Direct search methods: then and now
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
Development of an Autonomous Quadruped Robot for Robot Entertainment
Autonomous Robots - Special issue on autonomous agents
Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning to exploit dynamics for robot motor coordination
Learning to exploit dynamics for robot motor coordination
Online convex optimization in the bandit setting: gradient descent without a gradient
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Applied optimal control for dynamically stable legged locomotion
Applied optimal control for dynamically stable legged locomotion
Robot weightlifting by direct policy search
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Coordination motion-tasks using actual robot dynamics
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Coordination motion-tasks using actual robot dynamics
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
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A Policy Gradient Reinforcement Learning (RL) technique is used to design the low level controllers that drives the joints of articulated mobile robots: A search in the controller's parameters space. There is an unknown value function that measures the quality of the controller respect to the parameters of it. The search is orientated by the approximation of the gradient of the value function. The approximation is made by means of the robot experiences and then the behaviors emerge. This technique is employed in a structure that processes sensor information to achieve coordination. The structure is based on a modularization principle in which complex overall behavior is the result of the interaction of individual 'simple' components. The simple components used are standard low level controllers (PID) which output is combined, sharing information between articulations and therefore taking integrated control actions. Modularization and Learning are cognitive features, here we endow the robots with this features. Learning experiences in simulated robots are presented as demonstration.