John S. Baras

2014

Learning Hand Movements from Markerless Demonstrations for Humanoid Tasks

R.Mao, Y. Yang, C. Fermuller, Y. Aloimones, J. Baras

To appear in the Proceedings of the 2014 IEEE-RAS International Conference on Humanoid Robots (Humanoids 2014), Madrid, Spain, November 18-20, 2014.

Full Paper (.pdf)

Abstract

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 markerless 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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