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之间的转换能力，这代表了机器人学领域的重大挑战。本研究提出一种能够实现腿式运动与空中运动间自动平滑转换的方法。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目标任务，而无需设计复杂的奖励函数。机器人从类人步态数据中学习行走模式，并通过轨迹优化获得的运动数据学习空中运动模式。在此过程中，机器人基于环境反馈通过强化学习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行走与飞行模式的自动控制在空中仿人机器人领域实现多模态运动的潜力，为搜救、监视和勘探任务等多样化应用场景开辟了道路。本研究通过提升空中仿人机器人在复杂环境中的多场景运动能力，推动了该领域的技术发展。