4.4 Article

Parental scaffolding as a bootstrapping mechanism for learning grasp affordances and imitation skills

期刊

ROBOTICA
卷 33, 期 5, 页码 1163-1180

出版社

CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0263574714002148

关键词

Developmental robotics; Affordance; Imitation; Parental scaffolding; Motionese

类别

资金

  1. European Community's Seventh Framework Programme [270273, 321700]
  2. JSPS/MEXT [24000012, 24119003]
  3. Ministry of Internal Affairs and Communications, Japan
  4. Grants-in-Aid for Scientific Research [24000012, 25700027, 24119003] Funding Source: KAKEN

向作者/读者索取更多资源

Parental scaffolding is an important mechanism that speeds up infant sensorimotor development. Infants pay stronger attention to the features of the objects highlighted by parents, and their manipulation skills develop earlier than they would in isolation due to caregivers' support. Parents are known to make modifications in infant-directed actions, which are often called motionese. 7 The features that might be associated with motionese are amplification, repetition and simplification in caregivers' movements, which are often accompanied by increased social signalling. In this paper, we extend our previously developed affordances learning framework to enable our hand-arm robot equipped with a range camera to benefit from parental scaffolding and motionese. We first present our results on how parental scaffolding can be used to guide the robot learning and to modify its crude action execution to speed up the learning of complex skills. For this purpose, an interactive human caregiver-infant scenario was realized with our robotic setup. This setup allowed the caregiver's modification of the ongoing reach and grasp movement of the robot via physical interaction. This enabled the caregiver to make the robot grasp the target object, which in turn could be used by the robot to learn the grasping skill. In addition to this, we also show how parental scaffolding can be used in speeding up imitation learning. We present the details of our work that takes the robot beyond simple goal-level imitation, making it a better imitator with the help of motionese.

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