构建高效仿真环境与数据生成框架，推动家居重排任务的低层操作研究
ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks
- GPU加速仿真，速度超旧方案3倍，显存占用大幅降低
- 训练了强化学习与模仿学习基线模型用于后续对比
- 规则过滤生成安全可控的机器人操作示范数据
高质量基准是具身智能研究的基础，推动长时程导航、操作和重排任务的进步。然而，随着机器人前沿任务日益复杂，亟需更快的仿真速度、更精细的测试环境和更大的示范数据集。为此，我们提出MS-HAB，一个面向家居重排中低层操作的综合性基准。首先，我们实现了家用助手基准（HAB）的GPU加速版本，支持真实低层控制，仿真速度较之前魔法抓取方案提升3倍以上，显存使用仅为其几分之一。其次，我们训练了广泛的强化学习（RL）与模仿学习（IL）基线模型，供未来研究对比。最后，我们开发了一套基于规则的轨迹过滤系统，可从RL策略中筛选出符合预设行为与安全标准的示范轨迹。结合示范过滤与快速仿真环境，实现大规模、高效、可控的数据生成。
原文摘要 · Abstract (English)
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 prior magical grasp implementations at a fraction of the 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.
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