2026-08-18 · IROS

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

Amanuel Ergogo, Diego Dall'Alba, Przemyslaw Korzeniowski

Published at IROS (the arXiv record still lists it as a preprint). 0 citations, as of the last refresh.

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.

Ten-minute slide kit

Six slides is the whole talk: what was broken, what people tried, what these authors did, what the numbers say, where it falls over, and the sentence people should remember.

SLIDE 1

The problem

Due to recent advancements in computation, sensing, and hardware, robots are increasingly being adopted in human environments . Assistive robots, in particular, help individuals with daily tasks . Robot-Assisted Feeding (RAF) systems are an essential component of assistive robotics, providing critical support for individuals with The publication was created within the project of the Minister of Science and Higher Education "Support for the activity of Centers of Excellence established in Poland under Horizon 2020"…

SLIDE 2

What came before

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SLIDE 3

The method

VERAGMIL addresses the complexities of RAF, particularly for granular food manipulation, by combining a highperformance simulation environment with a VR interface. This system facilitates human demonstrations and imitation learning model training for efficient granular food handling tasks. As shown in Figure 2, the system consists of three key components: the simulation environment (described in Section III-A\), the VR control interface (see Section III-B\), and imitation learning frameworks (detailed in Section…

SLIDE 4

What they measured

We assess the performance of the three considered imitation learning models, BC, BC-RNN, and BCQ, on granular material manipulation tasks in our RAF environment. In addition, we compare these models against a human operator baseline to further validate the learned policies. The tasks involve the scooping and transporting of granular materials such as rice, buckwheat, barley, and pasta, each presenting distinct physical properties such as size, shape, friction, and density, as reported in Table I. These selected…

SLIDE 5

Where it breaks

The results demonstrate the impact of different imitation learning models and demonstration methods on the performance of granular food item handling tasks. Across both VRI and 3DM scenarios, BCQ consistently outperformed BC and BC-RNN in terms of success rate, spillage, and generalization. This is in line with previously published results that consider different robotic applications and require simpler interaction with the environment

SLIDE 6

One-line takeaway

VERAGMIL provides a realistic environment for training RAF systems to handle granular materials, including robots, sensors, and various food items with distinct physical characteristics, and demonstrates the effectiveness of the framework for training RAF systems in granular material handling.

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