Abstract
Quadrupedal robots resemble the physical ability of legged animals to walk through unstructured terrains. However, designing a controller for quadrupedal robots poses a significant challenge due to their functional complexity and requires adaptation to various terrains. Recently, deep reinforcement learning, inspired by how legged animals learn to walk from their experiences, has been utilized to synthesize natural quadrupedal locomotion. However, state-of-the-art methods strongly depend on a complex and reliable sensing framework. Furthermore, prior works that rely only on proprioception have shown a limited demonstration for overcoming challenging terrains, especially for a long distance. This work proposes a novel quadrupedal locomotion learning framework that allows quadrupedal robots to walk through challenging terrains, even with limited sensing modalities. The proposed framework was validated in real-world outdoor environments with varying conditions within a single run for a long distance.
Abstract (translated)
多足机器人类似于有腿动物穿越无序地形的能力。然而,设计多足机器人控制器面临着巨大的挑战,因为它们的功能复杂性需要适应各种地形。最近,基于有腿动物从经验中学习行走的深度学习被应用于合成自然多足行走。然而,最先进的方法强烈依赖于复杂的、可靠的感知框架。此外,仅依赖感觉移動的先前工作在克服挑战性地形、特别是长距离方面表现出有限的演示。这项工作提出了一种新的多足行走学习框架,使多足机器人能够穿越挑战性地形,即使感知模式有限。该框架在真实室外环境中、在一次运行中跨越长距离的情况下进行了验证。
URL
https://arxiv.org/abs/2301.10602