# RoboChop: Autonomous Framework for Fruit and Vegetable Chopping Leveraging Foundational Models

arXiv: https://arxiv.org/abs/2307.13159 · HTML: https://mlsanity.com/paper/2307.13159/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2023.

## Built on by (most cited first, 5 of 5)

- [One ACT Play: Single Demonstration Behavior Cloning with Action Chunking Transformers](https://mlsanity.com/paper/2309.10175/) (2023)
- [SculptBot: Pre-Trained Models for 3D Deformable Object Manipulation](https://mlsanity.com/paper/2309.08728/) (2023)
- [Stable Segment Anything Model](https://mlsanity.com/paper/2311.15776/) (2023)
- [CulinaryCut-VLAP: A Vision-Language-Action-Physics Framework for Food Cutting via a Force-Aware Material Point Method](https://mlsanity.com/paper/2601.06451/) (2026)
- [Classifying Healthy and Defective Fruits with a Multi-Input Architecture and CNN Models](https://mlsanity.com/paper/2410.11108/) (2024)

## Abstract (arXiv)

With the goal of developing fully autonomous cooking robots, developing robust systems that can chop a wide variety of objects is important. Existing approaches focus primarily on the low-level dynamics of the cutting action, which overlooks some of the practical real-world challenges of implementing autonomous cutting systems. In this work we propose an autonomous framework to sequence together action primitives for the purpose of chopping fruits and vegetables on a cluttered cutting board. We present a novel technique to leverage vision foundational models SAM and YOLO to accurately detect, segment, and track fruits and vegetables as they visually change through the sequences of chops, finetuning YOLO on a novel dataset of whole and chopped fruits and vegetables. In our experiments, we demonstrate that our simple pipeline is able to reliably chop a variety of fruits and vegetables ranging in size, appearance, and texture, meeting a variety of chopping specifications, including fruit type, number of slices, and types of slices.