让人形机器人跨平台生成类人行为，兼顾自然与恰当性。
HuBE: Cross-Embodiment Human-like Behavior Execution for Humanoid Robots
- 分层闭环框架融合机器人状态与情境，生成类人动作
- 在多款商用机器人上实现更高动作相似度与效率
- 构建带情境标注数据集，支持异构机器人毫米级适配
在人形机器人运动生成中，如何同时实现动作相似性与行为恰当性仍是开放挑战，且缺乏跨平台适应能力。为此，我们提出HuBE——一种双层闭环框架，通过整合机器人状态、目标姿态与上下文情境，生成兼具相似性与恰当性的类人行为，并消除生成与执行间的结构不匹配。为支持该框架，我们构建了HPose数据集，包含细粒度情境标注；同时引入基于骨骼缩放的数据增强策略，确保异构人形机器人间达到毫米级兼容性。在多个商用平台的全面评估表明，相比现有最优方法，HuBE显著提升动作相似度、行为恰当性与计算效率，为人形机器人跨平台可迁移类人行为执行奠定坚实基础。
原文摘要 · Abstract (English)
Achieving both behavioral similarity and appropriateness in human-like motion generation for humanoid robot remains an open challenge, further compounded by the lack of cross-embodiment adaptability. To address this problem, we propose HuBE, a bi-level closed-loop framework that integrates robot state, goal poses, and contextual situations to generate human-like behaviors, ensuring both behavioral similarity and appropriateness, and eliminating structural mismatches between motion generation and execution. To support this framework, we construct HPose, a context-enriched dataset featuring fine-grained situational annotations. Furthermore, we introduce a bone scaling-based data augmentation strategy that ensures millimeter-level compatibility across heterogeneous humanoid robots. Comprehensive evaluations on multiple commercial platforms demonstrate that HuBE significantly improves motion similarity, behavioral appropriateness, and computational efficiency over state-of-the-art baselines, establishing a solid foundation for transferable and human-like behavior execution across diverse humanoid robots.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。