no code implementations • 16 Oct 2023 • Milad Shafiee, Guillaume Bellegarda, Auke Ijspeert
Learning a locomotion policy for quadruped robots has traditionally been constrained to a specific robot morphology, mass, and size.
no code implementations • 12 Jun 2023 • Milad Shafiee, Guillaume Bellegarda, Auke Ijspeert
Consistent with quadruped animal data, we show that the walk-trot gait transition for quadruped robots on flat terrain improves both viability and energy efficiency.
no code implementations • 26 Feb 2023 • Milad Shafiee, Guillaume Bellegarda, Auke Ijspeert
Moreover, our investigation shows that sensing the front feet distances to the gap is the most important and sufficient sensory information for learning gap crossing.
no code implementations • 29 Dec 2022 • Guillaume Bellegarda, Milad Shafiee, Auke Ijspeert
2) What are the effects of using a memory-enabled vs. a memory-free policy network with respect to robustness, energy-efficiency, and tracking performance in sim-to-real navigation tasks?
no code implementations • 1 Nov 2022 • Guillaume Bellegarda, Auke Ijspeert
In this letter, we present a method for integrating central pattern generators (CPGs), i. e. systems of coupled oscillators, into the deep reinforcement learning (DRL) framework to produce robust and omnidirectional quadruped locomotion.
no code implementations • 18 Jan 2021 • Emmanouil Angelidis, Emanuel Buchholz, Jonathan Patrick Arreguit O'Neil, Alexis Rougè, Terrence Stewart, Axel von Arnim, Alois Knoll, Auke Ijspeert
In this work we propose a spiking CPG neural network and its implementation on neuromorphic hardware as a means to control a simulated lamprey model.