  While quadruped robots usually have good stability and load capacity, bipedal
robots offer a higher level of flexibility / adaptability to different tasks
and environments. A multi-modal legged robot can take the best of both worlds.
In this paper, we propose a multi-modal locomotion framework that is composed
of a hand-crafted transition motion and a learning-based bipedal controller --
learnt by a novel algorithm called Automated Residual Reinforcement Learning.
This framework aims to endow arbitrary quadruped robots with the ability to
walk bipedally. In particular, we 1) design an additional supporting structure
for a quadruped robot and a sequential multi-modal transition strategy; 2)
propose a novel class of Reinforcement Learning algorithms for bipedal control
and evaluate their performances in both simulation and the real world.
Experimental results show that our proposed algorithms have the best
performance in simulation and maintain a good performance in a real-world
robot. Overall, our multi-modal robot could successfully switch between biped
and quadruped, and walk in both modes. Experiment videos and code are available
at https://chenaah.github.io/multimodal/.
