106 research outputs found
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
Systematic mapping literature review of mobile robotics competitions
This paper presents a systematic mapping literature review about the mobile robotics
competitions that took place over the last few decades in order to obtain an overview of the main
objectives, target public, challenges, technologies used and final application area to show how these
competitions have been contributing to education. In the review we found 673 papers from 5 different
databases and at the end of the process, 75 papers were classified to extract all the relevant information
using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method.
More than 50 mobile robotics competitions were found and it was possible to analyze most of the
competitions in detail in order to answer the research questions, finding the main goals, target public,
challenges, technologies and application area, mainly in education.info:eu-repo/semantics/publishedVersio
- …