Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization

Authors: Hao Luo, Ye Wang, Wanpeng Zhang, Sipeng Zheng, Ziheng Xi, Chaoyi Xu, Haiweng Xu, Haoqi Yuan, Chi Zhang, Yiqing Wang, Yicheng Feng, Zongqing Lu · CoRL 2026

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Open Access
Paper: arXiv.org perpetual, non-exclusive license 1.0

Under arXiv's licence, everything below is written from the abstract only.

Why it matters

Vision-language-action models struggle with differences between robot bodies and with scarce robot data. The authors report state-of-the-art simulated results, 98.9% on LIBERO and 53.9% on RoboCasa, and cross-embodiment capability on five robotic platforms.

Proposed solution

Being-H0.5 treats human interaction traces as a shared basis for physical interaction, pre-trains on UniHand-2.0 (over 35,000 hours of multimodal data across 30 embodiments) through a unified action space, uses a Mixture-of-Transformers design with Mixture-of-Flow experts, and adds Manifold-Preserving Gating and Universal Async Chunking for real-world stability.

Abstract

We introduce Being-H0.5, a foundational Vision-Language-Action (VLA) model designed for robust cross-embodiment generalization across diverse robotic platforms. While existing VLAs often struggle with morphological heterogeneity and data scarcity, we propose a human-centric learning paradigm that treats human interaction traces as a universal "mother tongue" for physical interaction. To support this, we present UniHand-2.0, the largest embodied pre-training recipe to date, comprising over 35,000 hours of multimodal data across 30 distinct robotic embodiments. Our approach introduces a Unified Action Space that maps heterogeneous robot controls into semantically aligned slots, enabling low-resource robots to bootstrap skills from human data and high-resource platforms. Built upon this human-centric foundation, we design a unified sequential modeling and multi-task pre-training paradigm to bridge human demonstrations and robotic execution. Architecturally, Being-H0.5 utilizes a Mixture-of-Transformers design featuring a novel Mixture-of-Flow (MoF) framework to decouple shared motor primitives from specialized embodiment-specific experts. Finally, to make cross-embodiment policies stable in the real world, we introduce Manifold-Preserving Gating for robustness under sensory shift and Universal Async Chunking to universalize chunked control across embodiments with different latency and control profiles. We empirically demonstrate that Being-H0.5 achieves state-of-the-art results on simulated benchmarks, such as LIBERO (98.9%) and RoboCasa (53.9%), while also exhibiting strong cross-embodiment capabilities on five robotic platforms.

Open Source Robotics

Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization

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Why it matters

Proposed solution

Abstract

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