打造可实操的家用机器人套件，提升全身协同操作能力。
BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities
- 基于双臂轮式机器人，整合全身遥操作采集数据
- 在5个复杂家务任务中实现稳定导航与精准操控
- 适合研究家庭服务机器人与全身控制算法的团队
现实家居任务对移动操作机器人构成重大挑战。现有基准分析表明，任务成功依赖三大核心全身控制能力：双手协作、稳定精确导航以及广泛的末端执行器可达性。实现这些能力需精心设计硬件，但系统复杂性又加剧了视觉运动策略的学习难度。为此，我们提出BEHAVIOR Robot Suite（BRS），一个面向多样化家居任务的全身操作综合框架。BRS基于具备4自由度躯干的双臂轮式机器人，集成低成本全身遥操作接口用于数据收集，并提出一种新算法以学习全身视觉运动策略。我们在五个具有挑战性的家居任务上评估BRS，这些任务不仅强调上述三项核心能力，还引入长距离导航、与铰接及可变形物体交互、狭小空间操作等额外复杂性。我们认为，BRS在机器人本体、数据采集接口与学习框架上的集成，标志着向实现日常家居任务中的真实世界全身操作迈出了重要一步。BRS已开源，地址为https://behavior-robot-suite.github.io/
原文摘要 · Abstract (English)
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/
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