FLEX-WAM: 長期的な想像と計画のための柔軟なブロック因果的世界行動モデル
FLEX-WAM: Flexible Block-Causal World-Action Models for Long-Horizon Imagination and Planning
可変長コンテキストと無限自己回帰生成を可能にするブロック因果的世界行動モデルを提案し、行動応答性を高める訓練手法により長期計画タスクで高性能を実現した。
詳しい要約
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著者: R. Khorrambakht, Joseph Amigo, Félix Lebel, Leon Seetoo, Jean Ponce, Zhenzhen Li, Ludovic Righetti
分類: cs.RO, cs.AI
原文アブストラクト
World--action models (WAMs) promise a unified model that predicts action-conditioned futures, generates feasible actions, and supports planning in imagination. However, existing joint video--action models often use computationally heavy, fixed-horizon backbones ill-suited to streaming inference and stable long-horizon open-loop rollouts. We introduce FLEX-WAM, a Flexible and Efficient Block-Causal World--Action Model for unified simulation and policy inference. FLEX-WAM supports variable-length contexts and non-causal prediction horizons, as well as infinite autoregressive generation frame by frame or block by block. Its block-causal, KV-cacheable architecture combines axial attention and blockwise diffusion forcing to enable efficient real-time rollout and deployment-time latency--throughput tradeoffs without retraining. Joint training can nevertheless produce plausible futures that weakly respond to commanded actions. We address this failure mode by balancing state and action flow-matching gradient contributions across the state--action diffusion-noise grid and regulating world-model sampling using Forward-Dynamics (FD) elasticity, an efficient training-time proxy for action responsiveness. Across simulated and real-world datasets, FLEX-WAM achieves superior multi-step prediction quality and latency while producing stable joint state--action rollouts for thousands of steps. As a joint action proposer and simulator within MCTS, it solves long-horizon PushT and all five OGBench Puzzle-4x4 tasks entirely in imagination. On a bimanual OpenArm-based robot, a single checkpoint jointly serves as a play policy and expected-outcome predictor, enabling real-time identification and collection of model--reality mismatches for future self-improvement.
