HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models
World-action (WA) models can generate long-horizon action chunks for general-purpose robotic manipulation, but they remain vulnerable to calibration, perception, and contact-dynamics errors in real-world precision tasks, often failing in the final few millimeters of alignment or insertion. We propose HALO-WA, a hybrid-attention latent-guided online reinforcement learning (RL) framework for WA models, which leverages latent features and action priors from the WA generation process through a lightweight actor-critic adapter to enable fast online adaptation to real deployment errors. HALO-WA introduces a hybrid-attention structure that preserves the temporal consistency of action chunks whil...
| Authors | Angen Ye, Weijie Ke, Xiaofeng Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 July 2026 |
| Last updated at source | 21 September 2026 |
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