546 research outputs found
(A) Vision for 2050 - Context-Based Image Understanding for a Human-Robot Soccer Match
We believe it is possible to create the visual subsystem needed for the RoboCup 2050 challenge - a soccer match between humans and robots - within the next decade.  In this position paper, we argue, that the basic techniques are available, but the main challenge will be to achieve the necessary robustness. We propose to address this challenge through the use of probabilistically modeled context, so for instance a visually indistinct circle is  accepted as the ball, if it fits well with the ball's motion model and vice versa.Our vision is accompanied by a sequence of (partially already conducted) experiments for its verification.  In these experiments, a human soccer player carries a helmet with a camera and an inertial sensor and the vision system has to extract all information from that data, a humanoid robot would need to take the human's place
A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision
Deep learning has the potential to revolutionize sports performance, with
applications ranging from perception and comprehension to decision. This paper
presents a comprehensive survey of deep learning in sports performance,
focusing on three main aspects: algorithms, datasets and virtual environments,
and challenges. Firstly, we discuss the hierarchical structure of deep learning
algorithms in sports performance which includes perception, comprehension and
decision while comparing their strengths and weaknesses. Secondly, we list
widely used existing datasets in sports and highlight their characteristics and
limitations. Finally, we summarize current challenges and point out future
trends of deep learning in sports. Our survey provides valuable reference
material for researchers interested in deep learning in sports applications
Machine intelligence sports as research programs
Games and competitions have played a significant role throughout the history of artificial intelligence and robotics. Machine intelligence games are examined here from a distinctive methodological perspective, focusing on their use as generators of multidisciplinary research programs. In particular, Robocup is analyzed as an exemplary case of contemporary research program developing from machine intelligence games. These research programs arising are schematized in terms of framework building, subgoaling, and outcome appraisal processes. The latter process is found to involve a rather intricate system of rewards and penalties, which take into account the double allegiance of participating scientists, trading and sharing interchanges taking place in a multidisciplinary research environment, in addition to expected industrial payoffs and a variety of other fringe research benefits in the way of research outreach and results dissemination, recruitment of junior researchers and students enrollment
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
Non-Monotonic Reasoning on Board a Sony AIBO
Griffith Sciences, School of Information and Communication TechnologyFull Tex
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