Persistent Robot World Models: Stabilizing Multi-Step Rollouts via Reinforcement Learning
Action-conditioned robot world models generate future video frames of the manipulated scene given a robot action sequence, offering a promising alternative for simulating tasks that are difficult to model with traditional physics engines. However, these models are optimized for short-term prediction and break down when deployed autoregressively: each predicted clip feeds back as context for the next, causing errors to compound and visual quality to rapidly degrade. We address this through the following contributions. First, we introduce a reinforcement learning (RL) post-training scheme that trains the world model on its own autoregressive rollouts rather than on ground-truth histories. W...
| Authors | Jai Bardhan, Patrik Drozdik, Josef Sivic et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 March 2026 |
| Last updated at source | 4 September 2026 |
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