VERAGMIL: Virtual Environment for Scooping Granular Foods with Imitation Learning Models

2026-08-18

AI Summary

为了解决机器人辅助喂食系统处理颗粒状食物的难题，VERAGMIL框架结合了高保真模拟器和虚拟现实界面，通过模仿学习方法训练模型，显著提高了任务成功率并减少了洒落。

Abstract

Robot-Assisted Feeding (RAF) systems are essential for assisting individuals with disabilities or motor impairments in eating tasks. Manipulating granular food items, such as rice and beans, poses significant challenges due to their dynamic physical properties. Learning from human demonstrations offers a promising solution, but acquiring high-quality demonstrations is complex. To address this, we present VERAGMIL, a framework that combines a high-fidelity simulator with an intuitive Virtual Reality (VR) interface for recording demonstrations and supporting different imitation learning methods. VERAGMIL provides a realistic environment for training RAF systems to handle granular materials, including robots, sensors, and various food items with distinct physical characteristics. We evaluate VERAGMIL by training three imitation learning models, BC, BC-RNN, and BCQ, on granular scooping and transporting tasks using both VR interface and 3D space mouse demonstrations, comparing them with a human-expert baseline. The models are assessed on success rate, spillage, generalization to unseen food items, and task completion time. Results show that VR-based demonstrations significantly outperform 3D space mouse data, with BCQ achieving the best overall performance, particularly in reducing spillage and approaching human performance. These findings underscore the effectiveness of our framework for training RAF systems in granular material handling. The code for our framework is publicly available at: https://github.com/AmanuelErgogo/VERAGMIL.git.

Problem

• Robot-Assisted Feeding
• granular foods
• imitation learning
• human demonstrations
• dynamic physical properties

Innovation

• Virtual Reality (VR)
• Imitation Learning
• Granular Food Handling
• Robot-Assisted Feeding (RAF)