Sheng-Wen Cheng
Sheng-Wen (Colin) Cheng is a roboticist and systems software engineer who builds low-level software at the intersection of robotics, control engineering, and real-time systems.
He is also an active open-source contributor and has presented his work at leading open-source events, including Open Source Summit organized by the Linux Foundation.
For more information, please visit shengwen-tw.github.io
Sessions
This talk introduces the basic ideas behind quadrotor optimal control and reinforcement learning from a practical, beginner-friendly perspective. We will discuss how position, velocity, attitude, and angular velocity are used to describe the drone state, and how a controller converts tracking errors into motor commands.
Several representative control approaches will be introduced, including the Linear Quadratic Regulator (LQR), H∞ robust control, Model Predictive Control (MPC), and Reinforcement Learning (RL). The talk will explain the intuition behind each method: what it tries to optimize, what kinds of problems it handles well, and what trade-offs appear in practice.
The examples will be based on RotorBench, an open-source Python-based quadrotor control benchmarking framework. More details about the project are available at: https://github.com/shengwen-tw/rotor-bench