4 accepted papers
This paper proposes a real-time, generalizable trajectory prediction framework that integrates neural networks with physical modeling, applicable to various ball sports. Unlike existing methods that are tailored to specific ball types and lack scalability, our approach addresses two core challenges:
Learning-based methods, such as imitation learning (IL) and reinforcement learning (RL), can produce excel control policies over challenging agile robot tasks, such as sports robot. However, no existing work has harmonized learning-based policy with model-based methods to reduce training complexity
The 3D trajectory of a shuttlecock required for a badminton rally robot for human-robot competition demands real-time performance with high accuracy. However, the fast flight speed of the shuttlecock, along with various visual effects, and its tendency to blend with environmental elements, such as c
Surface electromyography (sEMG) noninvasively records muscle activities. It provides valuable information about muscle contractions and enables real-time decoding into hand gestures. Recently many studies have successfully demonstrated this capability. However, the accuracy of gesture recognition de…