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English(EN) Agile perceptive multi-skill locomotion for quadrupedal robots in the wild

新的APT-RL框架实现了机器人敏捷的多技能运动

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了APT-RL，一个用于四足机器人的新颖框架，该框架能够实现复杂地形中的敏捷多技能运动。该系统利用基于Transformer的强化学习方法，利用板载感知和计算自主地在不同步态之间切换。该框架已在现实世界实验中证明了其有效性，使机器人能够以高达每秒6米的速度执行动态机动并穿越各种障碍物。 AI

影响 这项研究可能带来更通用、更有能力的机器人来执行复杂的环境任务。

排序理由 详细介绍机器人新AI框架的研究论文。

在 arXiv cs.AI 阅读 →

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新的APT-RL框架实现了机器人敏捷的多技能运动

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jun-Gill Kang, Jaehyun Park, Tae-Gyu Song, Joon-Ha Kim, Seungwoo Hong, Hae-Won Park ·

    野外四足机器人敏捷感知多技能运动

    arXiv:2607.13579v1 Announce Type: cross Abstract: Enabling quadrupedal robots to traverse complex terrains-from rugged outdoor environments to urban landscapes-requires seamless integration of multiple motor skills, smooth transitions between gaits, and high-speed perceptive loco…

  2. arXiv cs.AI TIER_1 English(EN) · Hae-Won Park ·

    野外四足机器人敏捷感知多技能运动

    Enabling quadrupedal robots to traverse complex terrains-from rugged outdoor environments to urban landscapes-requires seamless integration of multiple motor skills, smooth transitions between gaits, and high-speed perceptive locomotion using only onboard sensors. We present APT-…

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