Hierarchical Table Tennis Spin Estimation Using Ball Trajectory Data

Accurate spin estimation is crucial for assisting table tennis robots in defeating high-level human players. Currently, most researchers use methods based on physical models or identify logos on the flying table tennis ball to estimate spin. We try a new approach, considering the use of a data-driven method to estimate spin. However, directly using such models does not produce satisfactory results, as these methods do not consider that table tennis spin estimation is a coarse-to-fine process. To address this problem, we develop a hierarchical spin estimation network to estimate spin progressively. Furthermore, we introduce a Mix Conv-Attn Block to enhance feature extraction from table tennis trajectories. This block can capture both short-term and long-term features to improve the estimation accuracy. Comparing our approach with physical-based and non-hierarchical neural network methods, experimental results show that our method achieves superior performance.