ELVIS: 長期的視覚MPCのためのアンサンブル校正潜在想像
ELVIS: Ensemble-Calibrated Latent Imagination for Long-Horizon Visual MPC
モデルベース強化学習による視覚制御の長期計画を実用的にするため、ガウス混合MPPIと不確実性を考慮したラムダリターンを用いた潜在モデル予測制御器ELVISを提案し、シミュレーションと実世界のタスクで性能を向上させた。
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著者: Yurui Du, Pinhao Song, Yutong Hu, Renaud Detry
分類: cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
