Evolutionary robotics is an embodied approach to Artificial Intelligence (AI) in which robots are automatically designed using Darwinian principles of natural selection. The design of a robot, or a subsystem of a robot such as a neural controller, is optimized against a behavioral goal (e.g. run as fast as possible). Usually, designs are evaluated in simulations as fabricating thousands or millions of designs and testing them in the real world is prohibitively expensive in terms of time, money, and safety.
An evolutionary robotics experiment starts with a population of randomly generated robot designs. The worst performing designs are discarded and replaced with mutations and/or combinations of the better designs. This evolutionary algorithm continues until a prespecified amount of time elapses or some target performance metric is surpassed.
Evolutionary robotics methods are particularly useful for engineering machines that must operate in environments in which humans have limited intuition (nanoscale, space, etc.). Evolved simulated robots can also be used as scientific tools to generate new hypotheses in biology and cognitive science, and to test old hypothesis that require experiments that have proven difficult or impossible to carry out in reality.
In the early 1990s, two separate European groups demonstrated different approaches to the evolution of robot control systems. Dario Floreano and Francesco Mondada at EPFL evolved controllers for the Khepera robot. Adrian Thompson, Nick Jakobi, Dave Cliff, Inman Harvey, and Phil Husbands evolved controllers for a Gantry robot at the University of Sussex.
However the body of these robots was presupposed before evolution.
The first simulations of evolved robots were reported by Karl Sims and Jeffrey Ventrella of the MIT Media Lab, also in the early 1990s. However these so-called virtual creatures never left their simulated worlds. The first evolved robots to be built in reality were 3D-printed by Hod Lipson and Jordan Pollack at Brandeis University at the turn of the 21st century.
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The course describes theories, methods, and technologies for designing robots and artificial systems inspired by evolution, development, and learning. It also shows how robotic models can help to unde
Cet enseignement croise des approches culturelles (notamment dans les domaines littéraires ou artistiques), sociales et scientifiques pour penser les enjeux, les perspectives et les problèmes que soul
The course teaches the basics of autonomous mobile robots. Both hardware (energy, locomotion, sensors) and software (signal processing, control, localization, trajectory planning, high-level control)
On propose dans ce MOOC de se former à et avec Thymio :
apprendre à programmer le robot Thymio et ce faisant, s’initier
à l'informatique et la robotique.
In diesem Kurs handelt es sich um das Verständnis der grundlegenden Mechanismen eines Roboters wie Thymio, seiner Programmierung mit verschiedenen Sprachen und seiner Verwendung im Unterricht mit den
In diesem Kurs handelt es sich um das Verständnis der grundlegenden Mechanismen eines Roboters wie Thymio, seiner Programmierung mit verschiedenen Sprachen und seiner Verwendung im Unterricht mit den
Robotics is an interdisciplinary branch of electronics and communication, computer science and engineering. Robotics involves the design, construction, operation, and use of robots. The goal of robotics is to design machines that can help and assist humans. Robotics integrates fields of mechanical engineering, electrical engineering, information engineering, mechatronics engineering, electronics, biomedical engineering, computer engineering, control systems engineering, software engineering, mathematics, etc.
An autonomous robot is a robot that acts without recourse to human control. The first autonomous robots environment were known as Elmer and Elsie, which were constructed in the late 1940s by W. Grey Walter. They were the first robots in history that were programmed to "think" the way biological brains do and meant to have free will. Elmer and Elsie were often labeled as tortoises because of how they were shaped and the manner in which they moved. They were capable of phototaxis which is the movement that occurs in response to light stimulus.
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated.
Introduces the basics of robotics, covering definitions, classifications, and statistics, and explores the evolution and applications of different types of robots.
Explains the significance of mini-batches in Deep Reinforcement Learning and the differences between on-policy and off-policy methods.
Covers compliant and force control for robots using Dynamical Systems.
In this work, we tackle the problem of 7-DoF grasping pose estimation(6-DoF with the opening width of parallel-jaw gripper) from point cloud data, which is a fundamental task in robotic manipulation. Most existing methods adopt 3D voxel CNNs as the backbon ...
Stories from the future of intelligent machines—from rescue drones to robot spouses—and accounts of cutting-edge research that could make it all possible. Tech prognosticators promised us robots—autonomous humanoids that could carry out any number of tasks ...
Achieving reactive robot behavior in complex dynamic environments is still challenging as it relies on being able to solve trajectory optimization problems quickly enough, such that we can replan the future motion at frequencies which are sufficiently high ...