Learning From Demonstration

In this work at first, I recognize the object in the scene and estimate the 6 DOF pose of that. Then I track the object by using particle filter. RGB data acquired from Kinect 2 and turned into PCL pointcloud.
I demonstrate a task several times to the robot. In this case, I move an object (a detergent) over an “S” shape path to get an “S” shape trajectory.

In the following, you can see the result of 6 times repeating the job. Trajectories are very noisy and each repeat gives you a new “S” shape.
Then I compute the GMM (Gaussian mixture model) of trajectory in each dimension. Numbers of kernels can be set by the user or can be determined automatically based on BIC (Bayesian information criterion).
After that, I computed the Gaussian mixture regression to generalize the task and get the learned trajectory.
DTW (Dynamic time warping) can be used to align the trajectories to a reference trajectory to get rid of time difference problem between several trajectories.

Finally, you can see the learned trajectory in black.

All codes have been done with C++ in ROS (indigo-level).



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