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BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities

About

Real-world household tasks present significant challenges for mobile manipulation robots. An analysis of existing robotics benchmarks reveals that successful task performance hinges on three key whole-body control capabilities: bimanual coordination, stable and precise navigation, and extensive end-effector reachability. Achieving these capabilities requires careful hardware design, but the resulting system complexity further complicates visuomotor policy learning. To address these challenges, we introduce the BEHAVIOR Robot Suite (BRS), a comprehensive framework for whole-body manipulation in diverse household tasks. Built on a bimanual, wheeled robot with a 4-DoF torso, BRS integrates a cost-effective whole-body teleoperation interface for data collection and a novel algorithm for learning whole-body visuomotor policies. We evaluate BRS on five challenging household tasks that not only emphasize the three core capabilities but also introduce additional complexities, such as long-range navigation, interaction with articulated and deformable objects, and manipulation in confined spaces. We believe that BRS's integrated robotic embodiment, data collection interface, and learning framework mark a significant step toward enabling real-world whole-body manipulation for everyday household tasks. BRS is open-sourced at https://behavior-robot-suite.github.io/

Yunfan Jiang, Ruohan Zhang, Josiah Wong, Chen Wang, Yanjie Ze, Hang Yin, Cem Gokmen, Shuran Song, Jiajun Wu, Li Fei-Fei• 2025

Related benchmarks

TaskDatasetResultRank
Mobile ManipulationSetTable
Open Fridge Success Rate27
12
Robot Manipulation and NavigationRoboCasa Manipulation Navigation
PnP C2S Success Rate0.00e+0
6
Mobile ManipulationManiSkill-HAB TidyHouse
Pick All Success Rate0.8
5
Mobile ManipulationManiSkill-HAB PrepareGroceries
Pick All Success Rate0.8
5
Mobile Robot ManipulationRoboCasa Mobile Manipulation
PnP C2S Success Rate0.00e+0
5
Inference Efficiency ComparisonEfficiency Evaluation Setup
Inference Time (ms)370
3
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