Automated assembly as situated development: using artificial ontogenies to evolve buildable 3-D objects

Artificial Ontogenies, which are inspired by biological development, have been used to automatically generate a wide array of novel objects, some of which have recently been manufactured in the real world. The majority of these evolved designs have been evaluated in simulation as completed objects, with no attention paid to how, or even if, they can be realistically built. As a consequence, significant human effort is required to transfer the designs to the real world. One way to reduce human involvement in this regard is to evolve how to build rather than what to build, by using prescriptive rather than descriptive representations. In the context of Artificial Ontogenies, this requires what we call Situated Development, in which an object's development occurs in the same environment as its final evaluation. Not only does this produce sufficient information on how to build evolved designs, but it also ensures that only buildable designs are evolved. In this paper we explore the consequences of Situated Development, and demonstrate how it can be incorporated into Artificial Ontogenies in order to generate buildable objects, which can be sequentially assembled in a realistic 3-D physics environment.

[1]  Rajeev Motwani,et al.  Complexity Measures for Assembly Sequences , 1999, Int. J. Comput. Geom. Appl..

[2]  Gregory S. Hornby,et al.  The advantages of generative grammatical encodings for physical design , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[3]  Samir W. Mahfoud.  Niching methods for genetic algorithms , 1996 .

[4]  Jordan B. Pollack,et al.  The Emergence of Ontogenic Scaffolding in a Stochastic Development Environment , 2004, GECCO.

[5]  Carlos A. Coello Coello,et al.  An updated survey of evolutionary multiobjective optimization techniques: state of the art and future trends , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[6]  Jordan B. Pollack,et al.  Three Generations of Automatically Designed Robots , 2001, Artificial Life.

[7]  Marc Toussaint,et al.  Demonstrating the Evolution of Complex Genetic Representations: An Evolution of Artificial Plants , 2003, GECCO.

[8]  Lydia E. Kavraki,et al.  On the Complexity of Assembly Partitioning , 1993, CCCG.

[9]  Gregory S. Hornby,et al.  Generative representations for evolutionary design automation , 2003 .

[10]  Jordan B. Pollack,et al.  Artificial Ontogenies for Real World Design and Assembly , 2004 .

[11]  Jeffrey J. Ventrella,et al.  Explorations in the emergence of morphology a~d locomotion behavior in animated characters , 1994 .

[12]  Jordan B. Pollack,et al.  Embodied Evolution: Distributing an evolutionary algorithm in a population of robots , 2002, Robotics Auton. Syst..

[13]  Inman Harvey,et al.  Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics , 1995, ECAL.

[14]  Phil Husbands,et al.  Towards an evolutionary-developmental approach for real-world substrates , 2004 .

[15]  Gregory S. Hornby,et al.  An Evolved Antenna for Deployment on NASA's Space Technology 5 Mission , 2004 .

[16]  Jordan B. Pollack,et al.  Evolution of complexity in real-world domains , 2001 .

[17]  Maciej Komosinski,et al.  Framsticks: Towards a Simulation of a Nature-Like World, Creatures and Evolution , 1999, ECAL.

[18]  Edwin D. de Jong,et al.  Reducing bloat and promoting diversity using multi-objective methods , 2001 .

[19]  Karl Sims,et al.  Evolving virtual creatures , 1994, SIGGRAPH.

[20]  Jordan B. Pollack,et al.  Embodied evolution: embodying an evolutionary algorithm in a population of robots , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[21]  Fumio Hara,et al.  Morpho-functional Machines: The New Species , 2012, Springer Japan.