Title: Leveraging Multimodal Sensory Data for Robust Cutting

Abstract:

Cutting food is a challenging task due to the variety of material properties across food items. In addition, different events occur during the slicing process that need to be monitored and detected for robust execution, such as when a knife has completely cut through a food item or missed it. We show that by utilizing vibration feedback from contact microphones and robot force data, we can continuously monitor these events. In addition, the multimodal sensory data enables the robot to adapt its cutting technique according to the material, which results in our robot being able to successfully cut 23 classes of objects. In this talk, we will present our entire system pipeline for slicing food items with a bimanual robotic setup.

Committee:

Manuela Veloso (Co-Advisor)

Oliver Kroemer (Co-Advisor)

George Kantor

Anahita Mohseni-Kabir