29 research outputs found
Quadruped robot locomotion using a global optimization stochastic algorithm
The problem of tuning nonlinear dynamical systems parameters, such that the attained results are considered good
ones, is a relevant one. This article describes the development of a gait optimization system that allows a fast but stable robot
quadruped crawl gait. We combine bio-inspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). CPGs are
modelled as autonomous differential equations, that generate the necessar y limb movement to perform the required walking
gait. The GA finds parameterizations of the CPGs parameters which attain good gaits in terms of speed, vibration and stability.
Moreover, two constraint handling techniques based on tournament selection and repairing mechanism are embedded in the
GA to solve the proposed constrained optimization problem and make the search more efficient.
The experimental results, performed on a simulated Aibo robot, demonstrate that our approach allows low vibration with
a high velocity and wide stability margin for a quadruped slow crawl gait.This work is funded by FEDER Funding supported by the Operational Program Competitive Factors .U COMPETE and National Funding supported by the FCT Portuguese Science Foundation through project PTDC/EEACRO/100655/200
Multiobjective optimization of a quadruped robot locomotion using a genetic algorithm
In this work, it is described a gait multiobjective optimization system
that allows to obtain fast but stable robot quadruped crawl gaits. We combine bioinspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). A motion architecture based on CPGs oscillators is used to model the locomotion of the robot dog and a GA is used to search parameterizations of the CPGs parameters
which minimize the body vibration, maximize the velocity and maximize the wide stability margin. In this problem, there are several conflicting objectives that leads to a multiobjective formulation that is solved using the Weighted Tchebycheff scalarization method. Several experimental results show the effectiveness of this proposed approach.Fundação para a Ciência e a Tecnologia (FCT
An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks
In search of a simple baseline for Deep Reinforcement Learning in locomotion
tasks, we propose a model-free open-loop strategy. By leveraging prior
knowledge and the elegance of simple oscillators to generate periodic joint
motions, it achieves respectable performance in five different locomotion
environments, with a number of tunable parameters that is a tiny fraction of
the thousands typically required by DRL algorithms. We conduct two additional
experiments using open-loop oscillators to identify current shortcomings of
these algorithms. Our results show that, compared to the baseline, DRL is more
prone to performance degradation when exposed to sensor noise or failure.
Furthermore, we demonstrate a successful transfer from simulation to reality
using an elastic quadruped, where RL fails without randomization or reward
engineering. Overall, the proposed baseline and associated experiments
highlight the existing limitations of DRL for robotic applications, provide
insights on how to address them, and encourage reflection on the costs of
complexity and generality.Comment: video: https://b2drop.eudat.eu/s/ykDPMM7F9KFyLgi minimal code:
https://gist.github.com/araffin/1fb77a8f290ac248b2e76e01164f21e