史丹佛大學推出 BEHAVIOR-1K 來訓練機器人做家事

史丹佛大學研究人員推出了 BEHAVIOR-1K，這是使用 NVIDIA 的 OmniGibson 模擬訓練機器人完成 1000 項家務任務的基準

Stanford researchers introduced BEHAVIOR-1K, a benchmark for training robots in 1,000 household tasks, using NVIDIA's OmniGibson simulation.

史丹佛大學研究人員推出了 BEHAVIOR-1K，這是使用 NVIDIA 的 OmniGibson 模擬訓練機器人完成 1,000 項家務任務的基準。

In a significant development in the field of robotics, researchers from Stanford University have introduced BEHAVIOR-1K, a comprehensive benchmark aimed at training robots to perform 1,000 real-world-inspired household activities. This initiative was unveiled at the NVIDIA GTC 2024 conference and represents a step forward in making robots practical for everyday assistance.

作為機器人領域的重大發展，史丹佛大學的研究人員推出了 BEHAVIOR-1K，這是一個綜合基準測試，旨在訓練機器人執行 1,000 項受現實世界啟發的家庭活動。該舉措在 NVIDIA GTC 2024 會議上公佈，代表機器人在日常協助方面又向前邁出了一步。

BEHAVIOR-1K and OmniGibson

BEHAVIOR-1K 和 OmniGibson

The BEHAVIOR-1K benchmark utilizes OmniGibson, a state-of-the-art simulation environment built on the NVIDIA Omniverse platform. This environment is designed to accelerate embodied AI research by providing robots with practical skills applicable in real-world settings. The focus is on tasks that range from folding laundry and cooking breakfast to cleaning up after social gatherings.

BEHAVIOR-1K 基準測試採用 OmniGibson，這是一種基於 NVIDIA Omniverse 平台建構的最先進的類比環境。該環境旨在透過為機器人提供適用於現實環境的實用技能來加速實體人工智慧研究。重點是從摺衣服、煮早餐到社交聚會後的清理等任務。

Practical Applications and Human-Centered Design

實際應用與以人為本的設計

BEHAVIOR-1K is part of a broader initiative to integrate robotics into daily life, thereby freeing up time for individuals to engage in activities they enjoy. The benchmark is informed by insights from surveys involving over 1,400 participants, ensuring that the tasks align with human needs and preferences.

BEHAVIOR-1K 是將機器人技術融入日常生活的更廣泛計劃的一部分，從而為個人騰出時間從事他們喜歡的活動。該基準基於對 1,400 多名參與者進行的調查的見解，確保任務符合人類的需求和偏好。

Training and Realism

訓練與現實主義

The training process involves large-scale simulations across 50 fully interactive environments, incorporating over 1,200 object categories and more than 5,000 3D models. This approach allows robots to experience diverse and realistic scenarios, enhancing their ability to operate effectively in real-world applications. The benchmark also focuses on improving the realism of AI training by incorporating various object states, complex interactions, and realistic physical properties.

訓練過程涉及跨 50 個完全互動式環境的大規模模擬，包含 1,200 多個物件類別和 5,000 多個 3D 模型。這種方法使機器人能夠體驗多樣化和真實的場景，增強它們在現實應用中有效操作的能力。這個基準也著重於透過整合各種物件狀態、複雜的互動和真實的物理屬性來提高人工智慧訓練的真實性。

Future Prospects

前景

As robotics technology continues to advance, the BEHAVIOR-1K benchmark represents a vital tool in bridging the gap between experimental research and practical application. By focusing on tasks that people want help with, the initiative ensures that robotic assistance is both effective and aligned with human needs.

隨著機器人技術的不斷進步，BEHAVIOR-1K 基準測試成為彌合實驗研究與實際應用之間差距的重要工具。透過專注於人們需要幫助的任務，該計劃確保機器人援助既有效又符合人類需求。

For further information, the original article can be accessed on the NVIDIA blog.

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