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Continuous Object State Recognition for Cooking Robots Using Pre-Trained Vision-Language Models and Black-box Optimization (2403.08239v1)

Published 13 Mar 2024 in cs.RO, cs.CV, and cs.LG

Abstract: 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-LLMs. 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.

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Citations (5)
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Summary

  • The paper introduces a continuous object state recognition method for cooking robots using pre-trained vision-language models and black-box optimization to track nuanced food state changes.
  • The approach leverages similarity metrics between image data and text prompts fitted to a sigmoid function, significantly enhancing recognition accuracy in boiling and melting scenarios.
  • Experimental results indicate that model choice influences stability, laying the groundwork for integrating additional sensory modalities in robotic culinary applications.

Continuous Object State Recognition in Cooking Robots: A Study Using Pre-Trained Vision-LLMs and Black-box Optimization

Introduction

The automatic understanding and recognition of environmental changes by robots are crucial aspects that aid in the advancement of automation in various sectors, including assistance in daily living, surveillance, and culinary processes. The ability to discern the state of food, specifically in cooking applications, presents unique challenges due to the continuous and complex nature of food state changes during cooking. Traditional methods that interpret these changes as discrete events often fall short due to the myriad of continuous transformations food undergoes, such as boiling, melting, and frying. Addressing this gap, a novel approach leveraging the prowess of pre-trained vision-LLMs (VLMs) and black-box optimization for continuous state recognition has been proposed by researchers from the University of Tokyo. This method provides a nuanced understanding of cooking states through the lens of spoken language analysis, aimed at enhancing the capabilities of cooking robots.

Methodology

The proposed system builds upon the vast semantic understanding encapsulated within pre-trained VLMs, utilizing their ability to compute the similarity between images and textual descriptions over time. By preparing a diverse set of text prompts related to the states of various foods, the system captures the continuous state changes by observing the variance in similarity scores between current images and these predefined texts. This approach is further refined by employing a fitting of the similarity changes to a sigmoid function, followed by a black-box optimization process to adjust the weighting of each text prompt. This methodology ensures the capture of significant state changes in cooking processes while mitigating the influence of less informative texts, thereby enhancing recognition accuracy and robustness.

Experimental Results

The effectiveness of the proposed method was tested across various cooking scenarios: water boiling, butter melting, egg cooking, and onion stir-frying. The experiments utilized CLIP and ImageBind models for similarity computation between images and texts. The results demonstrate that optimizing the weighting of text prompts significantly improves the performance of continuous state recognition. Particularly, water boiling and butter melting states were accurately captured with minimal deviations between the detected change and the actual change times. However, the recognition of egg cooking presented challenges due to initial rapid changes followed by subtler transformations, highlighting an area for future improvement. Through these experiments, it was also found that the choice of VLMs impacts the stability of recognition, with some models showing a propensity for more stable similarity change patterns across the different cooking states.

Implications and Future Directions

The paper introduces an innovative direction for robotic state recognition in cooking, emphasizing the continuous nature of food state changes. This research not only advances cooking robots' understanding of complex cooking processes but also opens up avenues for the integration of multimodal data, such as audio and heatmaps, to further enhance state recognition performance. The approach's reliance on pre-trained models and linguistic analysis simplifies the implementation and eliminates the need for extensive manual programming or dataset preparation specific to the cooking tasks. Looking forward, the adaptation of the method to incorporate other sensory modalities and the exploration of automatic text set generation using LLMs are promising areas that could provide comprehensive solutions to the nuanced challenges of cooking automation.

Conclusion

The continuous object state recognition method presented in this paper marks a significant step towards nuanced and adaptive robotic assistance in cooking. By leveraging the advanced semantic understanding capabilities of pre-trained VLMs and employing sophisticated optimization techniques, the system successfully interprets complex and continuous food state changes. This research not only broadens the horizon for cooking robots but also sets a foundation for further exploration into multimodal sensory integration and automatic linguistic analysis for state recognition, paving the way for more intelligent and autonomous robotic systems.

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