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.

中文翻译：

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DreamWaQ：通过深度强化学习学习具有隐式地形想象的稳健四足运动

四足机器人类似于有腿动物在非结构化地形中行走的身体能力。然而，由于四足机器人功能复杂且需要适应各种地形，为四足机器人设计控制器是一项重大挑战。最近，受有腿动物如何从经验中学会走路的启发，深度强化学习已被用于合成自然的四足运动。然而，最先进的方法强烈依赖于复杂而可靠的传感框架。此外，先前仅依赖本体感觉的作品在克服具有挑战性的地形方面表现出有限的示范，尤其是在长距离的情况下。这项工作提出了一种新颖的四足运动学习框架，允许四足机器人在具有挑战性的地形中行走，即使使用有限的传感方式。所提出的框架在现实世界的室外环境中得到验证，在长距离的单次运行中具有不同的条件。