论文标题
从人步行到两足机器人机器人：对计划和计划外的下台的反射启发赔偿
From Human Walking to Bipedal Robot Locomotion: Reflex Inspired Compensation on Planned and Unplanned Downsteps
论文作者
论文摘要
人类能够以显着的敏捷性和轻松的方式谈判计划和计划外行为。本文的目的是系统地研究这种人类行为向两足步行机器人的翻译，即使形态本质上不同。具体而言，我们从人类数据开始，其中计划和计划外的下台。我们从人类的减少阶层建模的角度分析了这些数据，编码质量（COM）运动学和接触力的中心，这允许将这些行为转化为双皮德机器人的相应减少阶模型。我们通过基于非线性优化的控制器将所得的行为嵌入了两足机器人的全阶动力学中。最终结果是在不足的步行机器人上模拟中计划和计划外的下台。
Humans are able to negotiate downstep behaviors -- both planned and unplanned -- with remarkable agility and ease. The goal of this paper is to systematically study the translation of this human behavior to bipedal walking robots, even if the morphology is inherently different. Concretely, we begin with human data wherein planned and unplanned downsteps are taken. We analyze this data from the perspective of reduced-order modeling of the human, encoding the center of mass (CoM) kinematics and contact forces, which allows for the translation of these behaviors into the corresponding reduced-order model of a bipedal robot. We embed the resulting behaviors into the full-order dynamics of a bipedal robot via nonlinear optimization-based controllers. The end result is the demonstration of planned and unplanned downsteps in simulation on an underactuated walking robot.
