Reinforcement Learning for Elliptical Cylinder Motion Control Tasks
Pawel Marczewski, Paulina Superczynska, Jakub Bernat, Szymon Szczesny

TL;DR
This paper explores using reinforcement learning to control the motion of an elliptical cylinder with limited torque, comparing it to classical control methods and analyzing its effectiveness in various scenarios.
Contribution
It introduces a reinforcement learning approach for elliptical cylinder control under torque constraints and compares it with traditional energy-shaping and LQR methods.
Findings
Reinforcement learning can effectively control the elliptical cylinder.
Classical controllers serve as strong baselines for comparison.
Control difficulty increases with mass and shape asymmetry.
Abstract
The control of devices with limited input always bring attention to solve by research due to its difficulty and non-trival solution. For instance, the inverted pendulum is benchmarking problem in control theory and machine learning. In this work, we are focused on the elliptical cylinder and its motion under limited torque. The inspiration of the problem is from untethered magnetic devices, which due to distance have to operate with limited input torque. In this work, the main goal is to define the control problem of elliptic cylinder with limited input torque and solve it by Reinforcement Learning. As a classical baseline, we evaluate a two-stage controller composed of an energy-shaping swing-up law and a local Linear Quadratic Regulator (LQR) stabilizer around the target equilibrium. The swing-up controller increases the system's mechanical energy to drive the state toward a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsControl and Stability of Dynamical Systems · Piezoelectric Actuators and Control · Model Reduction and Neural Networks
