AI RESEARCH
ELVIS: Ensemble-Calibrated Latent Imagination for Long-Horizon Visual MPC
arXiv CS.LG
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ArXi:2605.04709v1 Announce Type: new A central challenge of visual control with model-based reinforcement learning (RL) is reliable long-horizon planning: long rollouts with learned latent dynamics exhibit branching futures and multi-modal action-value distributions. In addition, compounding model errors amplified by visual occlusions make deep imagination brittle. We present ELVIS, a latent model predictive controller (MPC) designed to make long-horizon planning practical.