ResWM: Residual-Action World Model for Visual RL
Learning predictive world models from raw visual observations is a central challenge in reinforcement learning (RL), especially for robotics and continuous control. Conventional model-based RL frameworks directly condition future predictions on absolute actions, which makes optimization unstable: the optimal action distributions are task-dependent, unknown a priori, and often lead to oscillatory or inefficient control. To address this, we introduce the Residual-Action World Model (ResWM), a new framework that reformulates the control variable from absolute actions to residual actions -- incremental adjustments relative to the previous step. This design aligns with the inherent smoothness ...
| Authors | Jseen Zhang, Gabriel Adineera, Jinzhou Tan et al. |
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| Primary category | cs.RO |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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