MOSAIC: Modular System for Assistive and Interactive Cooking

المفاهيم الأساسية

The author presents MOSAIC, a modular system designed to enable multiple robots to collaborate with a human user in cooking tasks. By leveraging large-scale pre-trained models and a modular approach, the system efficiently communicates with users, forecasts human intents, and completes visuomotor skills.

الملخص

MOSAIC is a modular system developed by Cornell University for collaborative cooking tasks. The system tightly collaborates with humans, interacts using natural language, coordinates multiple robots, and manages an open vocabulary of everyday objects. Through extensive evaluations on end-to-end trials and individual modules testing, MOSAIC demonstrates efficient collaboration with humans in complex tasks like cooking.
The content discusses the challenges faced by home robots in performing collaborative tasks and introduces MOSAIC as a solution. The system employs modularity to enhance performance in interacting with users via natural language, executing various skills involving everyday objects, and seamless collaboration with humans during cooking tasks. By integrating large-scale pre-trained models and conducting thorough evaluations, MOSAIC showcases its ability to efficiently collaborate with humans in real-world scenarios.
Key points include the architecture overview of MOSAIC involving an Interactive Task Planner, Visuomotor Skills module, and Human Motion Forecasting module. The experiments conducted evaluate the performance of each module through end-to-end trials and individual skill tests. Results show that the modular approach enhances task planning efficiency, visuomotor skill execution accuracy, and human motion forecasting reliability.

الإحصائيات

Completing 68.3% (41/60) collaborative cooking trials of 6 different recipes.
Subtask completion rate of 91.6%.

اقتباسات

"We identify three key desiderata for the system: interact with users via natural language, perform a range of skills that require manipulating everyday objects, and collaborate seamlessly with humans." - Content
"MOSAIC is able to complete 41/60 tasks with an average subtask completion rate of 91.6%." - Content

الرؤى الأساسية المستخلصة من

MOSAIC

by Huaxiaoyue W... في arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18796.pdf

استفسارات أعمق

How does the modularity of MOSAIC contribute to its efficiency compared to traditional robotic systems?

The modularity of MOSAIC plays a crucial role in enhancing its efficiency compared to traditional robotic systems. By breaking down the system into distinct modules, each responsible for specific tasks, MOSAIC can achieve better performance and flexibility. Here are some key ways in which modularity contributes to its efficiency:

Specialization: Each module in MOSAIC is designed to handle a specific aspect of the task, such as task planning, visuomotor skills, and human motion forecasting. This specialization allows for more efficient processing and decision-making within each module.

Scalability: The modular architecture of MOSAIC makes it easier to scale up or modify individual components without affecting the entire system. New modules can be added or existing ones upgraded independently, leading to improved scalability.

Fault Isolation: In case of errors or failures, the modular design helps isolate issues within specific modules without impacting the overall functionality of the system. This makes troubleshooting and debugging more manageable and reduces downtime.

Interchangeability: Modules in MOSAIC can be interchanged with alternative implementations as long as they adhere to a standardized interface. This interchangeability enhances adaptability and allows for easy integration of new technologies or algorithms.

Optimized Performance: Each module can be optimized independently based on its requirements, leading to overall improved performance across different aspects of robot operation like task planning accuracy, object manipulation precision, and human-robot interaction fluidity.

How do large-scale pre-trained models enhance robot-human collaboration in MOSAIC?

Large-scale pre-trained models play a significant role in enhancing robot-human collaboration within MOSAIC by providing several benefits that improve communication, understanding user intent, and executing tasks effectively:

Natural Language Interaction: Pre-trained language models enable robots in MOSAIC to understand natural language commands from users accurately. This capability facilitates seamless communication between humans and robots during collaborative tasks like cooking by interpreting instructions effectively.

Task Planning Efficiency: Large-scale pre-trained models assist in interactive task planning by generating detailed plans based on user inputs quickly and efficiently.

3 .Improved Object Recognition: Vision-language models help robots identify objects accurately using visual cues combined with textual descriptions provided by users or other sources.
4 .Enhanced Human Motion Prediction: By leveraging large datasets for human motion forecasting training data sets ,MOSIAC's ability predict future movements enables robots plan their actions accordingly when collaborating closely with humans
5 .Generalization Across Tasks: These pre-trained models have been trained on vast amounts of diverse data allowing them generalize well across various scenarios encountered during collaborative tasks.

How can the concept of modularity be applied to other domains beyond robotics for improved task performance?

The concept of modularity demonstrated in systems like Mosaic has proven effective not only robotics but also applicable across various domains where complex tasks need efficient execution.Here are some ways this concept could benefit other areas:

Software Development: Breaking down software projects into modular components would allow developers work concurrently on different parts while ensuring easier maintenance & updates

Manufacturing Processes: Modularizing manufacturing processes could lead increased flexibility & customization options along with quicker reconfiguration times

Supply Chain Management: Implementing modular supply chain practices could optimize inventory management,reducing costs & improving responsiveness

Healthcare Systems: Modularity could streamline patient care delivery through specialized treatment modules that cater patients' unique needs efficiently

Education Sector: Adopting modular learning approaches would facilitate personalized learning paths tailored students' pace & preferences while enabling educators focus teaching methods based individual student needs