Using a 3DOF Parallel Robot and a Spherical Bat to hit a Ping-Pong Ball

Playing the game of Ping-Pong is a challenge to human abilities since it requires developing skills, such as fast reaction capabilities, precision of movement and high speed mental responses. These processes include the utilization of seven DOF of the human arm, and translational movements through the legs, torso, and other extremities of the body, which are used for developing different game strategies or simply imposing movements that affect the ball such as spinning movements. Computationally, Ping-Pong requires a huge quantity of joints and visual information to be processed and analysed, something which really represents a challenge for a robot. In addition, in order for a robot to develop the task mechanically, it requires a large and dexterous workspace, and good dynamic capacities. Although there are commercial robots that are able to play Ping-Pong, the game is still an open task, where there are problems to be solved and simplified. All robotic Ping-Pong players cited in the bibliography used at least four DOF to hit the ball. In this paper, a spherical bat mounted on a 3-DOF parallel robot is proposed. The spherical bat is used to drive the trajectory of a Ping-Pong ball.Fil: Trasloheros, Alberto. Universidad Aeronáutica de Querétaro; MéxicoFil: Sebastián, José María. Universidad Politécnica de Madrid; España. Consejo Superior de Investigaciones Científicas; EspañaFil: Torrijos, Jesús. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; EspañaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Roberti, Flavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Ping Pong Simulation

The purpose of this project is to come out with a working prototype of the Ping Pong game where the user can play Ping Pong game by using wireless detector of Ping Pong bat that is already equipped with infra red LED. Since normal virtual reality application requires a complex virtual reality system with a lot of wires and sophisticated gadgets to handle the application, this prototype of virtual reality Ping Pong game can be played by only using webcam and modified Ping Pong bat and the system requirement for the application is only average running PC that equip with Intel Pentium 4 or AMD Athlon. This project requires me to develop a game engine by using either DirectX or OpenGL graphic libraries. Both offer inherent advantages and disadvantages, hence warranting a study about these libraries and come out with a better solution. This project use waterfall method where the project consist of 3 phases that consist of all development step in waterfall model. Waterfall model is relevant because the project does not involve with customer. Hardware setup and basic application is done where the motion capture device is ready to be used and the application with basic functionality has completed. There is a lot of improvement that can be made to improve the whole system, both hardware and software. But there are limit where I can improve the hardware since the driver for motion capture is confidential

Probabilistic movement modeling for intention inference in human-robot interaction.

Intention inference can be an essential step toward efficient humanrobot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows to infer the intention from observed movements using Bayes ’ theorem. The IDDM simultaneously finds a latent state representation of noisy and highdimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e., target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.

Spin observation and trajectory prediction of a ping-pong ball

© 2014 IEEE. For ping-pong playing robots, observing a ball and predicting a ball's trajectory accurately in real-time is essential. However, most existing vision systems can only provide ball's position observation, and do not take into consideration the spin of the ball, which is very important in competitions. This paper proposes a way to observe and estimate ball's spin in real-time, and achieve an accurate prediction. Based on the fact that a spinning ball's motion can be separated into global movement and spinning respect to its center, we construct an integrated vision system to observe the two motions separately. With a pan-tilt vision system, the spinning motion is observed through recognizing the position of the brand on the ball and restoring the 3D pose of the ball. Then the spin state is estimated with the method of plane fitting on current and historical observations. With both position and spin information, accurate state estimation and trajectory prediction are realized via Extended Kalman Filter(EKF). Experimental results show the effectiveness and accuracy of the proposed method

Concepts in a Probabilistic Language of Thought

Note: The book chapter is reprinted courtesy of The MIT Press, from the forthcoming edited collection “The Conceptual Mind: New Directions in the Study of Concepts” edited by Eric Margolis and Stephen Laurence, print date Spring 2015.Knowledge organizes our understanding of the world, determining what we expect given what we have already seen. Our predictive representations have two key properties: they are productive, and they are graded. Productive generalization is possible because our knowledge decomposes into concepts—elements of knowledge that are combined and recombined to describe particular situations. Gradedness is the observable effect of accounting for uncertainty—our knowledge encodes degrees of belief that lead to graded probabilistic predictions. To put this a different way, concepts form a combinatorial system that enables description of many different situations; each such situation specifies a distribution over what we expect to see in the world, given what we have seen. We may think of this system as a probabilistic language of thought (PLoT) in which representations are built from language-like composition of concepts and the content of those representations is a probability distribution on world states. The purpose of this chapter is to formalize these ideas in computational terms, to illustrate key properties of the PLoT approach with a concrete example, and to draw connections with other views of conceptual structure.This work was supported by ONR awards N00014-09-1-0124 and N00014-13- 1-0788, by a John S. McDonnell Foundation Scholar Award, and by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216

The Cowl - v.18 - n.23 - May 23, 1956

The Cowl - student newspaper of Providence College. Volume 18, Number 23 - May 23, 1956. 6 pages

From Spacewar! to Twitch.tv: The Influence of Competition in Video Games and the Rise of eSports

Since their inception in the 1950s, video games have come a long way; with that advancement came more popularity, a growing demand, and an evolving culture. The first person shooter (FPS) video game genre and the competitive scene that was born out of it is an ideal case study to analyze this change over time. To understand how video games became so popular, one must examine their history: specifically, their development, impacts the games have had on society, and economic trajectories. Similar to traditional professional sports, video games experienced a cultural shift around their lucrative profit margins and unfolding professionalization of gamers as entertainers/athletes. Professional gaming started in the 1980s, where 10,000 participants competed in the Space Invaders Championship. Since then, video games evolved from being a casual past time to a career for some gamers. The resulting professional gaming community has attracted the attention of wealthy businessman, including a disproportionate number of iconic sports names, including the New York Yankees, Golden State Warriors, Magic Johnson, and Robert Kraft, who have all bought into eSports. All of this is possible due to advancement in technology and significantly improved graphics which allows game developers to increase the amount of content and quality of their games. Without continual advancement in these areas, gamers start to lose interest, which means no economic and societal growth. For example, games released in the early 2000s such as Counter Strike, World of Warcraft, and Halo have utilized online features to allow players to compete with whoever they want from the comfort of home, making it easier than ever for gamers to hone their skills against others. Today, constant updates and new titles are now the norm for successful video game companies; marking this particular industry and accompanying culture as a microcosm for global society at large