Bootstrapping Artificial Evolution to Design Robots for Autonomous Fabrication

Abstract

A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolutionary robotics has been widely used due to its capability of creating unique robot designs in simulation. Recent work has shown that it is possible to autonomously construct evolved designs in the physical domain; however, this brings new challenges: the autonomous manufacture and assembly process introduces new constraints that are not apparent in simulation. To tackle this, we introduce a new method for producing a repertoire of diverse but manufacturable robots. This repertoire is used to seed an evolutionary loop that subsequently evolves robot designs and controllers capable of solving a maze-navigation task. We show that compared to random initialisation, seeding with a diverse and manufacturable population speeds up convergence and on some tasks, increases performance, while maintaining manufacturability.

Real-World Robot Evolution: Why Would it (not) Work?

“…The system is based on a Robot Fabricator, the RoboFab, to implement robot (re)production without human assistance Hale et al (2019). The project is still running, but the experiences with an autonomous manufacture and assembly process revealed that real-world robot reproduction introduces new constraints on evolution that are not apparent in simulation Buchanan et al (2020).…”

“…The solution to mitigate this issue is to use an unnatural trick: a sophisticated assessment of a new robot genotype (created by recombination and mutation of the genotypes of existing robots) before it is transformed into a new phenotype, that is, before starting the construction of the corresponding physical robot. A recent study introduced two notions to this end: manufacturability and viability Buchanan et al (2020). Manufacturability is the property that a certain body plan can indeed be constructed with the given system for robot (re) production.…”

On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme

“…Approaches that operate in a morphological search-space defined by pre-defined modules are particularly common [6,7,12,14] while increasingly the idea is being applied in soft-robotics, using voxel based simulators [7,8]. Recent work by Buchanan et al [1] extends the approach to a very complex morphological space that mixes some pre-defined modules (sensors, wheels, joints) with a 3d printed skeleton that can take any shape or form. Unlike the vast majority of previous work relating to joint body-control optimisation, the robots contain a variety of sensors, which introduces additional challenges regarding automate design of controllers: most previous approaches optimise controllers to produce actuation (e.g.…”

“…In this paper we investigate the interaction between morphology evolution and controller learning in the rich morphological space defined in [1] that includes a variety of sensors and actuators using the hierarchical method shown in figure 1. We evaluate two forms of learning, used in conjunction with a single evolutionary approach to optimise morphology.…”

“…We use the most recent version [5] in which the learning is bootstrapped wherever possible by use of an external archive that stores the best controller found for a given 'type' of robot. Type is defined by a tuple (sensors, wheels, joints) 1 .Using two environments and a photo-taxis task, we investigate:…”