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This paper studies the probability distribution functions of the parameters to be learned and optimized in biped gait generation. By formulating the gait pattern generation into a multiobjective optimization problem with consideration of geometric and state constraints, dynamically stable and low energy cost biped gaits are generated and optimized by the proposed method, namely Spline-based Estimation of Distribution Algorithm (EDA) with Q-learning updating rule (EDA̱S̱Q). Instead of assuming variables as independent ones, the relationship between them is exploited by formulating the corresponding probability models with the Catmull-Rom cubic spline function. Such kind of function is proved to be a suboptimal and adaptive realization of the cubic spline function and is capable of providing high- precision description. Moreover, the probability models are up- dated autonomously by Q-learning method, which is model-free and adaptive. Thus, EDA̱S̱Q can deal with complex probability distribution functions without a prior knowledge about the distribution. The biped gait generated by EDA̱S̱Q has been verified using the simulation model of a humanoid soccer robot Robo-Erectus. It also shows that EDA̱S̱Q can generate the desired biped gaits autonomously in short learning epochs. An interpretation of the transition probability distribution achieved by EDA̱S̱Q provides us easy understanding for biped locomotion and better control in humanoid robots. [ABSTRACT FROM AUTHOR]

Introduces the rules and regulations that govern the games of the Humanoid League in RoboCup. Players of the game; Size of the field; Ball specifications; Challenges in the Humanoid league; Road map for the league.

Provides a description of a low-cost autonomous humanoid soccer robot called Robo-Erectus (RE), which has been developed in the Center for Advanced Robotics and Intelligent Control at Singapore Polytechnic since 2001. Implementation of several behaviors on a soccer-playing humanoid; Sensors in the RE humanoid soccer robots; Types of humanoid control systems developed for RE series humanoid robots.

Human-robot teams combining the complementary capabilities of robots and humans towards solving potentially complex service tasks are gaining widespread popularity. Many of these tasks will involve close interactions between the robot and the human it serves, thereby making safety a crucial parameter. Erroneous interactions that inevitably arises between the human and the robot cause accidents in service robotic applications. Currently, there are no metrics available in the human-robot interaction community for analyzing erroneous interactions. In this paper, we put forward a new class of false alarm metrics to define, classify and quantify the effects of erroneous interactions in human robot teams, and explore the relationship between false alarms and safety in service robots. We extend the receiver operating characteristics (ROC) curve commonly used in the signal processing community to classify robots based on their associated risks. We also show the utility of the designed false alarm metrics and extended ROC curve by applying them to a service robot, Robo-Erectus@Home, across tele-operation and semi-autonomous modes of autonomy. [ABSTRACT FROM AUTHOR]