Survey of Intelligent Control Techniques for Humanoid Robots
Journal of Intelligent and Robotic Systems
Layered learning in multiagent systems
Layered learning in multiagent systems
Exploiting inherent robustness and natural dynamics in the control of bipedal walking robots
Exploiting inherent robustness and natural dynamics in the control of bipedal walking robots
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Learning CPG-based Biped Locomotion with a Policy Gradient Method: Application to a Humanoid Robot
International Journal of Robotics Research
Reinforcement learning for robot soccer
Autonomous Robots
Machine learning for fast quadrupedal locomotion
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
UT Austin Villa 2011: a champion agent in the RoboCup 3D soccer simulation competition
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Humanoid robots learning to walk faster: from the real world to simulation and back
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
WrightEagle and UT Austin villa: RoboCup 2011 simulation league champions
Robot Soccer World Cup XV
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In several realistic domains an agent's behavior is composed of multiple interdependent skills. For example, consider a humanoid robot that must play soccer, as is the focus of this paper. In order to succeed, it is clear that the robot needs to walk quickly, turn sharply, and kick the ball far. However, these individual skills are ineffective if the robot falls down when switching from walking to turning, or if it cannot position itself behind the ball for a kick. This paper presents a learning architecture for a humanoid robot soccer agent that has been fully deployed and tested within the RoboCup 3D simulation environment. First, we demonstrate that individual skills such as walking and turning can be parameterized and optimized to match the best performance statistics reported in the literature. These results are achieved through effective use of the CMA-ES optimization algorithm. Next, we describe a framework for optimizing skills in conjunction with one another, a little-understood problem with substantial practical significance. Over several phases of learning, a total of roughly 100--150 parameters are optimized. Detailed experiments show that an agent thus optimized performs comparably with the top teams from the RoboCup 2010 competitions, while taking relatively few man-hours for development.