We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an extensive survey on "what do you want robots to do for you?". The first is the definition of 1,000 everyday activities, grounded in 50 scenes (houses, gardens, restaurants, offices, etc.) with more than 9,000 objects annotated with rich physical and semantic properties. The second is OMNIGIBSON, a novel simulation environment that supports these activities via realistic physics simulation and rendering of rigid bodies, deformable bodies, and liquids. Our experiments indicate that the activities in BEHAVIOR-1K are long-horizon and dependent on complex manipulation skills, both of which remain a challenge for even state-of-the-art robot learning solutions. To calibrate the simulation-to-reality gap of BEHAVIOR-1K, we provide an initial study on transferring solutions learned with a mobile manipulator in a simulated apartment to its real-world counterpart. We hope that BEHAVIOR-1K's human-grounded nature, diversity, and realism make it valuable for embodied AI and robot learning research. Project website: https://behavior.stanford.edu.

翻译：本文提出BEHAVIOR-1K，一个面向以人为中心的机器人学的综合性仿真基准测试。BEHAVIOR-1K包含两个核心组件，其设计思路与动机均源于一项大规模调查“你希望机器人为你做什么？”的结果。首先，该基准定义了1000项基于50个场景（包括住宅、花园、餐厅、办公室等）的日常活动，这些场景中标注了超过9000个具备丰富物理属性与语义属性的物体。其次，我们提出了OMNIGIBSON这一新型仿真环境，通过支持刚体、可变形体及液体的真实物理仿真与渲染，为上述活动提供实现保障。实验表明，BEHAVIOR-1K中的活动具有长时域特性，且高度依赖复杂操控技能，即便是当前的先进机器人学习方案仍难以攻克此类挑战。为校准BEHAVIOR-1K的仿真-现实差距，我们开展了一项初步研究，将基于仿真公寓中移动机械臂学得的解决方案迁移至真实场景。我们期待BEHAVIOR-1K以人为中心的特性、多样性及真实感，能为具身AI与机器人学习研究提供重要价值。项目网站：https://behavior.stanford.edu。