Robot multimodal locomotion encompasses the ability to transition between walking and flying, representing a significant challenge in robotics. This work presents an approach that enables automatic smooth transitions between legged and aerial locomotion. Leveraging the concept of Adversarial Motion Priors, our method allows the robot to imitate motion datasets and accomplish the desired task without the need for complex reward functions. The robot learns walking patterns from human-like gaits and aerial locomotion patterns from motions obtained using trajectory optimization. Through this process, the robot adapts the locomotion scheme based on environmental feedback using reinforcement learning, with the spontaneous emergence of mode-switching behavior. The results highlight the potential for achieving multimodal locomotion in aerial humanoid robotics through automatic control of walking and flying modes, paving the way for applications in diverse domains such as search and rescue, surveillance, and exploration missions. This research contributes to advancing the capabilities of aerial humanoid robots in terms of versatile locomotion in various environments.

翻译：机器人多模态运动涵盖行走与飞行的转换能力，这是机器人学中的一项重大挑战。本文提出一种方法，使机器人能够在腿式运动与空中运动之间实现自动平滑过渡。通过利用对抗性运动先验的概念，我们的方法使机器人能够模仿运动数据集并完成期望任务，而无需复杂的奖励函数。机器人从类人步态中学习行走模式，并从通过轨迹优化获得的运动中学习空中运动模式。在此过程中，机器人利用强化学习根据环境反馈自适应调整运动方案，并自发涌现出模式切换行为。研究结果凸显了通过自动控制行走与飞行模式实现空中类人机器人多模态运动的潜力，为搜索救援、监控探测等多样化领域的应用铺平道路。本研究有助于提升空中类人机器人在不同环境中实现多形态运动的能力。