Deep Reinforcement Learning for Coordinated Payload Transport in Biped-Wheeled Robots

Abstract

Coordinated payload transport via a fleet of modular wheeled mobile robots offers flexibility for handling larger loads in indoor and outdoor environments. Biped-wheeled robots have recently emerged as a viable architecture for an independent/stand-alone wheeled mobile robot. In this work, we explore the use of two biped-wheeled robots that can leverage their mobility and maneuvarability for enhanced spatial pose control and stabilization for various payload transport tasks. However, coordinated control of multiple articulated wheeled robots for path tracking of a payload presents significant (and potentially competing) challenges, including kinematic redundancy, stability concerns, relative motion between the payload and robots, and precise motion control to achieve effective coordination. To address these challenges, we propose a Deep Reinforcement Learning (DRL) framework to develop the motion-plans for the system. In particular, this approach generates the ego robot's body twist and the follower robot's relative twist with respect to the ego robot. By formulating the action space of the follower robot as a relative twist, our approach facilitates pairwise interactions between robots. Furthermore, we use only relative pose information and the errors as states for the DRL controller, thereby making it agnostic to initial conditions and avoiding explicit dependency on absolute pose. We validate our approach through simulations conducted in Isaac Sim and on hardware using Diablo biped-wheeled robots with zero-shot transfer, demonstrating effective payload path tracking across varying parameters.