Learning Hand Movements from Markerless Demonstrations for Humanoid Tasks

Learning Hand Movements from Markerless Demonstrations for Humanoid Tasks

Title : Learning Hand Movements from Markerless Demonstrations for Humanoid Tasks
Authors :
Aloimones, Yiannisc
Yang, Yezhou
Mao, Ren
Fermuller, Cornelia
Baras, John, S.
Conference : 2014 IEEE-RAS International Conference on Humanoid Robots (Humanoids 2014) pp. 938-943
Date: November 18 - November 20, 2014

We present a framework for generating trajectories of the hand movement during manipulation actions from demonstrations so the robot can perform similar actions in new situations. Our contribution is threefold: 1) we extract and transform hand movement trajectories using a state-of-the-art markerles full hand model tracker from Kinect sensor data; 2)we develop a new bio-inspired trajectory segmentation method that automatically segments complex movements into action units, and 3) we develop a generative method to learn task specific control using Dynamic Movement Primitives (DMPs).Experiments conducted both on synthetic data and real data using the Baxter research robot platform validate our approach.

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