Collaborative billiARds: Towards the Ultimate Gaming Experience

Abstract. In this paper, we identify the features that enhance gaming experience in Augmented Reality (AR) environments. These include Tangibl

Collaborative billiARds: Towards the ultimate gaming experience

Abstract. In this paper, we identify the features that enhance gaming experience in Augmented Reality (AR) environments. These include Tangible User Interface, force-feedback, audio-visual cues, collaboration and mobility. We base our findings on lessons learnt from existing AR games. We apply these results to billiARds which is an AR system that, in addition to visual and aural cues, provides force-feedback. billiARds supports interaction through a visionbased tangible AR interface. Two users can easily operate the proposed system while playing Collaborative billiARds game around a table. The users can collaborate through both virtual and real objects. User study confirmed that the resulting system delivers enhanced gaming experience by supporting the five features highlighted in this paper

Learning Manipulation under Physics Constraints with Visual Perception

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. In this work, we consider the problem of autonomous block stacking and explore solutions to learning manipulation under physics constraints with visual perception inherent to the task. Inspired by the intuitive physics in humans, we first present an end-to-end learning-based approach to predict stability directly from appearance, contrasting a more traditional model-based approach with explicit 3D representations and physical simulation. We study the model's behavior together with an accompanied human subject test. It is then integrated into a real-world robotic system to guide the placement of a single wood block into the scene without collapsing existing tower structure. To further automate the process of consecutive blocks stacking, we present an alternative approach where the model learns the physics constraint through the interaction with the environment, bypassing the dedicated physics learning as in the former part of this work. In particular, we are interested in the type of tasks that require the agent to reach a given goal state that may be different for every new trial. Thereby we propose a deep reinforcement learning framework that learns policies for stacking tasks which are parametrized by a target structure.Comment: arXiv admin note: substantial text overlap with arXiv:1609.04861, arXiv:1711.00267, arXiv:1604.0006

Learning Manipulation under Physics Constraints with Visual Perception

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. In this work, we consider the problem of autonomous block stacking and explore solutions to learning manipulation under physics constraints with visual perception inherent to the task. Inspired by the intuitive physics in humans, we first present an end-to-end learning-based approach to predict stability directly from appearance, contrasting a more traditional model-based approach with explicit 3D representations and physical simulation. We study the model's behavior together with an accompanied human subject test. It is then integrated into a real-world robotic system to guide the placement of a single wood block into the scene without collapsing existing tower structure. To further automate the process of consecutive blocks stacking, we present an alternative approach where the model learns the physics constraint through the interaction with the environment, bypassing the dedicated physics learning as in the former part of this work. In particular, we are interested in the type of tasks that require the agent to reach a given goal state that may be different for every new trial. Thereby we propose a deep reinforcement learning framework that learns policies for stacking tasks which are parametrized by a target structure

APLIKASI ACCELEROMETER SEBAGAI KENDALI (JOYSTICK) PERMAINAN BOLA SODOK BERBASIS MODUL GAME XGS AVR 8-BIT

Penggunaan joystick sebagai kendali permainan yang dirasa kurang menarik dalam bermain game membuat para pengembang game mengembangkan kendali yang menggunakan sensor gerak. Sehingga bermain game akan lebih interaktif karena pemain juga ikut bergerak dalam bermain game. Pada tugas akhir ini akan dikembangkan kendali game menggunakan accelerometer sebagai pengganti joystick. Accelerometer adalah sensor yang dapat mendeteksi percepatan, kemiringan dan getaran sebuah benda. Accelerometer ini digunakan sebagai kendali permainan bola sodok yang dibangun pada modul game XGS AVR 8-BIT. Accelerometer dipakaikan pada pergelangan tangan pemain sehingga dapat mendeteksi percepatan pergelangan tangan pemain. Data keluaran dari accelerometer masih terdapat noise sehingga harus diberi filter. Pada tugas akhir ini akan dibandingkan filter Moving Average(MA) dan filter Gaussian dalam memfilter data keluaran dari accelerometer. Pemberian filter membuat data keluaran accelerometer akan lebih halus dan noise akan sedikit berkurang. Pada tugas akhir ini terdapat lima gerakan tangan pemain yaitu gerakan memukul bola, membelokkan tongkat ke kanan dan ke kiri dan menggerakkan tongkat ke kanan dan ke kiri. Pemberian filter akan berdasarkan gerakan dan data keluaran yang dihasilkan accelerometer. Secara garis besar, keberhasilan gerakan tangan terhadap pergerakan dalam permainan rata-rata sebesar 84 %. Hal itu bisa disebabkan beberapa faktor yaitu sinyal noise dalam data keluaran accelerometer, gerakan tangan yang sulit dan perlunya penyesuaian pada pemain dalam menggunakan accelerometer sebagai kendali permainan. Kata kunci: accelerometer, permainan bola sodok, modul game XGS AVR 8-Bit, filter MA, filter Gaussia

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

Haptics: state of the art survey

This paper presents a novel approach to the understanding of Haptic and its related fields where haptics is used extensively like in display systems, communication, different types of haptic devices, and interconnection of haptic displays where virtual environment should feel like equivalent physical systems. There have been escalating research interests on areas relating to haptic modality in recent years, towards multiple fields. However, there seems to be limited studies in determining the various subfields and interfacing and related information on haptic user interfaces and its influence on the fields mentioned. This paper aims to bring forth the theory behind the essence of Haptics and its Subfields like haptic interfaces and its applications

PV boost converter conditioning using neural network

This master report presents a voltage control system for DC-DC boost converter integrated with Photovoltaic (PV) array using optimized feed-forward neural network controller. A specific output voltage of a boost converter is regulated at a constant value under input voltage variations caused by a sudden changes in irradiation for a purpose of supplying a stabilize dc voltage to Base Transceiver Station (BTS) telecommunication equipment that required a 48V dc input supply to be operated. For a given solar irradiance, the tracking algorithm changes the duty ratio of the converter such that the output voltage produced equals to 48V. This is done by the feed-forward loop, which generates an error signal by comparing converter output voltage and reference voltage. Depending on the error and change of error signals, the neural network controller generates a control signal for the pulse widthmodulation generator which in turn adjusts the duty ratio of the converter. The effectiveness of the proposed method is verified by developing a simulation model in MATLAB-Simulink program. Tracking performance of the proposed controller is also compared with the conventional proportional-integral-differential (PID) controller. The simulation results show that the proposed neural network controller (NNC) produce an improvement of control performance compared to the PID controller