SoGuDiff: 社会的規範に基づく調整可能な拡散モデルロボットナビゲーション
SoGuDiff: Socially Guided Diffusion for Steerable, Norm-Grounded Robot Navigation
拡散モデルを用いて、ロボットの社会的ナビゲーション行動を展開時にスタイル指定で調整可能にし、衝突回避と両立させたフレームワークを提案。
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？
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著者: Christian Schaible, Haoran Ji, Yash Vardhan Pant, Stephen L. Smith
分類: cs.RO
原文アブストラクト
Beyond collision avoidance, socially competent robot navigation requires adherence to implicit social conventions that vary across contexts, cultures, and deployment requirements. Many conventional navigation policies learn a single normative behavior, either through reinforcement learning against a fixed reward function or imitation of human demonstrations, exposing no interface for adjusting that conduct at runtime. We present a diffusion-based navigation framework whose social behavior can be tuned at deployment: a desired style is specified, such as how closely the robot passes, which side it yields to, or how much it defers to groups, and the planner adapts accordingly. Continuous style axes can be followed independently or composed, spanning a behavioral space rather than discrete, primitive-based specifications. A feasibility projection layer separates learned social behavior from kinematic feasibility and collision avoidance. A single-axis sweep illustrates a tradeoff curve that strictly dominates the evaluated fixed-behavior baseline configurations, and stylistic differences are replicated in real-world demonstrations.