This repository contains the main source code to perform a detailed evaluation of distal teaching, a machine learning approach to solve the inverse kinematics problem, under known forward kinematics. For three serial and rigid mechanisms of different complexity (3-DoF (planar), 6-DoF and 15-DoF), the provided code enables to train and evaluate an inverse kinematic model via distal teaching using Keras and TensorFlow. Further, analytical solutions are provided in native Python for the 3-DoF and 6-DoF mechanisms and the numerical solver TRAC-IK can be used for all three mechanisms as a comparison. The results in Comparison of Distal Teacher Learning with Numerical and Analytical Methods to Solve Inverse Kinematics for Rigid-Body Mechanisms are based on this implementation.
- The folder 2D/ contains a very basic implementation of distal teaching specifically for planar arms.
- The folder 3D/ contains a more general implementation of distal teaching suited for the three-dimensional case.
For training and evaluating an inverse kinematic model via distal teaching, Python 3 is mandatory. The required libraries can be found in requirements_distal_teaching.txt.
The numerical solver TRAC-IK depends on ROS which works best with Python 2.7.
If you want to solve the inverse kinematic problem for query poses with TRAC-IK via the script solve_with_trac_ik.py, install trac_ik_python by running sudo apt-get install ros-melodic-trac-ik (if your distribution differs, insert it instead of "melodic").
Before you run solve_with_trac_ik.py, make sure to use roslaunch with a launch file and a fitting urdf file (subfolder 2D/urdf/ or 3D/urdf/) as an argument. As an example launch file, example.launch is given.
First of all, training and testing data has to be generated. To do so, run the script generate_data.py (from folder 2D/ or 3D/) that stores the datasets in the subfolder data/. At the top of the script generate_data.py, there are a few modifiable parameters. After storing training and testing data, an inverse kinematic model (feed-forward neural network) can be trained by running distal_teaching.py. As soon as the training is finished, the model will be stored in the subfolder models/. Furthermore, some basic evaluation metrics are printed after the training.
As a comparison, the generated test dataset can also be solved using the numerical solver TRAC-IK by running solve_with_trac_ik.py. Note that this requires a launched urdf file via roslaunch.
An evaluation of the solutions by TRAC-IK can be performed by running numeric_eval.py.