Analysing the relative importance of robot brains and bodies

The evolution of robots, when applied to both the morphologies and the controllers, is not only a means to obtain high-quality robot designs, but also a process that results in many body-brain-fitness data points. Inspired by this perspective, in this paper we investigate the relative importance of robot bodies and brains for a good fitness. We introduce a method to isolate and quantify the effect of the bodies and brains on the quality of the robots and perform a case study. The method is general in that it is not restricted to evolutionary systems. For the case study, we use a system of modular robots, where the bodies are evolvable and the brains are evolvable and learnable. These case studies validate the usefulness of our method and deliver interesting insights into the interplay between bodies and brains in evolutionary robotics

Improving RL Power for On-Line Evolution of Gaits in Modular Robots

This paper addresses the problem of on-line gait learning in modular robots whose shape is not known in advance. The best algorithm for this problem known to us is a reinforcement learning method, called RL PoWER. In this study we revisit the original RL PoWER algorithm and observe that in essence it is a specific evolutionary algorithm. Based on this insight we propose two modifications of the main search operators and compare the quality of the evolved gaits when either or both of these modified operators are employed. The results show that using 2-parent crossover as well as mutation with self- adaptive step-sizes can significantly improve the performance of the original algorithm

Lamarckian Evolution of Simulated Modular Robots

We study evolutionary robot systems where not only the robot brains but also the robot bodies are evolvable. Such systems need to include a learning period right after ‘birth' to acquire a controller that fits the newly created body. In this paper we investigate the possibility of bootstrapping infant robot learning through employing Lamarckian inheritance of parental controllers. In our system controllers are encoded by a combination of a morphology dependent component, a Central Pattern Generator (CPG), and a morphology independent part, a Compositional Pattern Producing Network (CPPN). This makes it possible to transfer the CPPN part of controllers between different morphologies and to create a Lamarckian system. We conduct experiments with simulated modular robots whose fitness is determined by the speed of locomotion, establish the benefits of inheriting optimized parental controllers, shed light on the conditions that influence these benefits, and observe that changing the way controllers are evolved also impacts the evolved morphologies

Morphological Attractors in Darwinian and Lamarckian Evolutionary Robot Systems

Morphological evolution in a robotic system produces novel robot bodies after each reproduction event. This implies the necessity for life-time learning so that newborn robots can acquire a controller that fits their body. Thus, we obtain a system where evolution and learning are combined. This combination can be Darwinian or Lamarckian and in this paper, we compare the two. In particular, we investigate the evolved morphologies under these regimes for modular robots evolved for good locomotion. Using eight quantifiable morphological descriptors to characterize the physical properties of robots we compare the regions of attraction in the resulting 8-dimensional space. The results show prominent differences in symmetry, size, proportion, and coverage

Analysis of Lamarckian Evolution in Morphologically Evolving Robots

Evolving robot morphologies implies the need for lifetime learning so that newborn robots can learn to manipulate their bodies. An individual’s morphology will obviously combine traits of all its parents; it must adapt its own controller to suit its morphology, and cannot rely on the controller of any one parent to perform well without adaptation. This paper investigates the practicability and benefits of Lamarckian evolution in this setting. Implementing lifetime learning by means of on-line evolution, we first establish the suitability of an indirect encoding scheme that combines Compositional Pattern Producing Networks (CPPNs) and Central Pattern Generators (CPGs) as a relevant learner and controller for open-loop gait controllers. We then analyze a Lamarckian set-up and the effect of the parental genetic material on the early convergence to good locomotion performance

Revolve: A Versatile Simulator for Online Robot Evolution

Developing robotic systems that can evolve in real-time and real-space is a long term objective with technological as well as algorithmic milestones on the road. Technological prerequisites include advanced 3D-printing, automated assembly, and robust sensors and actuators. The necessary evolutionary mechanisms need not wait for these, they can be developed and investigated in simulations. In this paper, we present a system to simulate online evolution of constructible robots, where (1) the population members (robots) concurrently exist and evolve their morphologies and controllers, (2) all robots can be physically constructed. Experiments with this simulator provide us with insights into differences of using online and offline evolutionary setups

Real-World Evolution of Robot Morphologies: A Proof of Concept

Evolutionary robotics using real hardware has been almost exclusively restricted to evolving robot controllers, but the technology for evolvable morphologies is advancing quickly. We discuss a proof-of-concept study to demonstrate real robots that can reproduce. Following a general system plan, we implement a robotic habitat that contains all system components in the simplest possible form. We create an initial population of two robots and run a complete life cycle, resulting in a new robot, parented by the first two. Even though the individual steps are simplified to the maximum, the whole system validates the underlying concepts and provides a generic workflow for the creation of more complex incarnations. This hands-on experience provides insights and helps us elaborate on interesting research directions for future development