DriftWorld: ドリフトによる高速ワールドモデリング
DriftWorld: Fast World Modeling through Drifting
拡散ベースのワールドモデルは推論時の反復サンプリングが遅いため、ロボットの行動計画に必要な多数のロールアウト生成がボトルネックとなる。本論文では、訓練中に行動条件付きドリフトを学習し、単一の順伝播で未来フレームを生成できるDriftWorldを提案し、拡散ベースのベースラインより平均17倍高速で、複数の操作ベンチマークで最先端の意思決定性能を達成した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Susie Lu, Haonan Chen, Weirui Ye, Yilun Du
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Predictive world models enable robots to plan by imagining the outcomes of their actions, but their value for control hinges on generating many rollouts quickly. This creates a bottleneck for diffusion-based world models: multistep sampling makes each rollout expensive, limiting large-scale action search at inference time. We introduce DriftWorld, an action-conditioned world model based on drifting generative models. Rather than denoising iteratively at inference, DriftWorld learns an action-conditioned drift during training, allowing it to generate future frames from the current observation and a candidate action sequence in a single forward pass at 30+ fps, which is 17x faster on average than diffusion based baselines. We evaluate DriftWorld on standard vision-based robotic manipulation benchmarks, including Bridge-V2, RT-1, Language Table, Push-T, and Robomimic. By producing rollouts that are both accurate and fast, DriftWorld achieves state-of-the-art decision-making performance with far less inference time than diffusion-based world model baselines. Beyond online control, DriftWorld can also serve as an offline simulator for ranking real-world robot policies, with rollout-based scores correlating with ground truth at up to 0.99. These results show that drifting models are a strong fit for robot world modeling, where fast, high-quality imagination directly supports planning and policy evaluation.
