Evolutionary Robotics. From Intelligent Robotics to Artificial Life

2001 ◽  
Keyword(s):  
Artificial Life ◽  
Evolutionary Robotics ◽  
Intelligent Robotics

scholarly journals Evolving Mobile Robots in Simulated and Real Environments

1995 ◽  
Vol 2 (4) ◽  
pp. 417-434 ◽  
Author(s):  
Orazio Miglino ◽  
Henrik Hautop Lund ◽  
Stefano Nolfi
Keyword(s):  
Artificial Life ◽  
Autonomous Robots ◽  
Evolutionary Process ◽  
Evolutionary Robotics ◽  
Machine Learning Techniques ◽  
The Real ◽  
Real Environment ◽  
Neural Controllers ◽  
Conservative Form ◽  
Learning Techniques

The problem of the validity of simulation is particularly relevant for methodologies that use machine learning techniques to develop control systems for autonomous robots, as, for instance, the artificial life approach known as evolutionary robotics. In fact, although it has been demonstrated that training or evolving robots in real environments is possible, the number of trials needed to test the system discourages the use of physical robots during the training period. By evolving neural controllers for a Khepera robot in computer simulations and then transferring the agents obtained to the real environment we show that (a) an accurate model of a particular robot-environment dynamics can be built by sampling the real world through the sensors and the actuators of the robot; (b) the performance gap between the obtained behaviors in simulated and real environments may be significantly reduced by introducing a “conservative” form of noise; (c) if a decrease in performance is observed when the system is transferred to a real environment, successful and robust results can be obtained by continuing the evolutionary process in the real environment for a few generations.

scholarly journals Synthetic transitions: towards a new synthesis

2016 ◽  
Vol 371 (1701) ◽  
pp. 20150438 ◽  
Author(s):  
Ricard Solé
Keyword(s):  
Synthetic Biology ◽  
Artificial Life ◽  
Evolutionary Biology ◽  
Statistical Physics ◽  
Evolutionary Robotics ◽  
Evolutionary Transitions ◽  
Major Transitions ◽  
Genetic Codes ◽  
New Synthesis ◽  
Evolution Of Life

The evolution of life in our biosphere has been marked by several major innovations. Such major complexity shifts include the origin of cells, genetic codes or multicellularity to the emergence of non-genetic information, language or even consciousness. Understanding the nature and conditions for their rise and success is a major challenge for evolutionary biology. Along with data analysis, phylogenetic studies and dedicated experimental work, theoretical and computational studies are an essential part of this exploration. With the rise of synthetic biology, evolutionary robotics, artificial life and advanced simulations, novel perspectives to these problems have led to a rather interesting scenario, where not only the major transitions can be studied or even reproduced, but even new ones might be potentially identified. In both cases, transitions can be understood in terms of phase transitions, as defined in physics. Such mapping (if correct) would help in defining a general framework to establish a theory of major transitions, both natural and artificial. Here, we review some advances made at the crossroads between statistical physics, artificial life, synthetic biology and evolutionary robotics. This article is part of the themed issue ‘The major synthetic evolutionary transitions’.

Evolutionary Body Building: Adaptive Physical Designs for Robots

1998 ◽  
Vol 4 (4) ◽  
pp. 337-357 ◽  
Author(s):  
Pablo Funes ◽  
Jordan Pollack
Keyword(s):  
Real Time ◽  
Artificial Life ◽  
General Framework ◽  
Three Dimensional ◽  
Evolutionary Robotics ◽  
Life Forms ◽  
Physical Structure ◽  
Body Building ◽  
Slow Pace ◽  
Robot Body

Creating artificial life forms through evolutionary robotics faces a “chicken and egg” problem: Learning to control a complex body is dominated by problems specific to its sensors and effectors, while building a body that is controllable assumes the pre-existence of a brain. The idea of coevolution of bodies and brains is becoming popular, but little work has been done in evolution of physical structure because of the lack of a general framework for doing it. Evolution of creatures in simulation has usually resulted in virtual entities that are not buildable, while embodied evolution in actual robotics is constrained by the slow pace of real time. The work we present takes a step in addressing the problem of body evolution by applying evolutionary techniques to the design of structures assembled out of elementary components that stick together. Evolution takes place in a simulator that computes forces and stresses and predicts stability of three-dimensional brick structures. The final printout of our program is a schematic assembly, which is then built physically. We demonstrate the functionality of this approach to robot body building with many evolved artifacts.

Friends Reunited? Evolutionary Robotics and Representational Explanation

2005 ◽  
Vol 11 (1-2) ◽  
pp. 215-231 ◽  
Author(s):  
Michael Wheeler
Keyword(s):  
Real Time ◽  
Artificial Life ◽  
Evolutionary Robotics ◽  
Reciprocal Causation ◽  
Context Sensitive ◽  
Neural Factors ◽  
Over Time ◽  
Intelligent Action ◽  
The Way

Robotics as practiced within the artificial life community is no longer the bitter enemy of representational explanation in the way that it sometimes seemed to be in the heady, revolutionary days of the 1990s. This rapprochement is, however, fragile, because the field of evolutionary robotics continues to pose two important challenges to the idea that real-time intelligent action must or should be explained by appeal to inner representations. The first of these challenges, the threat from nontrivial causal spread, occurs when extra-neural factors account for the kind of adaptive richness and flexibility normally associated with representation-based control. The second, the threat from continuous reciprocal causation, occurs when the causal contributions made by the systemic components collectively responsible for behavior generation are massively context-sensitive and variable over time. I argue that while the threat from nontrivial causal spread can be resisted, the threat from continuous reciprocal causation provides a stern test for our representational intuitions.

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