MOSAIC: A Modular System for Assistive and Interactive Cooking
Kernekoncepter
MOSAIC is a modular system that enables multiple robots to collaborate with a human user in cooking tasks, leveraging pre-trained models and interactive task planning.
Resumé
MOSAIC is a modular system developed by Cornell University that allows home robots to collaborate with humans in cooking tasks. The system employs large-scale pre-trained models for language and image recognition, along with task-specific control modules. Through extensive evaluations, MOSAIC has shown efficient collaboration with humans in cooking trials, completing 68.3% of tasks successfully. The system addresses challenges in collaborative tasks by interacting naturally with users, performing various skills involving everyday objects, and seamless collaboration with humans. By using an ensemble of large-scale models, MOSAIC integrates multiple robots to tackle complex tasks like cooking alongside a human user.
Oversæt kilde
Til et andet sprog
Generer mindmap
fra kildeindhold
MOSAIC
Statistik
MOSAIC completed 68.3% (41/60) collaborative cooking trials of 6 different recipes.
Subtask completion rate was 91.6% on average.
The system employs Large Language Models (LLMs) for interactive task planning and vision-language models (VLMs) for visuomotor skills.
Human motion forecasting model trained on AMASS dataset predicts future human motion accurately.
Citater
"We show that MOSAIC is able to efficiently collaborate with humans."
"MOSAIC tightly collaborates with humans, interacts using natural language, coordinates multiple robots."
"MOSAIC employs modularity leveraging pre-trained models for general tasks and streamlined modules for task-specific control."
Dybere Forespørgsler
How does the use of large-scale pre-trained models impact the efficiency of collaborative tasks compared to traditional methods
大規模な事前学習モデルの使用は、従来の方法と比較して協力タスクの効率にどのように影響するでしょうか？
大規模な事前学習モデルを使用することは、協力タスクの効率を向上させるために重要です。これらのモデルは一般的な課題（言語理解や画像認識）において高い性能を発揮し、特定の任務制御用途に設計された最適化されたモジュールと組み合わせることで、全体的なパフォーマンスが向上します。例えば、MOSAICでは大規模言語モデル（LLMs）やビジョン・ランゲージ・モデル（VLMs）がインタラクティブなタスクプランニングやアクション予測に活用されています。これにより、ロボットが人間と円滑に連携しながら多様な作業を実行する際の精度や速度が向上します。
What are the potential limitations or challenges faced when integrating multiple robots to collaborate with a human user in real-world scenarios
複数のロボットを実世界シナリオで人間利用者と共同作業させる際に直面する可能性のある制限または課題は何ですか？
複数ロボットを人間利用者と協力して操作する場合、以下のような潜在的制約や課題が考えられます。
チームコーディネーション：異種ロボット同士や人間利用者と適切なコラボレーションを確立する必要があります。
セキュリティ：安全性へ配慮しながら動作計画および実行プロセスを設計し、意図しない接触や衝突等から保護する必要があります。
統合通信：各エージェント間およびエージェント - 人間利用者間で円滑かつ明確なコミュニケーション手段を確立し管理すべきです。
How can the concept of modularity be applied to other domains beyond robotics to enhance collaboration between machines and humans
他分野でも協働関係強化目的で「Modularity」コンセプトはどう応用可能ですか？
Modularity concept can be applied to other domains beyond robotics to enhance collaboration between machines and humans by:
Software Development: Breaking down complex software systems into modular components for easier maintenance and scalability.
Project Management: Utilizing modular project structures to facilitate collaboration among team members with diverse skill sets.
Healthcare: Implementing modular healthcare systems that allow different medical devices and professionals to work together seamlessly.
By applying modularity in various fields, it becomes possible to streamline processes, improve efficiency, and foster effective collaboration between machines and humans.
