局部在线运动Babbling：肌肉骨骼机器人手臂的运动学习
Local Online Motor Babbling: Learning Motor Abundance of A Musculoskeletal Robot Arm
摘要：
运动牙牙学语和目标牙学语已被用于软机器人高度冗余系统的感觉运动学习。最近在球门牙牙学语方面的工作已经证明了在此类系统上成功学习逆运动学（IK），并表明球门空间中的牙学语通过学习尽可能少的感觉运动映射来更好地解决运动冗余。然而，对于肌肉骨骼机器人系统，运动冗余可以是解释肌肉激活模式的有用信息，因此术语运动丰度。在这项工作中，我们引入了一些简单的算法来经验性地定义未知的目标空间，并学习逆运动学的10自由度的肌肉骨骼机器人手臂使用定向目标babbling。然后，我们进一步提出了本地在线电机咿呀学语使用协方差矩阵自适应演化策略（CMA-ES），引导在目标咿呀学语中收集的样本进行初始化，以便电机丰度可以查询定义的目标空间内的任何静态目标。结果表明，我们的运动咿呀学语方法可以有效地探索运动丰富度，并提供了有用的见解方面的肌肉刚度和协同作用。
英文摘要：
Motor babbling and goal babbling has been used for sensorimotor learning of highly redundant systems in soft robotics. Recent works in goal babbling has demonstrated successful learning of inverse kinematics (IK) on such systems, and suggests that babbling in the goal space better resolves motor redundancy by learning as few sensorimotor mapping as possible. However, for musculoskeletal robot systems, motor redundancy can be of useful information to explain muscle activation patterns, thus the term motor abundance. In this work, we introduce some simple heuristics to empirically define the unknown goal space, and learn the inverse kinematics of a 10 DoF musculoskeletal robot arm using directed goal babbling. We then further propose local online motor babbling using Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which bootstraps on the collected samples in goal babbling for initialization, such that motor abundance can be queried for any static goal within the defined goal space. The result shows that our motor babbling approach can efficiently explore motor abundance, and gives useful insights in terms of muscle stiffness and synergy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
