A Tactile-Based Framework for Active Object Learning and Discrimination using Multimodal Robotic Skin
A Tactile-Based Framework for Active Object Learning and Discrimination using Multimodal Robotic Skin
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DOI：
10.1109/lra.2017.2720853
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发表时间：
2017-10-01
影响因子：
5.2
通讯作者：
Cheng, Gordon
中科院分区：
文献类型：
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作者：
Kaboli, Mohsen;Feng, Di;Cheng, Gordon
在这封信中，我们提出了一个完整的基于概率触觉的框架，使机器人能够自主探索未知的工作空间并根据物体的物理属性识别物体。我们的框架由三个部分组成：1）有效探索未知工作空间的主动触摸前策略； 2）主动触摸学习方法，根据未知物体的物理属性（表面纹理、刚度和导热系数）以最少的训练样本来学习未知物体； 3）用于物体辨别的主动触摸算法，该算法选择信息最丰富的探索动作应用于物体，以便机器人可以通过少量动作有效地区分物体。我们提出的框架使用配备多模式人造皮肤的机械臂进行了实验评估。与均匀策略和随机策略相比，采用主动预触摸方法的机器人将工作空间的不确定性分别降低了 30% 和 70%。通过主动触摸学习算法，机器人使用比基线方法少50%的样本来达到相同的学习精度。通过利用学习过程中获得的先验知识，机器人主动识别物体，与随机动作选择方法相比，识别精度提高了 10%。
In this letter, we propose a complete probabilistic tactile-based framework to enable robots to autonomously explore unknown workspaces and recognize objects based on their physical properties. Our framework consists of three components: 1) an active pretouch strategy to efficiently explore unknown workspaces; 2) an active touch learning method to learn about unknown objects based on their physical properties (surface texture, stiffness, and thermal conductivity) with the least number of training samples; and 3) an active touch algorithm for object discrimination, which selects the most informative exploratory action to apply to the object, so that the robot can efficiently distinguish between objects with a few number of actions. Our proposed framework was experimentally evaluated using a robotic arm equipped with multimodal artificial skin. The robot with the active pretouch method reduced the uncertainty of the workspace up to 30% and 70% compared to uniform and random strategies, respectively. By means of the active touch learning algorithm, the robot used 50% fewer samples to achieve the same learning accuracy than the baseline methods. By taking advantage of the prior knowledge obtained during the learning process, the robot actively discriminated objects with an improvement of 10% recognition accuracy compare to the random action selection approach.