斯坦福大学推出 BEHAVIOR-1K 来训练机器人做家务

斯坦福大学研究人员推出了 BEHAVIOR-1K，这是使用 NVIDIA 的 OmniGibson 模拟训练机器人完成 1000 项家务任务的基准

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

斯坦福大学研究人员推出了 BEHAVIOR-1K，这是使用 NVIDIA 的 OmniGibson 模拟训练机器人完成 1,000 项家务任务的基准。

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.

作为机器人领域的一项重大发展，斯坦福大学的研究人员推出了 BEHAVIOR-1K，这是一个综合基准测试，旨在训练机器人执行 1,000 项受现实世界启发的家庭活动。该举措在 NVIDIA GTC 2024 会议上公布，代表着机器人在日常协助方面又向前迈出了一步。

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 基准测试采用 OmniGibson，这是一种基于 NVIDIA Omniverse 平台构建的最先进的模拟环境。该环境旨在通过为机器人提供适用于现实环境的实用技能来加速实体人工智能研究。重点是从叠衣服、煮早餐到社交聚会后的清理等任务。

Practical Applications and Human-Centered Design

实际应用和以人为本的设计

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 是将机器人技术融入日常生活的更广泛计划的一部分，从而为个人腾出时间从事他们喜欢的活动。该基准基于对 1,400 多名参与者进行的调查的见解，确保任务符合人类的需求和偏好。

Training and Realism

训练与现实主义

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.

训练过程涉及跨 50 个完全交互式环境的大规模模拟，包含 1,200 多个对象类别和 5,000 多个 3D 模型。这种方法使机器人能够体验多样化和真实的场景，增强它们在现实应用中有效操作的能力。该基准还侧重于通过整合各种对象状态、复杂的交互和真实的物理属性来提高人工智能训练的真实性。

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.

随着机器人技术的不断进步，BEHAVIOR-1K 基准测试成为弥合实验研究与实际应用之间差距的重要工具。通过专注于人们需要帮助的任务，该计划确保机器人援助既有效又符合人类需求。

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

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