论文标题
人形步行步态中强大的接触状态估计
Robust Contact State Estimation in Humanoid Walking Gaits
论文作者
论文摘要
在本文中，我们提出了一个深度学习框架，该框架为人类机器人步行步态中的腿部接触检测问题提供了统一的方法。我们的配方实现了准确，稳健地估计每条腿的接触状态概率（即稳定或滑动/无接触）。所提出的框架采用了仅本体感知感应，尽管它依赖于模拟的地面真实接触数据进行分类过程，但我们证明了它在不同的摩擦表面和不同的腿部机器人平台上进行了概括，同时也很容易将其从模拟转移到实践。该框架在模拟中通过使用地面触点数据进行了定量和定性评估，并与Atlas，NAO和Talos类人类机器人的现状与ART方法形成对比。此外，在基本的估计中，实际的talos人类生物生物可以证明其功效。为了加强进一步的研究努力，我们的实施是作为开源的ROS/Python软件包，即固定的腿部接触检测（LCD）。
In this article, we propose a deep learning framework that provides a unified approach to the problem of leg contact detection in humanoid robot walking gaits. Our formulation accomplishes to accurately and robustly estimate the contact state probability for each leg (i.e., stable or slip/no contact). The proposed framework employs solely proprioceptive sensing and although it relies on simulated ground-truth contact data for the classification process, we demonstrate that it generalizes across varying friction surfaces and different legged robotic platforms and, at the same time, is readily transferred from simulation to practice. The framework is quantitatively and qualitatively assessed in simulation via the use of ground-truth contact data and is contrasted against state of-the-art methods with an ATLAS, a NAO, and a TALOS humanoid robot. Furthermore, its efficacy is demonstrated in base estimation with a real TALOS humanoid. To reinforce further research endeavors, our implementation is offered as an open-source ROS/Python package, coined Legged Contact Detection (LCD).
