tel que celui que nous proposons dans ce mémoire. En effet, la puissance des machines ne permet pas encore la gestion d’objets virtuels avec une précision comparable à celle des outils de CAO. Certes notre système est intuitif et interactif mais il ne dispose pas d’assez de puissance machine pour rivaliser en précision avec de tels outils ; cette précision est pourtant nécessaire pour l’industrie. Cette évolution se fera, c’est sûr, mais il est pour l’instant plus raisonnable de voir la réalité virtuelle comme un complément de la CAO

A comparative study of search and optimization algorithms for the automatic control of physically realistic 2-D animated figures

In the Spacetime Constraints paradigm of animation, the animator specifies what a character should do, and the details of the motion are generated automatically by the computer. Ngo and Marks [11, 12] recently proposed a technique of automatic motion synthesis that uses a massively parallel genetic algorithm to search a space of motion controllers that generate physically realistic motions for 2D articulated figures. In this paper, we describe an empirical study of evolutionary computation algorithms and standard function optimization algorithms that were implemented in lieu of the massively parallel GA in order to find a substantially more efficient search algorithm that would be viable on serial workstations. We discovered that simple search algorithms based on the evolutionary programming paradigm were most efficient in searching the space of motion controllers.Engineering and Applied Science