基于对抗性运动先验学习行走与飞行
Learning to Walk and Fly with Adversarial Motion Priors
AI总结：
本研究提出基于对抗性运动先验的强化学习方法，使机器人无需复杂奖励函数即可模仿数据集学习行走与飞行模式，实现足式与空中运动的自动平滑过渡及多模态运动。
摘要：
机器人多模态运动涵盖了在行走和飞行之间切换的能力，代表了机器人领域的一项重大挑战。本研究提出了一种方法，能够实现足式和空中运动之间的自动平滑过渡。利用对抗性运动先验的概念，我们的方法允许机器人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。通过这一过程，机器人利用强化学习根据环境反馈调整运动方案，并自发产生模式切换行为。结果突出了通过自动控制行走和飞行模式在空中人形机器人中实现多模态运动的潜力，为搜救、监控和探索任务等多样化领域的应用铺平了道路。本研究有助于提升空中人形机器人在各种环境中多功能运动的能力。
英文摘要：
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.
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