Evolutionary robotics

By José Antonio Becerra Permuy, Richard José Duro. 2014

545 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Mechanical Engineering

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In Mechanical Engineering

Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Thus, the ﬁeld of evolutionary computation (Holland, 1975; Eiben and Smith, 2015) includes areas such as evolutionary robotics (Harvey et al., 1997; Nolﬁ and Floreano, 2000) and neuroevolution (Yao, 1999) to investigate the autonomous design of complex, bio-inspired adaptive physical and neural…”
Interactively shaping robot behaviour with unlabeled human instructions (2019, methodology): “Autonomous learning methods, such as Reinforcement Learning [22] or Evolutionary Approaches [9], rely on a predefined evaluation function that enables the robot to autonomously evaluate its performance on the task.”
Leveraging multiple simulators for crossing the reality gap (2012, methodology): “(2) Where, D is the drag force ρ is the liquid density A is the frontal area Cd is the drag coefficient Cl is the lift coefficient and V is the relative velocity The buoyancy force can be calculated from Archimedes principle.”
From evolutionary computation to the evolution of things (2015, background): “These include the possibility of continuous and automated design, manufacture, and deployment of robots of very different morphologies and control systems 41 .”
Evolving Spiking Networks with Variable Resistive Memories (2014, background): “Nolfi and Floreano (2000) describe the evolution of networks for robotics tasks.”
Quality Diversity: A New Frontier for Evolutionary Computation (2016, background): “…diversity, behavioral diversity, evolutionary computation, neuroevolution 1. inTrODUcTiOn The products of nature have long served as inspiration for the investigation and practice of evolutionary algorithms and evolutionary robotics (Cliff et al., 1993; Nolfi and Floreano, 2000; Stanley, 2011).”
The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics (2013, background): “EVOLUTIONARY ROBOTICS (ER) [39], [46] deals with the use of Evolutionary Algorithms (EA) in robotics.”
Evolvability Is Inevitable: Increasing Evolvability without the Pressure to Adapt (2013, background): “As an initial such exploration, a genotype-phenotype mapping and simulated environment are implemented in the spirit of digital evolution [12] and evolutionary robotics (ER; [13]).”
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012, background): “…for decades to test the bottom-up emergence of intelligent behaviors in fields such as artificial life (Langton, 1990) and evolutionary robotics (Floreano and Nolfi, 2004; Floreano and Mattiussi, 2008), the results presented here show that very small structures can serve as exemplary paradigms…”
A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges (2019, methodology): “Evolutionary robotics is a technique for the automatic creation of autonomous robots that leverages the tools of neural networks, genetic algorithms, and dynamic systems [131].”
Surrogate-Assisted Learning of Neural Networks (2017, background): “Rather, robots learn how to solve a certain task by themselves, e.g., by methods of evolutionary robotics [4].”