Realization of sign language motion using a dual-arm/hand humanoid robot

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• 作者：Sheng-Yen Lo ; Han-Pang Huang
• 关键词：Human–robot interaction ; Sign language ; Motor resonance ; Robot imitation ; Self ; collision avoidance ; Virtual impedance control
• 刊名：Intelligent Service Robotics
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：9
• 期：4
• 页码：333-345
• 全文大小：2,551 KB
• 刊物类别：Engineering
• 刊物主题：Automation and Robotics
  Control Engineering
  Artificial Intelligence and Robotics
  User Interfaces and Human Computer Interaction
  Vibration, Dynamical Systems and Control
  Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1861-2784
• 卷排序：9

文摘

The recent increase in technological maturity has empowered robots to assist humans and provide daily services. Voice command usually appears as a popular human–machine interface for communication. Unfortunately, deaf people cannot exchange information from robots through vocal modalities. To interact with deaf people effectively and intuitively, it is desired that robots, especially humanoids, have manual communication skills, such as performing sign languages. Without ad hoc programming to generate a particular sign language motion, we present an imitation system to teach the humanoid robot performing sign languages by directly replicating observed demonstration. The system symbolically encodes the information of human hand–arm motion from low-cost depth sensors as a skeleton motion time-series that serves to generate initial robot movement by means of perception-to-action mapping. To tackle the body correspondence problem, the virtual impedance control approach is adopted to smoothly follow the initial movement, while preventing potential risks due to the difference in the physical properties between the human and the robot, such as joint limit and self-collision. In addition, the integration of the leg-joints stabilizer provides better balance of the whole robot. Finally, our developed humanoid robot, NINO, successfully learned by imitation from human demonstration to introduce itself using Taiwanese Sign Language.