HumDex: 人形机器人灵活操控的便捷实现
HumDex: Humanoid Dexterous Manipulation Made Easy
AI总结：
本文提出HumDex系统，通过IMU运动追踪和学习导向方法，实现人形机器人灵活操控的高效数据采集与精准执行。
摘要：
本文研究了人形机器人整体灵活操控，其中高效收集高质量演示数据仍是关键瓶颈。现有遥控系统常面临可移植性差、遮挡或精度不足等问题，限制了其在复杂整体任务中的应用。为解决这些问题，我们引入HumDex，一种专为人形机器人整体灵活操控设计的便携遥控系统。我们的系统利用基于IMU的运动追踪来解决可移植性与精度之间的权衡，实现准确的全身追踪同时易于部署。对于灵活手部控制，我们进一步引入基于学习的重定向方法，生成流畅自然的手部动作，无需手动参数调整。除遥控外，HumDex还能高效收集人类运动数据。基于此能力，我们提出一个两阶段模仿学习框架，首先在多样化的人类运动数据上预训练以学习可推广的先验知识，然后在机器人数据上微调以弥合具身差距，实现精确执行。我们证明该方法显著提高了对新配置、物体和背景的泛化能力，且数据采集成本低。整个系统完全可复现且开源，网址为https://github.com/physical-superintelligence-lab/humdex。
英文摘要：
This paper investigates humanoid whole-body dexterous manipulation, where the efficient collection of high-quality demonstration data remains a central bottleneck. Existing teleoperation systems often suffer from limited portability, occlusion, or insufficient precision, which hinders their applicability to complex whole-body tasks. To address these challenges, we introduce HumDex, a portable teleoperation system designed for humanoid whole-body dexterous manipulation. Our system leverages IMU-based motion tracking to address the portability-precision trade-off, enabling accurate full-body tracking while remaining easy to deploy. For dexterous hand control, we further introduce a learning-based retargeting method that generates smooth and natural hand motions without manual parameter tuning. Beyond teleoperation, HumDex enables efficient collection of human motion data. Building on this capability, we propose a two-stage imitation learning framework that first pre-trains on diverse human motion data to learn generalizable priors, and then fine-tunes on robot data to bridge the embodiment gap for precise execution. We demonstrate that this approach significantly improves generalization to new configurations, objects, and backgrounds with minimal data acquisition costs. The entire system is fully reproducible and open-sourced at https://github.com/physical-superintelligence-lab/humdex.
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。
