Imitation of human body poses with a fluid based controller

In this work, we aim at imitation of two system with totally different dynamics, imitating each other, where any correspondence is missing. Towards this aim, we adopt a case where the imitator is a fluidic system whose dynamics is totally different than the imitatee, that is a human performing different body poses. Our work proposes the fluidics formation control of fluid particles where the formation results from the imitation of observed human body poses. Fluidic formation control layer is responsible of assigning the correct fluid parameters to the swarm formation layer according to the body poses adopted by the human performer. The movement of the fluid particles is modeled using the Smoothed Particle Hydrodynamics (SPH) which is a particle based Lagrangian method for simulation of fluid flows. The region based controller first extract the human body parts generating the regions where the attention is attracted by the imitatee and fits an appropriate ellipses to delimited boundaries of those regions. The ellipse parameters such as center of the ellipses, eccentricity, length of the major and minor axis etc. are used by the fludic layer in order to generate human body poses.

[1]  Darwin G. Caldwell,et al.  Evaluation of a probabilistic approach to learn and reproduce gestures by imitation , 2010, 2010 IEEE International Conference on Robotics and Automation.

[2]  A.M. Erkmen,et al.  Imitation of basic hand preshapes by fluid based method: Fluidic formation control , 2009, 2009 International Conference on Electrical and Electronics Engineering - ELECO 2009.

[3]  Aude Billard,et al.  LEARNING MOTOR SKILLS BY IMITATION: A BIOLOGICALLY INSPIRED ROBOTIC MODEL , 2001, Cybern. Syst..

[4]  Sridha Sridharan,et al.  3D ellipsoid fitting for multi-view gait recognition , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[5]  Guirong Liu,et al.  Smoothed Particle Hydrodynamics: A Meshfree Particle Method , 2003 .

[6]  Ismet Erkmen,et al.  Control of robotic swarm behaviors based on smoothed particle hydrodynamics , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Chrystopher L. Nehaniv,et al.  Imitation with ALICE: learning to imitate corresponding actions across dissimilar embodiments , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[8]  Zhe Zhang,et al.  Human Body Pose Interpretation and Classification for Social Human-Robot Interaction , 2011, Int. J. Soc. Robotics.

[9]  W. Eric L. Grimson,et al.  Gait analysis for recognition and classification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[10]  R. Mesquita,et al.  Fluids in Electrostatic Fields: An Analogy for Multirobot Control , 2007, IEEE Transactions on Magnetics.

[11]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.