# Continuous Object State Recognition for Cooking Robots Using Pre-Trained Vision-Language Models and Black-box Optimization

arXiv: https://arxiv.org/abs/2403.08239 · HTML: https://mlsanity.com/paper/2403.08239/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 11 of 11)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://mlsanity.com/paper/2202.03052/) (2022)
- [Vision-Language Intelligence: Tasks, Representation Learning, and Large Models](https://mlsanity.com/paper/2203.01922/) (2022)
- [Robotic Applications of Pre-Trained Vision-Language Models to Various Recognition Behaviors](https://mlsanity.com/paper/2303.05674/) (2023)
- [VQA-based Robotic State Recognition Optimized with Genetic Algorithm](https://mlsanity.com/paper/2303.05052/) (2023)
- [Identifying Object States in Cooking-Related Images](https://mlsanity.com/paper/1805.06956/) (2018)
- [Classifying cooking object's state using a tuned VGG convolutional neural network](https://mlsanity.com/paper/1805.09391/) (2018)
- [Recognition of Heat-Induced Food State Changes by Time-Series Use of Vision-Language Model for Cooking Robot](https://mlsanity.com/paper/2309.01528/) (2023)
- [Cooking Object's State Identification Without Using Pretrained Model](https://mlsanity.com/paper/2103.02305/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [Real-World Cooking Robot System from Recipes Based on Food State Recognition Using Foundation Models and PDDL](https://mlsanity.com/paper/2410.02874/) (2024)
- [Robotic Environmental State Recognition with Pre-Trained Vision-Language Models and Black-Box Optimization](https://mlsanity.com/paper/2409.17519/) (2024)

## Abstract (arXiv)

The state recognition of the environment and objects by robots is generally based on the judgement of the current state as a classification problem. On the other hand, state changes of food in cooking happen continuously and need to be captured not only at a certain time point but also continuously over time. In addition, the state changes of food are complex and cannot be easily described by manual programming. Therefore, we propose a method to recognize the continuous state changes of food for cooking robots through the spoken language using pre-trained large-scale vision-language models. By using models that can compute the similarity between images and texts continuously over time, we can capture the state changes of food while cooking. We also show that by adjusting the weighting of each text prompt based on fitting the similarity changes to a sigmoid function and then performing black-box optimization, more accurate and robust continuous state recognition can be achieved. We demonstrate the effectiveness and limitations of this method by performing the recognition of water boiling, butter melting, egg cooking, and onion stir-frying.