Summary
To cope with constant and unexpected changes in their environment, robots need to adapt their paths rapidly and appropriately without endangering humans. this course presents method to react within millisecunds.
Content
This course presents methods by which robots can learn control laws using machine learning. On-line reactivity is not just a matter of ensuring enough CPU on-board of the robot. It requires inherently robust control laws that can provide a multiplicity of solutions. In this course, we will see methods based on dynamical systems theory. Dynamical systems-based control law offer closed-form solution, hence with no need for further optimization at run time, and with convergence and stability guarantees. We will see applications of these methods for manipulation and navigation of robot arm manipulator and full body humanoid robots.
Topics include:
- Learning control laws with stability guarantees
- Synchronizing control laws and applications (multi-joint control, catching objects in flight)
- Modifying control laws for safe obstacle avoidance
- Learning force and impedance control for robust manipulation
- Professor: Aude Billard
