Food manipulation: A cadence of haptic signals

Autonomous assistive feeding is challenging because it requires manipulation of food items with various compliance, sizes, and shapes. To better understand how humans perform a feeding task and explore ways to adapt their strategies to robots, we collected a rich dataset of human subjects' feeding instances and compared them with position-controlled instances via a robot. In the analysis of the dataset which includes measurements from visual and haptic signals, we demonstrate that humans vary their control policies to accommodate to the compliance and the shape of the food item being acquired. We propose a taxonomy of manipulation strategies for feeding to highlight such policies. Our subsequent analysis of failed feeding instances of humans and the robot highlights the importance of adapting the policy to the compliance of a food item. Finally, as the first step to generate compliance-dependent policies, we propose a set of classifiers which classifies haptic and motion signals during bite acquisition into four compliance-based food categories. Temporal Convolution Network (TCN) outperforms other classifiers with an accuracy of 82.2% on the human data and 83.1% on the robot data.