Publications associées à Evolutionary robotics

VP-STO: Via-point-based Stochastic Trajectory Optimization for Reactive Robot Behavior

Sylvain Calinon, Julius Maximilian Jankowski

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 ...

IEEE2023

Multi-Source Fusion for Voxel-Based 7-DoF Grasping Pose Estimation

Zheng Dang, Fei Wang

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 ...

IEEE2023

Tales from a Robotic World. How Intelligent Machines Will Shape Our Future

Dario Floreano, Nicola Nosengo

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 ...

MIT Press2022

A Muscle-Reflex Model of Forelimb and Hindlimb of Felidae Family of Animal with Dynamic Pattern Formation Stimuli

Auke Ijspeert, Azhar Aulia Saputra

Human and animal locomotion are controlled by complex neural circuits, which can also serve as inspiration for designing locomotion controllers for dynamic locomotion in legged robots. We develop a locomotion controller model including a central pattern ge ...

IEEE2020

Inquiry-Based Learning with RoboGen: An Open-Source Software and Hardware Platform for Robotics and Artificial Intelligence

Dario Floreano, Joshua Evan Auerbach, Przemyslaw Mariusz Kornatowski, Alice Ghislaine Concordel

It has often been found that students appreciate hands-on work, and find that they learn more with courses that include a project than those relying solely on conventional lectures and tests. This type of project driven learning is a key component of “Inqu ...

2019

Collision Avoidance with Limited Field of View Sensing: A Velocity Obstacle Approach

Denis Gillet, Alcherio Martinoli, Steven Adriaan Roelofsen

Collision avoidance, in particular between robots, is an important component for autonomous robots. It is a necessary component in numerous applications such as human-robot interaction, automotive or unmanned aerial vehicles. While many collision avoidance ...

2017

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

Daniel Peppicelli, Marc-Oliver Gewaltig, Luc Guyot, Egidio Falotico, Lorenzo Vannucci, Axel von Arnim, Paul Levi, Georg Hinkel, Stefan Ulbrich, Eduardo Ros

Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensor ...

2017

Noise-Resistant Particle Swarm Optimization for the Learning of Robust Obstacle Avoidance Controllers using a Depth Camera

Alcherio Martinoli, Inaki Navarro Oiza, Ezequiel Leonardo Di Mario

The Ranger robot was designed to interact with children in order to motivate them to tidy up their room. Its mechanical configuration, together with the limited field of view of its depth camera, make the learning of obstacle avoidance behaviors a hard pro ...

Ieee2016

Rapid Evolution of Robot Gaits

Dario Floreano, Joshua Evan Auerbach, Przemyslaw Mariusz Kornatowski, Grégoire Hilaire Marie Heitz

The promise of Evolutionary Robotics (ER) to completely automatize the design of robot controllers and/or morphologies is an idea with great appeal not only to researchers, but also to students. However, when attempting to grab and hold student interest, t ...

ACM2015

A Distributed Noise-Resistant Particle Swarm Optimization Algorithm for High-Dimensional Multi-Robot Learning

Alcherio Martinoli, Inaki Navarro Oiza, Ezequiel Leonardo Di Mario

Population-based learning techniques have been proven to be effective in dealing with noise in numerical benchmark functions and are thus promising tools for the high-dimensional optimization of controllers for multiple robots with limited sensing capabili ...

2015