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相关概念视频

Muscles of the Leg that Move the Foot and Toes01:28

Muscles of the Leg that Move the Foot and Toes

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The human leg comprises an intricate system of muscles that facilitate the movement of feet and toes. Within this system, the muscles are categorized into the anterior, lateral, and posterior compartments, each with a unique set of muscles carrying out specific functions.
Anterior Compartment
The anterior compartment includes muscles that contribute to the dorsiflexion of the foot. This compartment houses the tibialis anterior, extensor hallucis longus, and extensor digitorum longus muscles....
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Muscles that Move the Leg01:23

Muscles that Move the Leg

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The movement of the legs is facilitated by numerous muscles located within the anterior, medial, and posterior compartments of the thigh.
Anterior Compartment
The quadriceps femoris, the most visible muscle of the anterior compartment, is integral for leg extension and thigh flexion. It is formed by merging four distinct muscles — the vastus lateralis, vastus medialis, vastus intermedius, and rectus femoris. The quadriceps tendon, a shared tendon of the four quadriceps muscles, is affixed...
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相关实验视频

Updated: Sep 10, 2025

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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基于注意力的地图编码用于学习通用腿部运动

Junzhe He1, Chong Zhang1, Fabian Jenelten1

  • 1Robotic Systems Lab, ETH Zurich, 8092 Zurich, Switzerland.

Science robotics
|August 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了基于注意力的机器人控制器, 增强了在不同地形上的动态移动. 这种方法提高了机器人在充满挑战的环境中驾驶的稳定性和精度.

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Studying the Neural Basis of Adaptive Locomotor Behavior in Insects
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相关实验视频

Last Updated: Sep 10, 2025

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科学领域:

  • 机器人技术
  • 人工智能
  • 控制系统

背景情况:

  • 腿类机器人的动态运动对于扩大移动机器人的能力至关重要,但在不同地形上面临着精度和强度的挑战.
  • 传统的基于模型的控制器对现实世界的不确定性缺乏稳定性,而基于学习的控制器可能在稀疏的地形上缺乏精度.
  • 混合方法结合了方法,但计算密集,并受到基于模型的规划者的限制.

研究的目的:

  • 开发一款通用腿式机动控制器,
  • 利用注意力机制和强化学习来改善地形感知和立足规划.
  • 在复杂的环境中实现敏捷和强大的动态移动.

主要方法:

  • 提出了基于注意力的地图编码, 基于机器人自身感知, 使用强化学习进行训练.
  • 开发了一种新型控制器,将基于神经网络的地形感知与动态移动规划相结合.
  • 训练有素的四足机器人和人形机器人, 证明了他们的适应能力.

主要成果:

  • 基于注意力的网络学会了在动态导航中有效地识别可步行区域.
  • 合成的行为表现出对不确定性的强度, 并使精确,敏捷地穿越稀疏地形.
  • 这种方法为神经网络的地形感知提供了可解释的见解.

结论:

  • 拟议的基于注意力的控制器可以在多样化和具有挑战性的地形上实现腿部机器人的通用和强大的动态移动.
  • 这种方法提高了精度和灵活性,克服了传统和纯粹基于学习的方法的局限性.
  • 在四足和人形机器人实验中验证了控制器在各种情况下的有效性,