Leveraging Multimodal Haptic Sensory Data for Robust Cutting
Leveraging Multimodal Haptic Sensory Data for Robust Cutting
复制标题
利用多模态触觉传感数据实现稳健切割
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Oliver Kroemer
中科院分区：
文献类型：
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作者：
Kevin Zhang;Mohit Sharma;M. Veloso;Oliver Kroemer
切割是处理食物、绳子或粘土等可分割物体时常用的一种操作方式。烹饪在很大程度上依赖于切割，将食物分成想要的形状。然而，切割食物是一项具有挑战性的任务，因为食物所表现出的材料特性范围很广。由于这种可变性，相同的切割动作不能用于所有食品。接触事件产生的感觉，例如，当把刀放在食物上时，也会根据材料的特性而变化，机器人需要相应地适应。在本文中，我们建议使用振动和力-扭矩反馈从相互作用来适应切片运动和监测接触事件。机器人学习神经网络来执行这些任务，并在不同的材料属性中进行推广。通过适应和监控技能的执行，机器人能够可靠地切割20多种不同类型的食物，甚至可以检测出某些食物是新鲜的还是旧的。
Cutting is a common form of manipulation when working with divisible objects such as food, rope, or clay. Cooking in particular relies heavily on cutting to divide food items into desired shapes. However, cutting food is a challenging task due to the wide range of material properties exhibited by food items. Due to this variability, the same cutting motions cannot be used for all food items. Sensations from contact events, e.g., when placing the knife on the food item, will also vary depending on the material properties, and the robot will need to adapt accordingly. In this paper, we propose using vibrations and force-torque feedback from the interactions to adapt the slicing motions and monitor for contact events. The robot learns neural networks for performing each of these tasks and generalizing across different material properties. By adapting and monitoring the skill executions, the robot is able to reliably cut through more than 20 different types of food items and even detect whether certain food items are fresh or old.
DOI：
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发表时间：
2018-10
期刊：
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影响因子：
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作者：
Samuel Clarke;Travers Rhodes;C. Atkeson;Oliver Kroemer
通讯作者：
Samuel Clarke;Travers Rhodes;C. Atkeson;Oliver Kroemer