Robotic pitching by rolling ball on fingers for a randomly located target
This study considers a robotic pitching task with the goal of implementing dynamic manipulation. The strategy used to control the pitching direction of the ball is based on human pitching action, and involves rolling the fingers on the ball at release. The effect of varying the time of release in the pitching direction is analyzed based on ball dynamics. Experiments to test the proposed method featured a high-speed manipulator, composed of a robotic arm with four degrees of freedom (DoFs) and a hand with 10 DoFs, throwing a ball toward a randomly located target recognized by using high-speed vision.
The world of competitive Esports and video gaming has seen and continues to experience steady growth in popularity and complexity. Correspondingly, more research on the topic is being published, ranging from social network analyses to the benchmarking of advanced artificial intelligence systems in playing against humans. In this paper, we present ongoing work on an intelligent agent recommendation engine that suggests actions to players in order to maximise success and enjoyment, both in the space of in-game choices, as well as decisions made around play session timing in the broader context. By leveraging temporal data and appropriate models, we show that a learned representation of player psychological momentum, and of tilt, can be used, in combination with player expertise, to achieve state-of-the-art performance in pre- and post-draft win prediction. Our progress toward fulfilling the potential for deriving optimal recommendations is documented.