Motion recognition and generation by combining reference-point-dependent probabilistic models

— This paper presents a method to recognize and generate sequential motions for object manipulation such as placing one object on another or rotating it. Motions are learned using reference-point-dependent probabilistic models, which are then transformed to the same coordinate system and combined for motion recognition/generation. We conducted physical experiments in which a user demonstrated the manipulation of puppets and toys, and obtained a recognition accuracy of 63% for the sequential motions. Furthermore, the results of motion generation experiments performed with a robot arm are presented.

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Komei Sugiura, Naoto Iwahashi
• Keywords: IROS 2008, Object Manipulation, Reference-point-dependent Probabilistic Models, Robotics, Sequential Motions