The Robot Baby Project

The Robot Baby Project

The Robot Baby Project is a focused attempt to demonstrate that robots can have children. The scientific background is provided by a model of robotic reproduction and evolution, published the 2013 paper called: The Triangle of Life: Evolving Robots in Real-time and Real-space. The Triangle of Life framework describes the pivotal life cycle of self-reproducing robots. This life cycle does not run from birth to death, but from conception (being conceived) to conception (conceiving one or more children) and it is repeated over and over again, thus creating consecutive generations of robots. The result is an evolving population of robotic organisms, where the bodies as well as the brains can adapt to the given environment. The Triangle of Life is an abstract model, EvoSphere -discussed on the previous tab- is a tangible incarnation of it.

The Robot Baby Project is a proof of concept. Its main objective is to implement all three constituents of the Triangle of Life in a simplified form and to connect the dots, that is, complete one full life cycle. It is to prove the feasibility of robots that can reproduce in hardware, in the real world, rather than in software simulation. With this demonstration we hope to initiate a healthy scientific discussion and inspire further research.

The main premise is simplification. Each system component is made as simple as possible in order to be able to integrate them into a full cycle. In particular, we have:

  • Specified a certain type of robots (bodies and brains) and the genetic language that can describe a robot through an artificial genotype (DNA). Our design is based on RoboGen.
  • Set up a procedure that starts with a genotype (code for a certain robot) and ends with a phenotype, a physical robot designated by the given genotype. This amounts to the birth or morphogenesis process in the Triangle.
  • Implemented learning method for infant robots to learn to control their own body. This belongs to the infancy stage.
  • Established a reproduction mechanism that regulates mate selection and randomised recombination of the parental genomes. This implements adult life.

For the specific demonstration we have constructed two robots. We let them go through the infancy stage and become adults. The skill they had to learn was locomotion and navigation to a specific spot, the ‘mating corner’ of the habitat. Once they met in the mating corner, they mated (virtually) and sent their DNA to the Birth Clinic. This consisted of a 3D printer and collection of ‘body parts’, such as CPUs, light sensors, servo motors. The parental genotypes were randomly recombined into a new piece of DNA and a new robot was printed and assembled according to this specification. This delivered the first robot baby and concluded the first robotic life cycle. The project achieved its objective. We have gained much know-how and could identify important issues for further research and development.

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Milan Jelisavcic
Researcher PhD Candidate

My research interests include distributed robotics, mobile computing and programmable matter.

Publications

We study evolutionary robot systems where not only the robot brains but also the robot bodies are evolvable. Such systems need to …

Developing robotic systems that can evolve in real-time and real-space is a long term objective with technological as well as …

The evolution of robots, when applied to both the morphologies and the controllers, is not only a means to obtain high-quality robot …

Morphologically evolving robot systems need to include a learning period right after ‘birth’ to acquire a controller that fits …

Morphological evolution in a robotic system produces novel robot bodies after each reproduction event. This implies the necessity for …

Evolving robot morphologies implies the need for lifetime learning so that newborn robots can learn to manipulate their bodies. An …

We construct and investigate a strongly embodied evolutionary system, where not only the controllers but also the morphologies undergo …

Implementing lifetime learning by means of on-line evolution, we establish an indirect encoding scheme that combines Compositional …

Evolutionary robotics using real hardware has been almost exclusively restricted to evolving robot controllers, but the technology for …

This paper addresses the problem of on-line gait learning in modular robots whose shape is not known in advance. The best algorithm for …