Abstract:Finite state machines (FSMs) are widely used to manage robot behavior logic, particularly in real-world applications that require a high degree of reliability and structure. However, traditional manual FSM design and modification processes can be time-consuming and error-prone. We propose that large language models (LLMs) can assist developers in editing FSM code for real-world robotic use cases. LLMs, with their ability to use context and process natural language, offer a solution for FSM modification with high correctness, allowing developers to update complex control logic through natural language instructions. Our approach leverages few-shot prompting and language-guided code generation to reduce the amount of time it takes to edit an FSM. To validate this approach, we evaluate it on a real-world robotics dataset, demonstrating its effectiveness in practical scenarios.
Abstract:SODA aims to revolutionize assistive feeding systems by designing a multi-purpose utensil using origami-inspired artificial muscles. Traditional utensils, such as forks and spoons,are hard and stiff, causing discomfort and fear among users, especially when operated by autonomous robotic arms. Additionally, these systems require frequent utensil changes to handle different food types. Our innovative utensil design addresses these issues by offering a versatile, adaptive solution that can seamlessly transition between gripping and scooping various foods without the need for manual intervention. Utilizing the flexibility and strength of origami-inspired artificial muscles, the utensil ensures safe and comfortable interactions, enhancing user experience and efficiency. This approach not only simplifies the feeding process but also promotes greater independence for individuals with limited mobility, contributing to the advancement of soft robotics in healthcare applications.