CulinaryCut-VLAP: A Vision-Language-Action-Physics Framework for Food Cutting via a Force-Aware Material Point Method

2026-01-10

arXiv.org

AI Summary

Food cutting entails highly nonlinear interactions between a knife and deformable objects, characterized by large deformations, frequent contacts, and topological changes, which render stable and safe large-scale data collection in real-world settings extremely challenging. This work proposes a unified framework that, for the first time, integrates force-aware MLS-MPM high-fidelity physics simulation with a multimodal vision-language-action (VLA) dataset. By leveraging multi-view images, fine-grained language instructions, force-torque measurements, and tool pose annotations, the framework generates physically consistent training signals. It enables accurate modeling of mechanical responses under topological changes and establishes the first reproducible benchmark encompassing diverse cutting trajectories and multimodal observations, thereby providing a safe and scalable foundation for VLA learning in deformable object manipulation.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Food cutting is a highly practical yet underexplored application at the intersection of vision and robotic manipulation. The task remains challenging because interactions between the knife and deformable materials are highly nonlinear and often entail large deformations, frequent contact, and topological change, which in turn hinder stable and safe large-scale data collection. To address these challenges, we propose a unified framework that couples a vision-language-action (VLA) dataset with a physically realistic cutting simulator built on the material point method (MPM). Our simulator adopts MLS-MPM as its computational core, reducing numerical dissipation and energy drift while preserving rotational and shear responses even under topology-changing cuts. During cutting, forces and stress distributions are estimated from impulse exchanges between particles and the grid, enabling stable tracking of transient contact forces and energy transfer. We also provide a benchmark dataset that integrates diverse cutting trajectories, multi-view visual observations, and fine-grained language instructions, together with force--torque and tool--pose labels to provide physically consistent training signals. These components realize a learning--evaluation loop that respects the core physics of cutting and establishes a safe, reproducible, and scalable foundation for advancing VLA models in deformable object manipulation.

Problem

• food cutting
• deformable object manipulation
• vision-language-action
• physical simulation
• topological change

Innovation

• Material Point Method
• Vision-Language-Action
• Force-Aware Simulation
• Deformable Object Manipulation
• MLS-MPM