CycleRL: Sim-to-Real Deep Reinforcement Learning for Robust Autonomous Bicycle Control

Description

Autonomous bicycles offer a promising agile solution for urban mobility and last-mile logistics, however, conventional control strategies often struggle with their underactuated nonlinear dynamics, suffering from sensitivity to model mismatches and limited adaptability to real-world uncertainties. To address this, this paper presents CycleRL, the first sim-to-real deep reinforcement learning framework designed for robust autonomous bicycle control. Our approach trains an end-to-end neural contro

Source