Publication

Learning physical collaborative robot behaviors from human demonstrations

Journal Article (2016)

Journal

IEEE Transactions on Robotics

Pages

513-527

Volume

32

Number

3

Doc link

http://dx.doi.org/10.1109/TRO.2016.2540623

File

Download the digital copy of the doc pdf document

Abstract

Robots are becoming safe and smart enough to work alongside people not only on manufacturing production lines, but also in spaces such as houses, museums, or hospitals. This can be significantly exploited in situations in which a human needs the help of another person to perform a task, because a robot may take the role of the helper. In this sense, a human and the robotic assistant may cooperatively carry out a variety of tasks, therefore requiring the robot to communicate with the person, understand his/her needs, and behave accordingly. To achieve this, we propose a framework for a user to teach a robot collaborative skills from demonstrations. We mainly focus on tasks involving physical contact with the user, in which not only position, but also force sensing and compliance become highly relevant. Specifically, we present an approach that combines probabilistic learning, dynamical systems, and stiffness estimation to encode the robot behavior along the task. Our method allows a robot to learn not only trajectory following skills, but also impedance behaviors. To show the functionality and flexibility of our approach, two different testbeds are used: a transportation task and a collaborative table assembly.

Categories

intelligent robots, robot programming, robots.

Author keywords

Robot learning, programming by demonstration, physical human-robot interaction, stiffness estimation

Scientific reference

L. Rozo, S. Calinon, D. Caldwell, P. Jiménez and C. Torras. Learning physical collaborative robot behaviors from human demonstrations. IEEE Transactions on Robotics, 32(3): 513-527, 2016.