Figure 1 - uploaded by Milan Jelisavcic
Content may be subject to copyright.
The Triangle of Life. The pivotal moments that span the triangle and separate the three stages are 1) Conception: A new genome is activated, construction of a new robot starts. 2)Delivery: Construction of the new robot is completed. 3) Fertility: The robot becomes an adult, ready to conceive offspring.
Source publication
Morphological evolution in a robotic system produces novel robot bodies after each reproduction event. This implies the necessity for lifetime 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...
Context in source publication
Context 1
... underlying system architecture that fully explores interactions between bodies, brains and environments is called the Triangle of Life and has been put forward in 2013 [11]. This system captures the pivotal life cycle of an ecosystem of self-reproducing robots as illustrated in Figure 1. ...
Similar publications
The field of Evolutionary Robotics addresses the challenge of automatically designing robotic systems. Furthermore, the field can also support biological investigations related to evolution. In this paper, we evolve (simulated) modular robots under diverse environmental conditions and analyze the influences that these conditions have on the evolved...
Citations
... Finally, there is the full-blown case, embodied evolution of morphologies and controllers together in a Lamarckian manner. This is the most complex category that has hardly been studied so far with only two papers we are aware of 66,67 . These considered the simplest possible robot task (undirected locomotion or gait learning) and observed the increased efficiency and efficacy of Lamrackian evolution without analyzing the underlying mechanisms and the source of the improved performance. ...
... Additionally, it presents a higher overall efficacy: the average fitness of the final populations is 25% higher when using the Lamarckian system. Previous work, from ourselves, with similar findings was based on much simpler robots (without sensors using open-loop controllers) and a simple task (undirected gait learning) 66,67 . Here, we move the front of applicability, showing that Lamarckian evolution is also superior in practically more relevant cases: robots with sensors and closed-loop controllers, evolved for a more challenging task. ...
Evolutionary robot systems offer two principal advantages: an advanced way of developing robots through evolutionary optimization and a special research platform to conduct what-if experiments regarding questions about evolution. Our study sits at the intersection of these. We investigate the question “What if the 18th-century biologist Lamarck was not completely wrong and individual traits learned during a lifetime could be passed on to offspring through inheritance?” We research this issue through simulations with an evolutionary robot framework where morphologies (bodies) and controllers (brains) of robots are evolvable and robots also can improve their controllers through learning during their lifetime. Within this framework, we compare a Lamarckian system, where learned bits of the brain are inheritable, with a Darwinian system, where they are not. Analyzing simulations based on these systems, we obtain new insights about Lamarckian evolution dynamics and the interaction between evolution and learning. Specifically, we show that Lamarckism amplifies the emergence of ‘morphological intelligence’, the ability of a given robot body to acquire a good brain by learning, and identify the source of this success: newborn robots have a higher fitness because their inherited brains match their bodies better than those in a Darwinian system.
... Finally, there is the full-blown case, embodied evolution of morphologies and controllers together in a Lamarckian manner. This is the most complex category that has hardly been studied so far with only two papers (of ourselves) we are aware of [ 63,64 ]. These considered the simplest possible robot task (undirected locomotion, a.k.a. ...
Evolutionary robot systems offer two principal advantages: an advanced way of developing robots through evolutionary optimization and a special research platform to conduct what-if experiments regarding questions about evolution. Our study sits at the intersection of these. We investigate the question ''What if the 18th-century biologist Lamarck was not completely wrong and individual traits learned during a lifetime could be passed on to offspring through inheritance?'' We research this issue through simulations with an evolutionary robot framework where morphologies (bodies) and controllers (brains) of robots are evolvable and robots also can improve their controllers through learning during their lifetime. Within this framework, we compare a Lamarckian system, where learned bits of the brain are inheritable, with a Darwinian system, where they are not. Analyzing simulations based on these systems, we obtain new insights about Lamarckian evolution dynamics and the interaction between evolution and learning. Specifically, we show that Lamarckism amplifies the emergence of 'morphological intelligence', the ability of a given robot body to acquire a good brain by learning, and identify the source of this success: 'newborn' robots have a higher fitness because their inherited brains match their bodies better than those in a Darwinian system.
... While in this paper and in[16] the outstanding strategy in the Plain environment regards disproportional robots, in[11] it is shown that when having a learning phase with in robot framework, robots become more proportional.On the other hand, the fact that the robots in Obstacles are slower than in Plain, but still present the same traits, suggests that the emerged strategy in Obstacles is perhaps a local optimum. Finally, these observations raise a question: if not all environmental changes induce dierent traits in evolved robots, then what kind of changes do and what kind of changes do not? ...
This paper studies the effects of different environments on morphological and behavioral properties of evolving populations of modular robots. To assess these properties, a set of morphological and behavioral descriptors was defined and the evolving population mapped in this multi-dimensional space. Surprisingly, the results show that seemingly distinct environments can lead to the same regions of this space, i.e., evolution can produce the same kind of morphologies/behaviors under conditions that humans perceive as quite different. These experiments indicate that demonstrating the 'ground truth' of evolution stating the firm impact of the environment on evolved morphologies is harder in evolutionary robotics than usually assumed.
Evolutionary robotics is concerned with optimizing autonomous robots for one or more specific tasks. Remarkably, the energy needed to operate autonomously is hardly ever considered. This is quite striking because energy consumption is a crucial factor in real-world applications and ignoring this aspect can increase the reality gap. In this paper, we aim to mitigate this problem by extending our robot simulator framework with a model of a battery module and studying its effect on robot evolution. The key idea is to include energy efficiency in the definition of fitness. The robots will need to evolve to achieve high gait speed and low energy consumption. Since our system evolves the robots’ morphologies as well as their controllers, we investigate the effect of the energy extension on the morphologies and on the behavior of the evolved robots. The results show that by including the energy consumption, the evolution is not only able to achieve higher task performance (robot speed), but it reaches good performance faster. Inspecting the evolved robots and their behaviors discloses that these improvements are not only caused by better morphologies, but also by better settings of the robots’ controller parameters.
Most evolutionary robotics studies focus on evolving some targeted behavior without taking the energy usage into account. This limits the practical value of such systems because energy efficiency is an important property for real-world autonomous robots. In this paper, we mitigate this problem by extending our simulator with a battery model and taking energy consumption into account during fitness evaluations. Using this system we investigate how energy awareness affects the evolution of robots. Since our system is to evolve morphologies as well as controllers, the main research question is twofold: (i) what is the impact on the morphologies of the evolved robots, and (ii) what is the impact on the behavior of the evolved robots if energy consumption is included in the fitness evaluation? The results show that including the energy consumption in the fitness in a multi-objective fashion (by NSGA-II) reduces the average size of robot bodies while at the same time reducing their speed. However, robots generated without size reduction can achieve speeds comparable to robots from the baseline set.
Genetic encodings and their particular properties are known to have a strong influence on the success of evolutionary systems. However, the literature has widely focused on studying the effects that encodings have on performance, i.e., fitness-oriented studies. Notably, this anchoring of the literature to performance is limiting, considering that performance provides bounded information about the behavior of a robot system. In this paper, we investigate how genetic encodings constrain the space of robot phenotypes and robot behavior. In summary, we demonstrate how two generative encodings of different nature lead to very different robots and discuss these differences. Our principal contributions are creating awareness about robot encoding biases, demonstrating how such biases affect evolved morphological, control, and behavioral traits, and finally scrutinizing the trade-offs among different biases.
