6 papers
Being-H0.7: A Latent World-Action Model from Egocentric Videos
Hao Luo, Wanpeng Zhang, Yicheng Feng +6
Visual-Language-Action models (VLAs) have advanced generalist robot control by mapping multimodal observations and language instructions directly to actions, but sparse action supe…
Unmasking the Illusion of Embodied Reasoning in Vision-Language-Action Models
Haiweng Xu, Sipeng Zheng, Hao Luo +3
Recent Vision-Language-Action (VLA) models report impressive success rates on standard robotic benchmarks, fueling optimism about general-purpose physical intelligence. However, re…
Conservative Offline Robot Policy Learning via Posterior-Transition Reweighting
Wanpeng Zhang, Hao Luo, Sipeng Zheng +6
Offline post-training adapts a pretrained robot policy to a target dataset by supervised regression on recorded actions. In practice, robot datasets are heterogeneous: they mix emb…
Joint-Aligned Latent Action: Towards Scalable VLA Pretraining in the Wild
Hao Luo, Ye Wang, Wanpeng Zhang +5
Despite progress, Vision-Language-Action models (VLAs) are limited by a scarcity of large-scale, diverse robot data. While human manipulation videos offer a rich alternative, exist…
Rethinking Visual-Language-Action Model Scaling: Alignment, Mixture, and Regularization
Ye Wang, Sipeng Zheng, Hao Luo +9
While Vision-Language-Action (VLA) models show strong promise for generalist robot control, it remains unclear whether -- and under what conditions -- the standard "scale data" rec…
Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization
Hao Luo, Ye Wang, Wanpeng Zhang +9
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…