Abstract. 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 show a complete pipeline of design, control, and sim-to-real transfer of a multi-modal legged robot. We 1) design an additional supporting structure for a quadruped robot, 2) evaluate a class of novel hybrid learning-based algorithms combining different reinforcement learning algorithms (Twin Delayed Deep Deterministic Policy Gradients and Soft Actor Critic) and black-box parameter optimisers (Evolutionary Strategy and Bayesian Optimisation), and 3) propose a sim-to-real transfer technique. We use parameter optimisers to tune a conventional feedback controller simultaneously with the training of an RL agent that solves the residual task. Experimental results show that our proposed algorithms have the best performance in simulation and competitive performance on the real robot with our sim-to-real technique. Overall, our multi-modal robot could successfully switch between biped and quadruped, and walk in both modes.