/intelligent-control-techniques-for-robots

An analysis of different intelligent control techniques (evolutionary alg., reinf. learn., neural networks) applied to robotic manipulators.

Primary LanguageJupyter Notebook

Intelligent Control Techniques for Robot Manipulators.

An analysis of different intelligent control techniques (evolutionary alg., reinf. learn., neural networks) applied to robot manipulator.

Here you can find the blogposts describing this work: https://medium.com/towards-data-science/making-a-robot-learn-of-to-move-intro-2bcf3c3330df.

WORK IN PROGRESS

Work by Norman Di Palo and Tiziano Guadagnino.

We explore the performance of different AI/Machine Learning techniques applied to trajectory tracking for a 3R robotic manipulator, whose dynamical model is unknown. Thus, the robot needs to learn of to precisely follow a trajectory to decrease the position and velocity error. Our baseline is a classic PD controller.

You can find the code for Evolutionary Algorithms applied to neural network controllers and reinforcement learning techniques such as Q-learning for Dynamic PD Tuning and Actor Critic for deterministic policy learning, and then Inverse Dynamic Model Learning by Feedback-Backpropagation.