Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration

TL;DR

This paper introduces a robust Learning from Demonstration policy for robotic grasping of compliant food objects, effectively handling inconsistent demonstrations and complex shapes to improve automation in food handling industries.

Contribution

It presents a novel LfD approach that merges RGB-D and tactile data, automatically filters inconsistent demonstrations, and estimates the teacher's intended policy for fragile food objects.

Findings

Successfully handles inconsistent demonstrations in robotic grasping.

Effective for fragile, compliant objects with complex 3D shapes.

Potential to enhance automation in food industry applications.

Abstract

The robotic handling of compliant and deformable food raw materials, characterized by high biological variation, complex geometrical 3D shapes, and mechanical structures and texture, is currently in huge demand in the ocean space, agricultural, and food industries. Many tasks in these industries are performed manually by human operators who, due to the laborious and tedious nature of their tasks, exhibit high variability in execution, with variable outcomes. The introduction of robotic automation for most complex processing tasks has been challenging due to current robot learning policies. A more consistent learning policy involving skilled operators is desired. In this paper, we address the problem of robot learning when presented with inconsistent demonstrations. To this end, we propose a robust learning policy based on Learning from Demonstration (LfD) for robotic grasping of food compliant objects. The approach uses a merging of RGB-D images and tactile data in order to estimate the necessary pose of the gripper, gripper finger configuration and forces exerted on the object in order to achieve effective robot handling. During LfD training, the gripper pose, finger configurations and tactile values for the fingers, as well as RGB-D images are saved. We present an LfD learning policy that automatically removes inconsistent demonstrations, and estimates the teacher's intended policy. The performance of our approach is validated and demonstrated for fragile and compliant food objects with complex 3D shapes. The proposed approach has a vast range of potential applications in the aforementioned industry sectors.