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cs.RO2026
Latent Policy Steering: An Efficient and Flexible Framework for Cross-Embodiment Transfer
Yiqi Wang, Mrinal Verghese, Jeff Schneider
The performance of learned robot visuomotor policies depends heavily on the size and quality of their training data, yet collecting high-quality demonstrations remains costly for r…
cs.RO2026
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…
cs.RO2025
Latent Policy Steering with Embodiment-Agnostic Pretrained World Models
Yiqi Wang, Mrinal Verghese, Jeff Schneider
The performance of learned robot visuomotor policies is heavily dependent on the size and quality of the training dataset. Although large-scale robot and human datasets are increas…