DemoHLM：从单次演示到可泛化的人形机器人移动操作
DemoHLM: From One Demonstration to Generalizable Humanoid Loco-Manipulation
AI总结：
提出 DemoHLM 框架，通过分层结构集成全身控制器与高级操作策略，仅需仿真中的单次演示即可在真实人形机器人上实现可泛化且鲁棒的移动操作。
摘要：
移动操作是人形机器人在人类环境中实现多功能交互的一项基本挑战。尽管近期研究在人形机器人全身控制方面取得了显著进展，但移动操作仍探索不足，且通常依赖硬编码的任务定义或昂贵的真实世界数据收集，这限制了自主性和泛化能力。我们提出了 DemoHLM，这是一个用于人形机器人移动操作的框架，能够在仿真中仅凭单次演示，就让真实人形机器人实现可泛化的移动操作。DemoHLM 采用了一种分层结构，将低级通用全身控制器与用于多任务的高级操作策略相集成。全身控制器将全身运动指令映射为关节力矩，并为人形机器人提供全向移动能力。操作策略通过我们的数据生成和模仿学习流水线在仿真中学习，利用闭环视觉反馈指挥全身控制器执行具有挑战性的移动操作任务。实验表明，合成数据量与策略性能之间存在正相关，突显了我们数据生成流水线的有效性以及该方法的数据效率。在配备 RGB-D 相机的 Unitree G1 机器人上进行的真实世界实验验证了 DemoHLM 的从仿真到现实的可迁移性，展现了在十项移动操作任务中面对空间变化时的鲁棒性能。
英文摘要：
Loco-manipulation is a fundamental challenge for humanoid robots to achieve versatile interactions in human environments. Although recent studies have made significant progress in humanoid whole-body control, loco-manipulation remains underexplored and often relies on hard-coded task definitions or costly real-world data collection, which limits autonomy and generalization. We present DemoHLM, a framework for humanoid loco-manipulation that enables generalizable loco-manipulation on a real humanoid robot from a single demonstration in simulation. DemoHLM adopts a hierarchy that integrates a low-level universal whole-body controller with high-level manipulation policies for multiple tasks. The whole-body controller maps whole-body motion commands to joint torques and provides omnidirectional mobility for the humanoid robot. The manipulation policies, learned in simulation via our data generation and imitation learning pipeline, command the whole-body controller with closed-loop visual feedback to execute challenging loco-manipulation tasks. Experiments show a positive correlation between the amount of synthetic data and policy performance, underscoring the effectiveness of our data generation pipeline and the data efficiency of our approach. Real-world experiments on a Unitree G1 robot equipped with an RGB-D camera validate the sim-to-real transferability of DemoHLM, demonstrating robust performance under spatial variations across ten loco-manipulation tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
