Stanford’s Fei-Fei Li Team CoRL 2025 | How Far Are We from the ‘Robot Butler’? The Stanford Fei-Fei Li team has released the comprehensive household robot suite BRS platform. Introduction Making robots clean, organize, and clear tables in real home environments like humans is a scene depicted in countless science fiction works and is one of the ultimate dreams in the field of embodied intelligence. However, reality is much more challenging than imagination. Daily household chores may seem simple, but they impose extremely demanding requirements on robots: they need to navigate vast home spaces, collaborate with two hands to move objects of varying sizes, reach high shelves and pick up items from the floor, and interact dexterously with various doors, drawers, and appliances. To clarify the core capabilities required to achieve this goal, the Stanford University Fei-Fei Li team conducted an in-depth analysis of a benchmark containing 1,000 daily household tasks (BEHAVIOR-1K) and distilled three key full-body control capabilities: Bimanual Coordination: such as moving large boxes and organizing clothes. Stable and Accurate Navigation: such as navigating between rooms and avoiding obstacles. Extensive End-effector Reachability: such as picking up items from the floor and placing items on high shelves. However, achieving these three capabilities simultaneously poses significant challenges in hardware design, data collection, and algorithm learning. To systematically address these issues, the team launched the BEHAVIOR ROBOT SUITE (BRS)—a comprehensive, open-source framework for real-world full-body manipulation of robots. It is not just a new algorithm but a complete solution that integrates a robotic platform, teleoperation tools, and learning algorithms. BRS Empowered Household Activities. This image intuitively demonstrates the powerful capabilities of the BRS system. With strategies trained through BRS, the robot successfully completed five complex long-duration tasks in real home environments, including: 1) cleaning the room after a party, 2) cleaning the toilet, 3) taking out the trash, 4) placing items on the shelf, and 5) organizing clothes. The image labels the core capabilities most tested by each task with B, N, and R respectively. BRS Hardware, Teleoperation, and Algorithms. The success of BRS stems from its systematic innovations at three levels, which are interlinked and together form a complete closed loop from data collection to strategy deployment.
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