Abstract
This paper presents the modifications needed to adapt a humanoid agent architecture and behaviors from simulation to a real robot. The experiments were conducted using the Aldebaran Nao robot model. The agent architecture was adapted from the RoboCup 3D Simulation League to the Standard Platform League with as few changes as possible. The reasons for the modifications include small differences in the dimensions and dynamics of the simulated and the real robot and the fact that the simulator does not create an exact copy of a real environment. In addition, the real robot API is different from the simulated robot API and there are a few more restrictions on the allowed joint configurations. The general approach for using behaviors developed for simulation in the real robot was to: first, (if necessary) make the simulated behavior compliant with the real robot restrictions, second, apply the simulated behavior to the real robot reducing its velocity, and finally, increase the velocity, while adapting the behavior parameters, until the behavior gets unstable or inefficient. This paper also presents an algorithm to calculate the three angles of the hip that produce the desired vertical hip rotation, since the Nao robot does not have a vertical hip joint. All simulation behaviors described in this paper were successfully adapted to the real robot.

Abstract
Decision making in complex, multi agent and dynamic environments such as disaster spaces is a challenging problem in Artificial Intelligence. Uncertainty, noisy input data and stochastic behavior which are common characteristics of such environment makes real time decision making more complicated. In this paper an approach to solve the bottleneck of dynamicity and variety of conditions in such situations based on reinforcement learning is presented. This method is applied to RoboCup Rescue Simulation Fire brigade agent's decision making process and it learned a good strategy to save civilians and city from fire. The utilized method increases the speed of learning and it has very low memory usage. The effectiveness of the proposed method is shown through simulation results.

Abstract
This paper presents an approach to the problem of novelty detection in the context of semantic room categorization. The ability to assign semantic labels to areas in the environment is crucial for autonomous agents aiming to perform complex human-like tasks and human interaction. However, in order to be robust and naturally learn the semantics from the human user, the agent must be able to identify gaps in its own knowledge. To this end, we propose a method based on graphical models to identify novel input which does not match any of the previously learnt semantic descriptions. The method employs a novelty threshold defined in terms of conditional and unconditional probabilities. The novelty threshold is then optimized using an unconditional probability density model trained from unlabelled data.

Abstract
This paper presents an approach to the identification of playing cards and counting of chips in a poker game environment, using an entry-level webcam and computer vision methodologies. Most of the previous works on playing cards identification rely on optimal camera position and controlled environment. The presented approach is intended to suit a real and uncontrolled environment along with its constraints. The recognition of playing cards lies on template matching, while the counting of chips is based on colour segmentation combined with the Hough Circles Transform. With the proposed approach it is possible to identify the cards and chips in the table correctly. The overall accuracy of the rank identification achieved is around 94%.

Abstract
Biped walking by using all joint movements and DOFs in both directions (sagittal plane and coronal plane) is one of the most complicated research topics in robotics. In this paper, angular trajectories of a stable biped walking for a humanoid robot are generated by a Truncated Fourier Series (TFS) approach. The movements of legs and arms in sagittal plane are implemented by an optimized gait generator and a new model is proposed that can also produce the movement of legs in coronal plane based on TFS. Particle Swarm Optimization (PSO) is used to find the best angular trajectories and optimize TFS. Experimental results show that the using joints movements in sagittal and coronal planes to compose the walking skill allowed the biped robot to walk faster than previous methods that only used the joints in sagittal plane. © 2011 Springer-Verlag.

Abstract
In soccer games, a performance indicator is defined as a selection of action variables that aims to define all aspects of accomplishment of the game goals. However their perception during the match is extremely difficult. Over the years, soccer has been used in many research areas including the robotic international soccer competition, RoboCup. The aim of this research project is to present a comparison study, performed to detect similarities between these two games (Human versus Robotic Simulation 2D soccer). Having an off-line automatic event detection tool as a base, a collection of final game statistics was done and the Mann-Whitney test was used to verify their statistical significance. The results show that the most frequent events occurred in both types of game are successful passes. In what concerns stopped game situation types, in both types of games, the most frequent one is the Throw in situation (Human-59,8%, versus Robotic-74,1%) and the less frequent is the Corner situation (Human-13,7%, versus Robotic-10,3%). Some differences still reside, especially in the frequency of set pieces and the action prior the goal. © 2011 Springer-Verlag.