GRAB: A Systematic Real-World Grasping Benchmark for Robotic Food Waste Sorting

2026-02-21

AI Summary

This study addresses the challenge of robotic grasping in food waste sorting, where the diverse and unpredictable forms of inorganic contaminants hinder reliable manipulation. To this end, the authors establish a real-world-oriented grasping evaluation benchmark that, for the first time, incorporates pre-grasp environmental conditions and multidimensional grasping capability metrics. The framework integrates deformable object modeling, high-precision grasp pose estimation, and multiple gripper hardware types, enabling 1,750 physical grasping trials in high-fidelity, complex scenarios. The experiments reveal that object mass is the dominant factor influencing success rates and demonstrate that gripper performance is highly dependent on target categories. These findings underscore the necessity of developing multimodal, general-purpose gripper technologies, thereby overcoming the limitations of current simulation-dominated approaches and overly simplistic evaluation metrics.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Food waste management is critical for sustainability, yet inorganic contaminants hinder recycling potential. Robotic automation presents a compelling approach to this challenge by accelerating the sorting process through automated contaminant removal. Still, the diverse and unpredictable nature of contaminants creates major challenges for robotic grasping. Benchmarking frameworks are critical for evaluating challenges from various perspectives. However, existing protocols rely on limited simulation datasets, prioritise simple metrics such as success rate, and overlook key object and environment-related pre-grasp conditions. This paper introduces GRAB, a comprehensive Grasping Real-World Article Benchmarking framework that addresses this gap by integrating diverse deformable objects, advanced grasp-pose-estimation vision, and, importantly, pre-grasp conditions, establishing a set of critical graspability metrics. It systematically compares industrial grasping modalities through an in-depth experimental evaluation involving 1,750 food contaminant grasp attempts across four high-fidelity scenes. This large-scale evaluation provides an extensive assessment of grasp performance for food waste sorting, offering a level of depth that has rarely been explored in previous studies. The results reveal distinct gripper strengths and limitations, with object quality emerging as the dominant performance factor in cluttered environments, while vision quality and clutter levels play moderate roles. These findings highlight essential design considerations and reinforce the necessity of developing multimodal gripper technologies capable of robust cross-category performance for effective robotic food waste sorting.

Problem

• robotic grasping
• food waste sorting
• benchmarking
• deformable objects
• pre-grasp conditions

Innovation

• grasping benchmark
• food waste sorting
• deformable objects
• pre-grasp conditions
• multimodal grippers