Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection

TL;DR

This paper presents a synthetic-data trained instance segmentation approach for cluttered food grasping, incorporating adaptive fingers and damage prevention techniques, to improve automation in food handling.

Contribution

It introduces a synthetic-data training pipeline with sim2real transfer, adaptive finger design, and grasp filtering to enhance food grasping in cluttered environments.

Findings

Successful transfer from synthetic to real food trays

Adaptive fingers reduce damage during grasping

Effective filtering of potentially damaging grasps

Abstract

The food packaging industry handles an immense variety of food products with wide-ranging shapes and sizes, even within one kind of food. Menus are also diverse and change frequently, making automation of pick-and-place difficult. A popular approach to bin-picking is to first identify each piece of food in the tray by using an instance segmentation method. However, human annotations to train these methods are unreliable and error-prone since foods are packed close together with unclear boundaries and visual similarity making separation of pieces difficult. To address this problem, we propose a method that trains purely on synthetic data and successfully transfers to the real world using sim2real methods by creating datasets of filled food trays using high-quality 3d models of real pieces of food for the training instance segmentation models. Another concern is that foods are easily damaged during grasping. We address this by introducing two additional methods -- a novel adaptive finger mechanism to passively retract when a collision occurs, and a method to filter grasps that are likely to cause damage to neighbouring pieces of food during a grasp. We demonstrate the effectiveness of the proposed method on several kinds of real foods.