TRIPODS Data Science Boot Camp:
Dynamics, Data Analysis, and Robotic Control

Monday, Jan 10 – Thursday, Jan 13. Time: 4pm EST

Ewerton Viera & Cameron Thieme

with Edgar Granados, Aravind Sivaramakrishnan, Yao Song

Overview: Dynamical systems is the mathematics that explains how systems change over time, playing a key role in a wide range of fields: physics, biology, economics, robotics, and more. In many modern applications we often have large quantities of data that monitor the changes in these systems and we need to be able to interpret this data in a robust and accurate way. This series of interactive workshops will introduce students to this process, covering the basics of dynamical systems, the Morse decomposition, and learning functions from data. These ideas will culminate in an example that demonstrates how these concepts allow us to evaluate and build controls for robotic systems such as the motion of an arm.

Intended audience: These sessions are aimed for all students with a curiosity about the relationship of mathematics and computation in analyzing data about a changing world. We welcome undergraduates and graduate students, and invite interested students from computer science, mathematics, statistics and physics and related disciplines. To follow along with the interactive sessions, students should be familiar with basic programming (we will use Python, but the code is simple enough that those who have used other languages should not have many difficulties). Knowledge of basic calculus concepts is also expected

Schedule and Participation:

The sessions will be held over zoom. Registration is free and open but required:

1. January 10, 4pm: Introduction to Dynamical Systems and Combinatorial Dynamics
– Basic ideas and applications.
– Continuous dynamics and iterated maps.
– Discretization of phase space.
Watch the Recording (pass: ?g&BL7Zw)

2. January 11, 4pm: Morse Decomposition and Global Dynamics
– Lattice of Attracting Blocks.
– Global Dynamics (Morse Decomposition).
– Apply concepts to robotics examples.
Watch the Recording (pass: $0V&^B#Q)

3. January 12, 4pm: Learned Functions.
– Machine Learning.
– Gaussian Processes.
Watch the Recording (pass: 78bjk7i@)

4. January 13, 4pm: Applications to Robotic Controls.
– Compare the different approaches: Analytical and Learned controllers.
– Combinatorial dynamics as a guide to create hybrid controllers.
Watch the Recording (pass: ztSx*J8U)