ZeroWBC: 从人类自我中心数据学习自然全身人形交互
ZeroWBC: Learning Natural Whole-Body Humanoid Interaction from Human Egocentric Data
AI总结：
提出ZeroWBC框架，利用人类自我中心视频和同步全身运动数据，通过生成-跟踪方法实现无遥操作的人形机器人全身交互控制。
摘要：
由于全身遥操作数据成本高昂，实现多功能且自然的全身人形交互控制仍然具有挑战性。我们提出ZeroWBC，一种无需遥操作的框架，从人类自我中心视频以及同步的全身运动和文本注释中学习人形全身交互。ZeroWBC采用生成-跟踪公式来解决静态场景全身交互控制问题。给定初始自我中心图像和语言指令，微调的视觉-语言模型生成未来人类全身运动标记，这些标记被解码为连续运动并重定向到人形机器人。得到的参考运动，连同根部和关键身体部位轨迹，然后由通用交互运动跟踪策略执行。为了提高交互性能，我们引入了一种面向交互的跟踪奖励，该奖励优先考虑全局根部和关键身体部位轨迹对齐，同时保持自然的全身运动。在Unitree G1人形机器人上的实验表明，ZeroWBC无需机器人遥操作演示即可实现多样化的场景感知行为。这些结果表明了一种从人类自我中心数据学习自然人形全身交互的可扩展范式。
英文摘要：
Achieving versatile and natural whole-body humanoid interaction control remains challenging due to the high cost of whole-body teleoperation data. We present ZeroWBC, a teleoperation-free framework that learns humanoid whole-body interaction from human egocentric videos paired with synchronized whole-body motion and text annotations. ZeroWBC adopts a generation-then-tracking formulation to tackle the static scene whole-body interaction control problem. Given an initial egocentric image and a language instruction, a fine-tuned Vision-Language Model generates future human whole-body motion tokens, which are decoded into continuous motions and retargeted to the humanoid. The resulting reference motions, together with root and key body-part trajectories, are then executed by a general interactive motion tracking policy. To improve interaction performance, we introduce an interaction-oriented tracking reward that prioritizes global root and key body-part trajectory alignment while preserving natural whole-body motion. Experiments on the Unitree G1 humanoid robot show that ZeroWBC enables diverse scene-aware behaviors without robot teleoperation demonstrations. These results suggest a scalable paradigm for learning natural humanoid whole-body interaction from human egocentric data.
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。
