Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Robot weightlifting by direct policy search
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Emerging behaviors by learning joint coordination in articulated mobile robots
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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This paper presents a methodology for generating coordination motions task performed by articulated mobile robots. It is considered for the case that both, states defining the task dynamics are not completely observable and model-based solutions fail to scale up due to the complexity of unstructured problems. The novel approach try to optimize primitive trajectories to achieve joint coordination emerging as a consequence of the dynamic followed by the actuators. A simple dynamical system is proposed as basic trajectory generator, becoming a nonlinear solution in the task space due to the actual nonlinearities and constraints of the robot. Joints share information while act along its common natural environment, the robot body. Policies gathering and distributing signals to the actuators are optimized based on a measure of the task's performance. Trajectories are generated through the experiences of the robot, rather than computed from a mathematical model of its body. The simulated version of the AIBO robot completing a ball throwing task is used to illustrate the effectiveness of the proposed scheme.