# HuBE: Cross-Embodiment Human-like Behavior Execution for Humanoid Robots

arXiv: https://arxiv.org/abs/2508.19002 · HTML: https://mlsanity.com/paper/2508.19002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [Iterative Closed-Loop Motion Synthesis for Scaling the Capabilities of Humanoid Control](https://mlsanity.com/paper/2602.21599/) (2026)

## Abstract (arXiv)

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.