空間的注意：観測感度に基づく拡散ポリシーの実行ホライズン適応
Spatial Attention: Adapting Execution Horizons for Diffusion Policies via Observation Sensitivity
拡散ポリシーで行動チャンクをサンプリングする際、固定の実行ホライズンではなく、観測に対する行動分布の感度（空間的注意）に応じてホライズンを動的に調整する手法を提案。シミュレーションと実機で成功率を向上させた。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Che-Sang Park, Junsu Ha, Jianlong Fu, Frank C. Park
分類: cs.RO
原文アブストラクト
Sampling action chunks via generative models has become a widely adopted methodology for robotic learning from demonstration. However, existing methods often struggle to balance responsiveness and computational cost because they execute each action chunk for a fixed execution horizon. In this paper, we adaptively adjust the execution horizon of sampled action chunks, balancing responsiveness and computational efficiency. We introduce Spatial Attention -- defined as the expected squared norm of the gradient of the action log-likelihood with respect to the observation -- which indicates the sensitivity of the policy's action distribution to variations in the observation. We show that, under a fixed budget of chunk samplings, the execution horizon that minimizes the cumulative likelihood drop induced by disturbances decreases as Spatial Attention increases. By forecasting future Spatial Attention values alongside the action chunk, our framework dynamically assigns shorter execution horizons to phases with high Spatial Attention, and longer horizons to phases with low Spatial Attention. Experiments on standard and perturbed tasks, in both simulation and on a real robot, show that our method significantly improves success rates over fixed-horizon baselines while maintaining the average execution horizon.
