時相論理によるアクションのみの拡散ポリシーへのガイダンス：ワールドモデルを用いた手法
Temporal Logic Guidance for Action-Only Diffusion Policies with World Models
アクションのみを生成する拡散ポリシーに対し、学習済みワールドモデルを用いてSTLロバストネスを微分可能に評価し、その勾配を拡散過程に注入することで、再学習なしに制約充足を改善する手法を提案。Robomimicのタスクで成功率100%を維持しつつ制約違反を大幅削減した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Moritz Zoellner, Anastasios Manganaris, Rohan Paleja
分類: cs.RO
原文アブストラクト
Diffusion policies enable multimodal robot behavior but offer limited ability to choose among behavior modes at inference time, even though such control is desirable in human-robot settings. Prior solutions to this lack of control have utilized Signal Temporal Logic (STL) to express human intentions and provide corresponding guidance for diffusion policy inference. However, these approaches can only guide diffusion policies that jointly generate future actions and states, increasing both complexity and runtime. We propose a novel guidance method for action-only diffusion policies that uses a separate learned world model to enable differentiable evaluation of STL robustness, with its gradient then injected into the diffusion process. This steers behavior toward constraint satisfaction without retraining, improving constraint adherence while preserving task performance. On the Can Transport task from Robomimic, our method maintains 100% task success while reducing constraint violations from over 80% for baseline methods to 4%. We also discuss extensions toward improved robustness and more complex constraints.
