記号計画と連続計画を同時に生成するハイブリッド拡散モデル
Hybrid Diffusion for Simultaneous Symbolic and Continuous Planning
拡散モデルで連続的なロボット軌道を生成しつつ、高レベルの記号計画も同時に生成するハイブリッド手法を提案し、長期的タスクの意思決定を改善した。
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: Sigmund Hennum Høeg, Aksel Vaaler, Chaoqi Liu, Olav Egeland, Yilun Du
分類: cs.RO, cs.AI
原文アブストラクト
Constructing robots to accomplish long-horizon tasks is a long-standing challenge within artificial intelligence. Approaches using generative methods, particularly Diffusion Models, have gained attention due to their ability to model continuous robotic trajectories for planning and control. However, we show that these models struggle with long-horizon tasks that involve complex decision-making and, in general, are prone to confusing different modes of behavior, leading to failure. To remedy this, we propose to augment continuous trajectory generation by simultaneously generating a high-level symbolic plan. We show that this requires a novel mix of discrete variable diffusion and continuous diffusion, which dramatically outperforms the baselines. In addition, we illustrate how this hybrid diffusion process enables flexible trajectory synthesis, allowing us to condition synthesized actions on partial and complete symbolic conditions.
