Ball positioning in robotic billiards: a nonprehensile manipulation-based solution

The development and testing of a robotic system to play billiards is described in this paper. The last two decades have seen a number of developments in creating robots to play billiards. Although the designed systems have uccessfully incorporated the kinematics required for gameplay, a system level approach needed for accurate shot- making has not been realized. The current work considers the different aspects, like machine vision, dynamics, robot design and computational intelligence, and proposes, for the first time, a method based on robotic non-prehensile manipulation. High-speed video tracking is employed to determine the parameters of balls dynamics. Furthermore, three-dimensional impact models, involving ball spin and friction, are developed for different collisions. A three degree of freedom manipulator is designed and fabricated to execute shots. The design enables the manipulator to position the cue on the ball accurately and strike with controlled speeds. The manipulator is controlled from a PC via a microcontroller board. For a given table scenario, optimization is used to search the inverse dynamics space to find best parameters for the robotic shot maker. Experimental results show that a 90% potting accuracy and a 100–200 mm post-shot cue ball positioning accuracy has been achieved by the autonomous system

Trajectory solutions for a game-playing robot using nonprehensile manipulation methods and machine vision

The need for autonomous systems designed to play games, both strategy-based and physical, comes from the quest to model human behaviour under tough and competitive environments that require human skill at its best. In the last two decades, and especially after the 1996 defeat of the world chess champion by a chess-playing computer, physical games have been receiving greater attention. Robocup TM, i.e. robotic football, is a well-known example, with the participation of thousands of researchers all over the world. The robots created to play snooker/pool/billiards are placed in this context. Snooker, as well as being a game of strategy, also requires accurate physical manipulation skills from the player, and these two aspects qualify snooker as a potential game for autonomous system development research. Although research into playing strategy in snooker has made considerable progress using various artificial intelligence methods, the physical manipulation part of the game is not fully addressed by the robots created so far. This thesis looks at the different ball manipulation options snooker players use, like the shots that impart spin to the ball in order to accurately position the balls on the table, by trying to predict the ball trajectories under the action of various dynamic phenomena, such as impacts. A 3-degree of freedom robot, which can manipulate the snooker cue on a par with humans, at high velocities, using a servomotor, and position the snooker cue on the ball accurately with the help of a stepper drive, is designed and fabricated. [Continues.

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

Extending a Game Engine with Machine Learning and Artificial Intelligence

Since the early days of Artificial Intelligence (AI), video games have been a popular testbed for evaluating methods. However, not until a long time ago, this only included building AI agents for playing board games like chess. In the last decade, researchers have found out that games are also a rich source for other types of AI problems. At the same time, Machine Learning (ML) has entered its golden age with the advancements of deep learning. In games, this has led to a wide range of novel AI methods for solving various problems like playing games with super-human performance. Despite the significant advances, state-of-the-art AI methods are still far from being used in commercial games. One of the main reasons is that the tools used by researchers and game developers are different, which makes it difficult to use open-source codes in game projects. Furthermore, implementing these methods requires a moderate understanding of ML, which is not among the skill set of a regular game programmer. This calls for plug-and-play tools that enable game developers to deploy AI methods with minimum cost. In this thesis, we develop a library that enables game developers to use state-of-the-art ML methods in their commercial projects. This library integrates Tensorflow, a modern ML toolbox, into Unity, the most common game engine in the industry. This library uses C# with intuitive Keras-like API for building and training models. We have also implemented several state-of-the-art algorithms, including Proximal Policy Optimization (PPO), Matrix Adaptation Evolution Strategies (MA-ES), and Generative Adversarial Networks (GAN). This library can also be used along with Unity ML-Agents, the Unity plugin for building AI-training environments. Moreover, we provide various examples that demonstrate the library and the algorithms. One important example is a game called Calamachine Union, where the core game mechanic includes training the AI

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Development of criteria suitable for machine learning based on morphological hierarchical trees

The goal of this work is to study image criteria to be assigned to morphological trees such as Max/Mintree, Binary Partition Trees or similar representations to be able to classify the tree node and to identify the presence of object of interest in the scene.Nowadays the technology is changing the way of performing and it is adapting towards Artificial Intelligence. However this technique is still being introduced and is not common in the domain of image processing based on morphological trees. This thesis focuses on the creation of a criterion based on machine learning to be assigned into morphological tree. The developed criterion is based on a Convolutional Neural Network, called Overfeat, which runs in to the nodes of a Binary Partition Tree, in order to be able to detect traffic signs. It has turned out to be a suitable criterion to identify traffic sings in images but it has room of improvement due to its performance is lower than 70% of success.Hoy en día la tecnología está cambiando su forma de actuar y se está adaptando hacia la Inteligencia Artificial. Aunque esta técnica se está introduciendo, no es muy común en el dominio del procesamiento de imagen basado en arboles morfológicos. Esta tesis se centra en la creación de un criterio basado en Machine learning que se asigna a un árbol morfológico. El criterio desarrollado en este proyecto se basa en una Red Neuronal Colvolucional, llamada Overfeat, que trabaja sobre los nodos de un árbol de partición binaria, para ser capaz de identificar señales de tráfico. El criterio ha resultado ser adecuado para identificar señales de tráfico pero aún tiene margen de mejora ya que los resultados obtenidos no son superiores al 70% de acierto.Avui en dia la tecnologia esta canviant la seva forma d'actuar i s'està adaptant cap a la Intel·ligència Artificial. Tot i que aquesta tècnica s'està introduint no és gaire comú en el domini del processament d'imatge basat en arbres morfològics. Aquesta tesis es centra en la creació d'un criteri basat en machine learning que s'assigna a un arbre morfològic. El criteri desenvolupat en aquest projecte es basa en una Xarxa Neuronal Convolucional, anomenada Overfeat, que treballa sobre els nodes d'un arbre de partició binaria, per ser capaç d'identificar senyals de transit. El criteri ha resultat ser adequat per identificar senyals de transit però encara te marge de millora ja que els resultats obtinguts no son superiors al 70% d'encert