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Learning DS Motion Policies from Demonstrations

Fill in from ds-ltl and ds-opt packages.

Using a Learned DS as a Motion Policy for Robot Control

Once you have verified that the DSs were learned correctly (and exhibit the desired behavior) we can use them as a motion policy to the control the end-effector of a real robot. There are several ways to accomplish this. These are listed below:

  • lpvDS-lib: This repository contains a standalone C++ library that reads the parameters of the learned DS, in either yaml/txt format and can compile the library either with ROS-ified catkin_make or pure cmake compilation commands. This is the preferred library if you are NOT using ROS to control your robot and use/like C++.
  • ds_motion_generator [Preferred]: This repository is a ROS package that includes nodes for DS motion generation of different types (linear DS, lpv-ds, lags-ds, etc,). It depends on the standalone lpvDS-lib library and uses yaml files to load the parameters to ros node. This node generates the desired velocity from the learned DS (given the state of the robot end-effector) and also filters and truncates the desired velocity (if needed). The node also generates a trajectory roll-out (forward integration of the DS) to visualize the path the robot will follow -- this is updayed in realtime at each time-step.
  • ds-opt-py: This is an experimental python package that includes a python library to read parameters in yaml format for the execution of the lpv-DS, ROS integration is yet to be done, but should be straightforward. However, filtering and smoothing should be done separately.