aiqbench analysis
VERAGMIL introduces a VR-integrated simulation framework designed specifically for training imitation learning models in Robot-Assisted Feeding tasks involving granular materials. By comparing Virtual Reality demonstrations against 3D space mouse inputs, the study demonstrates that VR data significantly improves robot performance metrics such as spillage reduction and task success rates when handling dynamic food items like rice or beans. This work advances robotic manipulation research by providing a scalable method to acquire high-quality training data for assistive robotics without requiring extensive physical human demonstrations.
VERAGMIL: Virtual Environment for Scooping Granular Foods with Imitation Learning Models
arXiv:2608.18258v1 Announce Type: new 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.
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: this https URL.
Robotics (cs.RO)
arXiv:2608.18258 [cs.RO]
(or arXiv:2608.18258v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.18258 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 13075-13082, 2025
https://doi.org/10.1109/IROS60139.2025.11247362 Focus to learn more DOI(s) linking to related resources
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