This package implements Dynamic Motion Primitives for Learning from Demonstration. This package was developed during the R and D project as part of academics. Detailed report on analysis, implementation and use of this package can be found at https://github.com/abhishek098/r_n_d_report/blob/master/PadalkarAbhishek-%5BRnD%5DReport.pdf .
Reference : Auke Jan Ijspeert, Jun Nakanishi, Heiko Hoffmann, Peter Pastor, and Stefan Schaal. Dynamical movement primitives: learning attractor models for motor behaviors. Neural computation, 25(2):328{373, 2013}.
Clone the repository:
git clone https://github.com/abhishek098/ros_dmp.git
Build the package:
Inside the package
catkin build --this
This package provides two services:
- Service for learning the DMP
- Service for genearating motion from already learnt DMP
Geerated motion is published as navigation path on the topic:
/generate_motion_service_node/cartesian_path
And as cartesian trajectory on the topic:
/generate_motion_service_node/cartesian_trajectory
For more details about using these services, have a look at example clients in example folder.
Running learn DMP and generate motion clients
Launch the DMP simulation launch file in one terminal using following command.
roslaunch ros_dmp dmp_sim.launch
Run rviz to visualize learned and demonstrated path published on following topics which can be visualized in rviz.
/learn_dmp_service_node/demonstrated_path
/learn_dmp_service_node/demonstrated_path
In another terminal, run learn client script in example folder
python learn_client.py
This script calls the learn DMP service for imitating a artificial trajectory.
Observe the imitated and demonstrated trajectories in rviz.
In another terminal, run generate motion client
python motion_generation_client.py
Genrated motion can be visualized in rviz on following topic:
/generate_motion_service_node/cartesian_path