首页> 外文会议>Robotics and Automation (ICRA), 2012 IEEE International Conference on >Using basin ruins and co-moving low-dimensional latent coordinates for dynamic programming of biped walkers on roughing ground
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Using basin ruins and co-moving low-dimensional latent coordinates for dynamic programming of biped walkers on roughing ground

机译:使用盆地遗址和共同移动的低维潜在坐标对粗糙地面上的两足动物步行者进行动态编程

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摘要

Disturbance rejection is one of the most important abilities required for biped walkers. In this study, we propose a method for dynamic programming of biped walking and apply it to a simple passive dynamic walker (PDW) on an irregular slope. The key of the proposed approach is to employ the transient dynamics of the walker just before approaching the falling state in the absence of any controlling input, and to derive the optimal control policy in the low-dimensional latent space. In recent our study, we found that such transient dynamics deeply relates to the basin of attraction for a stable gait. By patching latent coordinates to such a structures in each Poincaré section and defining the reward function according to the survive time of the transient dynamics, so-called escape-times, we construct a Markov decision process (MDP) for the PDW and obtain an optimal policy using a dynamic programming (DP). We will show that the proposed method actually succeeds in controlling the PDW even if the degree of disturbance is relatively large and the dimensionality of coordinates is reduced to lower ones.
机译:干扰抑制是Biped步行者所需的最重要功能之一。在这项研究中,我们提出了一种用于Biped步行动态规划的方法,并将其应用于不规则坡度上的简单被动动态步行器(PDW)。提出的方法的关键是在没有任何控制输入的情况下,在即将接近跌倒状态之前,采用助行器的瞬态动力学,并在低维潜在空间中得出最佳控制策略。在最近的研究中,我们发现这种瞬态动力学与稳定步态的吸引力范围密切相关。通过将潜在坐标打补丁到每个庞加莱部分中的此类结构,并根据瞬态动力学的生存时间(即所谓的逃逸时间)定义奖励函数,我们为PDW构造了马尔可夫决策过程(MDP)并获得了最优解使用动态编程(DP)的策略。我们将证明,即使干扰程度相对较大且坐标的维数降低到较低的水平,所提出的方法实际上也可以成功地控制PDW。

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