Quadrupedal Robot Skateboard Mounting via Reverse Curriculum Learning
This addresses a specific locomotion task for robotics researchers, but it is incremental as it builds on prior skateboarding work.
The paper tackled the problem of enabling quadrupedal robots to mount skateboards, which was previously challenging, and achieved successful transfer to scenarios with mobile skateboards using Reverse Curriculum Reinforcement Learning.
The aim of this work is to enable quadrupedal robots to mount skateboards using Reverse Curriculum Reinforcement Learning. Although prior work has demonstrated skateboarding for quadrupeds that are already positioned on the board, the initial mounting phase still poses a significant challenge. A goal-oriented methodology was adopted, beginning with the terminal phases of the task and progressively increasing the complexity of the problem definition to approximate the desired objective. The learning process was initiated with the skateboard rigidly fixed within the global coordinate frame and the robot positioned directly above it. Through gradual relaxation of these initial conditions, the learned policy demonstrated robustness to variations in skateboard position and orientation, ultimately exhibiting a successful transfer to scenarios involving a mobile skateboard. The code, trained models, and reproducible examples are available at the following link: https://github.com/dancher00/quadruped-skateboard-mounting