6 accepted papers
Driven by significant advancements in structure, sensors, and control algorithms, multi-fingered hand systems have received increasing attention from both academia and industry. While existing research primarily focuses on the interaction static rigid objects manipulation, real-world scenarios often
This paper proposes a simple yet effective markerless hand-eye calibration method that achieves low cost, high accuracy, and strong generalization across different types of robots. The method utilizes a circular flange, a standardized structure in industrial robots, for calibration via the perspecti…
Although many impressive advances have been reported in the table tennis robots field using reinforcement learning method, challenges related to policy complexity and adaptability continue to hinder large-scale deployment and practical applications. In this work, building upon extensive prior studie
Tactile sensing technologies enable robots to interact with the environment in increasingly nuanced and dexterous ways. A significant gap in this domain is the absence of curved tactile sensors, which are essential for performing sophisticated manipulation tasks. In this study, we present DotTip, a
Table tennis robots have significantly advanced in performance owing to the rapid progress in deep learning and reinforcement learning technologies. However, these advancements often require a large number of training samples. Besides, research focused on the robot serve task remains relatively limi
Multi-stage deformable linear object manipulation, such as cable routing, is the common and necessary part of human life and industry. However, autonomous robots still lack the dexterity and generalization required for these complex tasks. Direct teleoperation is an alternative approach, but the abs