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This paper introduces ELVIS, a latent model predictive controller designed for long-horizon visual control, addressing challenges like branching futures and compounding model errors. ELVIS employs a Gaussian-mixture MPPI to maintain multiple hypotheses and a shared uncertainty-aware lambda-return, using an ensemble of latent critics to adaptively trade off bootstrapping versus look-ahead. ELVIS achieves state-of-the-art performance on DeepMind Control Suite visual tasks and demonstrates zero-shot transfer to a real-world sand-spraying task.
Achieve robust long-horizon visual control by adaptively balancing model-based lookahead with bootstrapping, enabling zero-shot transfer to real-world tasks with severe occlusions.
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. ELVIS plans in a Dreamer-style recurrent state space model (RSSM) and replaces standard unimodal model predictive path integral (MPPI) with a Gaussian-mixture MPPI that maintains multiple coherent hypotheses over long horizons, avoiding mode averaging under branching rollouts. In parallel, ELVIS stabilizes deep imagination with a shared uncertainty-aware lambda-return: an ensemble of latent critics defines an upper-confidence-bound (UCB) score that gates a time-varying lambda, adaptively trading off bootstrapping versus look-ahead to limit compounding error during planning. The same return is used both to train an actor-critic prior from imagined rollouts and to score candidate trajectories inside GMM-MPPI, aligning RL objectives with the planner's long-horizon optimization. On fourteen DeepMind Control Suite visual tasks, ELVIS establishes state-of-the-art performance compared with TD-MPC2 and DreamerV3. Finally, ELVIS transfers zero-shot to a real-world sand-spraying task with severe occlusions, improving surface-quality metrics and demonstrating robustness beyond simulation.
