Active Tactile Exploration Based on Cost-Aware Information Gain Maximization
Active Tactile Exploration Based on Cost-Aware Information Gain Maximization
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DOI：
10.1142/s0219843618500159
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发表时间：
2018-02-01
影响因子：
1.5
通讯作者：
Asfour, Tamim
中科院分区：
文献类型：
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作者：
Ottenhaus, Simon;Kaul, Lukas;Asfour, Tamim
主动触觉感知是一种强大的机制，通过机器人手指触摸未知物体来收集接触信息，从而实现与物体的进一步互动或抓取物体。获取的物体知识可以用于基于这些通常稀疏的触觉接触信息构建物体形状模型。在本文中，我们解决了从机器人手指获得的稀疏触觉数据中重建物体形状的问题，该数据可以产生接触信息和接触点的表面方向。为此，我们提出了一种确定下一个最佳触摸目标的探索算法，以最大化估计信息增益并最小化探索动作的预期成本。我们引入了信息增益估计函数（IGEF），它结合了不同的目标作为量化勘探过程中成本感知信息增益的度量。基于igef的勘探策略在48个公开对象模型的模拟中得到验证，并与最先进的基于高斯过程的勘探方法进行了比较。结果表明，该方法在探索效率、成本意识和适合实际触觉场景应用方面表现良好。
Active tactile perception is a powerful mechanism to collect contact information by touching an unknown object with a robot finger in order to enable further interaction with the object or grasping of the object. The acquired object knowledge can be used to build object shape models based on such usually sparse tactile contact information. In this paper, we address the problem of object shape reconstruction from sparse tactile data gained from a robot finger that yields contact information and surface orientation at the contact points. To this end, we present an exploration algorithm which determines the next best touch target in order to maximize the estimated information gain and to minimize the expected costs of exploration actions. We introduce the Information Gain Estimation Function (IGEF), which combines different goals as measure for the quantification of the cost-aware information gain during exploration. The IGEF-based exploration strategy is validated in simulation using 48 publicly available object models and compared to state-of-the-art Gaussian processes-based exploration approaches. The results show the performance of the approach regarding exploration efficiency, cost-awareness and suitability for application in real tactile sensing scenarios.