490 research outputs found
Microsoft robotics soccer challenge : movement optimization of a quadruped robot
Estágio realizado na Universidade de Aveiro e orientado pelo Prof. Doutor Nuno LauTese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200
Communications for cooperation: the RoboCup 4-legged passing challenge
Communications are the basis for the collaborative
activities in the TeamChaos 4-legged team. In this paper we
present the communications architecture developed both to let
teammates communicate, and to easy the debugging of robot
behaviors from external computers. Details of its implementation
on the aiBo robots are also given. Using this infrastructure we
describe a protocol for role exchange named Switch! that we have
created. We also describe the use of both the communication
architecture, and the Switch! protocol in the passing challenge
of the 2006 edition of the RoboCu
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
We investigate whether Deep Reinforcement Learning (Deep RL) is able to
synthesize sophisticated and safe movement skills for a low-cost, miniature
humanoid robot that can be composed into complex behavioral strategies in
dynamic environments. We used Deep RL to train a humanoid robot with 20
actuated joints to play a simplified one-versus-one (1v1) soccer game. We first
trained individual skills in isolation and then composed those skills
end-to-end in a self-play setting. The resulting policy exhibits robust and
dynamic movement skills such as rapid fall recovery, walking, turning, kicking
and more; and transitions between them in a smooth, stable, and efficient
manner - well beyond what is intuitively expected from the robot. The agents
also developed a basic strategic understanding of the game, and learned, for
instance, to anticipate ball movements and to block opponent shots. The full
range of behaviors emerged from a small set of simple rewards. Our agents were
trained in simulation and transferred to real robots zero-shot. We found that a
combination of sufficiently high-frequency control, targeted dynamics
randomization, and perturbations during training in simulation enabled
good-quality transfer, despite significant unmodeled effects and variations
across robot instances. Although the robots are inherently fragile, minor
hardware modifications together with basic regularization of the behavior
during training led the robots to learn safe and effective movements while
still performing in a dynamic and agile way. Indeed, even though the agents
were optimized for scoring, in experiments they walked 156% faster, took 63%
less time to get up, and kicked 24% faster than a scripted baseline, while
efficiently combining the skills to achieve the longer term objectives.
Examples of the emergent behaviors and full 1v1 matches are available on the
supplementary website.Comment: Project website: https://sites.google.com/view/op3-socce
Multi-Agent Task Allocation for Robot Soccer
This is the published version. Copyright De GruyterThis paper models and analyzes task allocation methodologies for multiagent systems. The evaluation process was implemented as a collection of simulated soccer matches. A soccer-simulation software package was used as the test-bed as it provided the necessary features for implementing and testing the methodologies. The methodologies were tested through competitions with a number of available soccer strategies. Soccer game scores, communication, robustness, fault-tolerance, and replanning capabilities were the parameters used as the evaluation criteria for the mul1i-agent systems
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