用可穿戴外骨骼将真人动作转为机器人数据，解决人形机器人训练数据少的问题。
HumanoidExo: Scalable Whole-Body Humanoid Manipulation via Wearable Exoskeleton
- 通过外骨骼捕捉人类动作，生成高质量人形机器人数据
- 仅用5次真实演示即可学会复杂全身控制，还能从仿真数据中学会走路
- 适合需要高效获取机器人训练数据的研究者和开发者
人形机器人策略学习的一大瓶颈在于缺乏大规模、多样化的数据集，因为可靠的真实世界数据收集既困难又成本高昂。为此，我们提出HumanoidExo，一种将人类运动迁移至全身人形机器人数据的新系统。该系统有效缩小了人类示范者与机器人之间的具身差距，从而缓解了全身人形机器人数据稀缺的问题。通过促进更大量、更多样化数据的采集，我们的方法显著提升了人形机器人在动态真实场景中的表现。我们在三个挑战性真实任务上评估：桌面操作、结合起坐动作的操作，以及全身操作。实验结果表明，HumanoidExo是真实机器人数据的重要补充，能使机器人策略泛化到新环境，并仅通过五次真实机器人演示就掌握复杂全身控制，甚至仅从HumanoidExo数据中学会行走新技能。
原文摘要 · Abstract (English)
A significant bottleneck in humanoid policy learning is the acquisition of large-scale, diverse datasets, as collecting reliable real-world data remains both difficult and cost-prohibitive. To address this limitation, we introduce HumanoidExo, a novel system that transfers human motion to whole-body humanoid data. HumanoidExo offers a high-efficiency solution that minimizes the embodiment gap between the human demonstrator and the robot, thereby tackling the scarcity of whole-body humanoid data. By facilitating the collection of more voluminous and diverse datasets, our approach significantly enhances the performance of humanoid robots in dynamic, real-world scenarios. We evaluated our method across three challenging real-world tasks: table-top manipulation, manipulation integrated with stand-squat motions, and whole-body manipulation. Our results empirically demonstrate that HumanoidExo is a crucial addition to real-robot data, as it enables the humanoid policy to generalize to novel environments, learn complex whole-body control from only five real-robot demonstrations, and even acquire new skills (i.e., walking) solely from HumanoidExo data.
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