A Practical Multi-Body Model Enabling a Flexible-Wheeled Robot to Learn Blind Stair Climbing

Abstract

Controlling a flexible wheeled robot for complex tasks such as stair climbing is highly challenging. The nonlinearity inherent in soft materials hinders accurate modeling, creating a trade-off in Reinforcement Learning (RL) between simulation fidelity and learning speed. We propose an RL-friendly, multi-body model that approximates the deformation of the flexible wheel as a Mass-Spring-Damper (MSD) system composed of rigid links and joints. This model enables end-to-end RL within a fast rigid-body simulator, facilitating a blind control policy that relies solely on proprioceptive feedback. To reduce the reality gap and enhance policy robustness, we randomize the main parameters of the MSD system. In real-world experiments, a robot successfully climbed an 18 cm step, corresponding to approximately 51% of the wheel radius—a feat impossible for a rigid-wheeled equivalent. To our knowledge, this is the first successful application of RL-based blind control for stair climbing with a flexible wheeled robot. However, structural limitations in our model and challenges in parameter identification hinder sim-to-real transfer, and improving robustness remains a key issue for future work.