research product . 2014

Learning interaction for collaborative tasks with probabilistic movement primitives

Maeda, G.; Ewerton, M.; Lioutikov, R.; Ben Amor, H.; Peters, J.; Neumann, G.;
Open Access English
  • Published: 18 Nov 2014
  • Country: United Kingdom
Abstract
This paper proposes a probabilistic framework based on movement primitives for robots that work in collaboration with a human coworker. Since the human coworker can execute a variety of unforeseen tasks a requirement of our system is that the robot assistant must be able to adapt and learn new skills on-demand, without the need of an expert programmer. Thus, this paper leverages on the framework of imitation learning and its application to human-robot interaction using the concept of Interaction Primitives (IPs). We introduce the use of Probabilistic Movement Primitives (ProMPs) to devise an interaction method that both recognizes the action of a human and gener...
Subjects
free text keywords: G760 Machine Learning, H671 Robotics
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