A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is
For table tennis robots, it is a significant challenge to understand the opponent's movements and return the ball accordingly with high performance. ... In this paper, we propose a real-time 6D racket pose detection method and classify racket movements into five stroke categories with a neural network. ... Acknowledgment This work was supported in part by the Vector Stiftung and KUKA. ...doi:10.35708/rc1868-126249 fatcat:o2f4rcyrinbhvjrii3iogd52pa
2019 Third IEEE International Conference on Robotic Computing (IRC)
), Alessandro Masi (CERN), Manuel Ferre (UPM), and Raul Marin Prades (UJI) Best Paper Session Markerless Racket Pose Detection and Stroke Classification Based on Stereo Vision for Table Tennis Robots ... Selection Predictive Power Estimation for a Differential Drive Mobile Robot Based on Motor and Robot Dynamic Models 301 Mauricio F. ...doi:10.1109/irc.2019.00004 fatcat:fnivkrmx4vbmrovqyuv2ljbjl4
We give our perspective on this integration of different fields to transition from motor learning research in laboratory settings to real world environments and real world motor tasks and propose a stepwise ... We also give an overview about the rapid expansion of machine learning algorithms and sensor technologies for human motion analysis. ... days or, with new strings on the existing racket, only a couple of strokes (i.e., motor adaptation). ...doi:10.3390/s22072481 pmid:35408094 pmcid:PMC9002555 fatcat:ku34qggs4zasncicwjrlucla4m
Despite this, eye tracking is not widely used as control interface for movement impaired patients due to poor signal interpretation and lack of control flexibility. ... The developed 3D gaze interfaces also allowed this potential to be taken beyond the computer screen to control robot [...] ... The gaze events used are based on the SMI classification. ...doi:10.25560/68748 fatcat:zrtowu3wc5bqxde7ocs3hcy664