High-quality benchmarks are the foundation for embodied AI research, enabling significant advancements in long-horizon navigation, manipulation and rearrangement tasks. However, as frontier tasks in robotics get more advanced, they require faster simulation speed, more intricate test environments, and larger demonstration datasets. To this end, we present MS-HAB, a holistic benchmark for low-level manipulation and in-home object rearrangement. First, we provide a GPU-accelerated implementation of the Home Assistant Benchmark (HAB). We support realistic low-level control and achieve over 3x the speed of previous magical grasp implementations at similar GPU memory usage. Second, we train extensive reinforcement learning (RL) and imitation learning (IL) baselines for future work to compare against. Finally, we develop a rule-based trajectory filtering system to sample specific demonstrations from our RL policies which match predefined criteria for robot behavior and safety. Combining demonstration filtering with our fast environments enables efficient, controlled data generation at scale.

翻译：高质量基准是具身人工智能研究的基石，能够显著推动长时程导航、操作与重排任务的进展。然而，随着机器人前沿任务日趋复杂，它们对更快的仿真速度、更精细的测试环境以及更大规模的演示数据集提出了要求。为此，我们提出了MS-HAB，一个面向低层次操作与家庭物体重排的整体性基准。首先，我们提供了家庭助手基准（HAB）的GPU加速实现。我们支持逼真的低层次控制，并在相近的GPU内存使用量下，实现了超过先前魔法抓取实现3倍以上的速度。其次，我们训练了广泛的强化学习（RL）与模仿学习（IL）基线，以供未来工作进行比较。最后，我们开发了一个基于规则的轨迹过滤系统，用于从我们的RL策略中采样符合预定义的机器人行为与安全标准的特定演示。将演示过滤与我们的快速环境相结合，能够实现高效、可控的大规模数据生成。