Journal
ADVANCED ROBOTICS
Volume 23, Issue 15, Pages 2059-2076Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1163/016918609X12529294461843
Keywords
Robot programming by demonstration; learning by imitation; kinesthetic teaching; Gaussian mixture regression; inverse kinematics
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We present a probabilistic architecture for solving generically the problem of extracting the task constraints through a programming by demonstration framework and for generalizing the acquired knowledge to various situations. In previous work, we proposed an approach based on Gaussian mixture regression to find a controller for the robot reproducing the statistical characteristics of a movement in joint space and in task space through Lagrange optimization. In this paper, we develop an alternative procedure to handle simultaneously constraints in joint space and in task space by combining directly the probabilistic representation of the task constraints with a solution to Jacobian-based inverse kinematics. The method is validated in manipulation tasks with two 5-d.o.f. Katana robotic arms displacing a set of objects. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2009
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