Interactive online learning of the kinematic workspace of a humanoid robot
Interactive online learning of the kinematic workspace of a humanoid robot
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DOI：
10.1109/iros.2012.6385595
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
L. Jamone;L. Natale;G. Sandini;A. Takanishi
中科院分区：
文献类型：
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作者：
L. Jamone;L. Natale;G. Sandini;A. Takanishi
我们描述了一种交互式学习策略，使人形机器人建立一个代表其工作空间：我们称之为可达空间地图。机器人在执行目标导向的到达运动期间自主地在线学习该地图;到达控制也基于在线学习的运动学模型。该地图可用于估计固定对象的可达性，并计划准备运动（例如弯曲或旋转腰部），以提高后续到达动作的有效性。三个主要的概念，使我们的解决方案创新相对于以前的作品：使用凝视为中心的电机表示来描述机器人的工作空间，在构建和表示知识（即交互式学习）的主要作用的行动，实现自主在线学习。我们评估我们的策略，通过学习模拟人形机器人的工作空间，我们展示了如何利用这些知识来规划和执行复杂的动作，如全身双手达成。
We describe an interactive learning strategy that enables a humanoid robot to build a representation of its workspace: we call it a Reachable Space Map. The robot learns this map autonomously and online during the execution of goal-directed reaching movements; reaching control is based on kinematic models that are learned online as well. The map can be used to estimate the reachability of a fixated object and to plan preparatory movements (e.g. bending or rotating the waist) that improve the effectiveness of the subsequent reaching action. Three main concepts make our solution innovative with respect to previous works: the use of a gaze-centered motor representation to describe the robot workspace, the primary role of action in building and representing knowledge (i.e. interactive learning), the realization of autonomous online learning. We evaluate our strategy by learning the workspace of a simulated humanoid robot and we show how this knowledge can be exploited to plan and execute complex actions, like whole-body bimanual reaching.