Towards Robotic Feeding: Role of Haptics in Fork-based Food Manipulation

TL;DR

This paper investigates how humans manipulate food during feeding using haptic and motion data, proposing classifiers for compliance-based categorization and comparing human strategies with robotic policies to improve autonomous feeding.

Contribution

It introduces a taxonomy of feeding manipulation strategies and compliance-dependent classifiers, advancing robot adaptability in food handling tasks.

Findings

Humans adapt their control policies based on food compliance and shape.

Classifiers can categorize food compliance from haptic and motion signals.

Robotic policies benefit from adapting to food compliance, improving success rates.

Abstract

Autonomous feeding is challenging because it requires manipulation of food items with various compliance, sizes, and shapes. To understand how humans manipulate food items during feeding and to explore ways to adapt their strategies to robots, we collected a rich dataset of human trajectories by asking them to pick up food and feed it to a mannequin. From the analysis of the collected haptic and motion signals, we demonstrate that humans adapt their control policies to accommodate to the compliance and shape of the food item being acquired. We propose a taxonomy of manipulation strategies for feeding to highlight such policies. As a first step to generate compliance-dependent policies, we propose a set of classifiers for compliance-based food categorization from haptic and motion signals. We compare these human manipulation strategies with fixed position-control policies via a robot. Our analysis of success and failure cases of human and robot policies further highlights the importance of adapting the policy to the compliance of a food item.