Abstract
Competitive robotic table tennis involves many topics such as smart ball returning strategy, precise motion control, etc. It remains a quite challenging task due to the unpredictable, uncooperative incoming ball and the requirement of a smart strategy to defeat the opponent. Designation and control of landing points is one basic aspect of ball returning strategies in competitive robotic table tennis because different landing points require the opponent to make different efforts to return the ball. In this paper, we present a method to designate desired landing points based on competitiveness level. We also propose a learning based landing point control approach to minimize the error of the actual landing points with respect to the designated landing points. The proposed methods have been verified through experiments on a humanoid table tennis robot.
Highlights
Robotic table tennis is an excellent but challenging task
Cooperative table tennis means that the ball is returned in a way such that the ball will be suitable for the opponent to hit
This paper addresses the landing point designation and control problems for competitive robotic table tennis
Summary
Robotic table tennis is an excellent but challenging task. It involves many research topics such as visual servo, trajectory generation, ball returning strategy, etc. Designation and control of landing points is such a basic and important issue in competitive robotic table tennis, to our knowledge, only a tiny amount of the previous studies have addressed the problem. Paper [4] designed an expert control or game strategy planning approach for landing point determination of a ping-pong player prototype. A recent study [21], from a biological viewpoint, modelled human strategies in table tennis playing using inverse reinforcement learning and provided discussions on some relevant features, such as landing point of the ball and velocity of the ball which are not ignorable in ball returning. In order to explore the basic issues of competitive robotic table tennis, a method to designate landing points for ball returning, and a learning control method to reduce errors between the actual and desired landing points will be presented in this paper.
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